Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients

Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Pan, Bo [verfasserIn] Xu, Ying Yao, Ru Cao, Xi Zhou, Xingtong Hao, Zhixin Zhang, Yanna Wang, Changjun Shen, Songjie Luo, Yanwen Zhu, Qingli Ren, Xinyu Kong, Lingyan Zhou, Yidong Sun, Qiang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Breast cancer 70-gene signature (MammaPrint) Prognosis Nomogram Risk prediction

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Journal of translational medicine - London : BioMed Central, 2003, 21(2023), 1 vom: 09. Nov.
Übergeordnetes Werk:	volume:21 ; year:2023 ; number:1 ; day:09 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s12967-023-04523-7

Katalog-ID:	SPR053683552

Internformat


LEADER	01000naa a22002652 4500
001	SPR053683552
003	DE-627
005	20231110064709.0
007	cr uuu---uuuuu
008	231110s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s12967-023-04523-7 \|2 doi
035			\|a (DE-627)SPR053683552
035			\|a (SPR)s12967-023-04523-7-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a Pan, Bo \|e verfasserin \|4 aut
245	1	0	\|a Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2023
520			\|a Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.
650		4	\|a Breast cancer \|7 (dpeaa)DE-He213
650		4	\|a 70-gene signature (MammaPrint) \|7 (dpeaa)DE-He213
650		4	\|a Prognosis \|7 (dpeaa)DE-He213
650		4	\|a Nomogram \|7 (dpeaa)DE-He213
650		4	\|a Risk prediction \|7 (dpeaa)DE-He213
700	1		\|a Xu, Ying \|4 aut
700	1		\|a Yao, Ru \|4 aut
700	1		\|a Cao, Xi \|4 aut
700	1		\|a Zhou, Xingtong \|4 aut
700	1		\|a Hao, Zhixin \|4 aut
700	1		\|a Zhang, Yanna \|4 aut
700	1		\|a Wang, Changjun \|4 aut
700	1		\|a Shen, Songjie \|4 aut
700	1		\|a Luo, Yanwen \|4 aut
700	1		\|a Zhu, Qingli \|4 aut
700	1		\|a Ren, Xinyu \|4 aut
700	1		\|a Kong, Lingyan \|4 aut
700	1		\|a Zhou, Yidong \|4 aut
700	1		\|a Sun, Qiang \|0 (orcid)0000-0001-7547-1470 \|4 aut
773	0	8	\|i Enthalten in \|t Journal of translational medicine \|d London : BioMed Central, 2003 \|g 21(2023), 1 vom: 09. Nov. \|w (DE-627)369084136 \|w (DE-600)2118570-0 \|x 1479-5876 \|7 nnns
773	1	8	\|g volume:21 \|g year:2023 \|g number:1 \|g day:09 \|g month:11
856	4	0	\|u https://dx.doi.org/10.1186/s12967-023-04523-7 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 21 \|j 2023 \|e 1 \|b 09 \|c 11

Indexfelder

author_variant	b p bp y x yx r y ry x c xc x z xz z h zh y z yz c w cw s s ss y l yl q z qz x r xr l k lk y z yz q s qs
matchkey_str	article:14795876:2023----::oormrdcinfh7gnsgaueampitiaynqatlctgrzdiksnmdclitriaigetrsnciioah
hierarchy_sort_str	2023
publishDate	2023
allfields	10.1186/s12967-023-04523-7 doi (DE-627)SPR053683552 (SPR)s12967-023-04523-7-e DE-627 ger DE-627 rakwb eng Pan, Bo verfasserin aut Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213 Xu, Ying aut Yao, Ru aut Cao, Xi aut Zhou, Xingtong aut Hao, Zhixin aut Zhang, Yanna aut Wang, Changjun aut Shen, Songjie aut Luo, Yanwen aut Zhu, Qingli aut Ren, Xinyu aut Kong, Lingyan aut Zhou, Yidong aut Sun, Qiang (orcid)0000-0001-7547-1470 aut Enthalten in Journal of translational medicine London : BioMed Central, 2003 21(2023), 1 vom: 09. Nov. (DE-627)369084136 (DE-600)2118570-0 1479-5876 nnns volume:21 year:2023 number:1 day:09 month:11 https://dx.doi.org/10.1186/s12967-023-04523-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1 09 11
spelling	10.1186/s12967-023-04523-7 doi (DE-627)SPR053683552 (SPR)s12967-023-04523-7-e DE-627 ger DE-627 rakwb eng Pan, Bo verfasserin aut Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213 Xu, Ying aut Yao, Ru aut Cao, Xi aut Zhou, Xingtong aut Hao, Zhixin aut Zhang, Yanna aut Wang, Changjun aut Shen, Songjie aut Luo, Yanwen aut Zhu, Qingli aut Ren, Xinyu aut Kong, Lingyan aut Zhou, Yidong aut Sun, Qiang (orcid)0000-0001-7547-1470 aut Enthalten in Journal of translational medicine London : BioMed Central, 2003 21(2023), 1 vom: 09. Nov. (DE-627)369084136 (DE-600)2118570-0 1479-5876 nnns volume:21 year:2023 number:1 day:09 month:11 https://dx.doi.org/10.1186/s12967-023-04523-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1 09 11
allfields_unstemmed	10.1186/s12967-023-04523-7 doi (DE-627)SPR053683552 (SPR)s12967-023-04523-7-e DE-627 ger DE-627 rakwb eng Pan, Bo verfasserin aut Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213 Xu, Ying aut Yao, Ru aut Cao, Xi aut Zhou, Xingtong aut Hao, Zhixin aut Zhang, Yanna aut Wang, Changjun aut Shen, Songjie aut Luo, Yanwen aut Zhu, Qingli aut Ren, Xinyu aut Kong, Lingyan aut Zhou, Yidong aut Sun, Qiang (orcid)0000-0001-7547-1470 aut Enthalten in Journal of translational medicine London : BioMed Central, 2003 21(2023), 1 vom: 09. Nov. (DE-627)369084136 (DE-600)2118570-0 1479-5876 nnns volume:21 year:2023 number:1 day:09 month:11 https://dx.doi.org/10.1186/s12967-023-04523-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1 09 11
allfieldsGer	10.1186/s12967-023-04523-7 doi (DE-627)SPR053683552 (SPR)s12967-023-04523-7-e DE-627 ger DE-627 rakwb eng Pan, Bo verfasserin aut Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213 Xu, Ying aut Yao, Ru aut Cao, Xi aut Zhou, Xingtong aut Hao, Zhixin aut Zhang, Yanna aut Wang, Changjun aut Shen, Songjie aut Luo, Yanwen aut Zhu, Qingli aut Ren, Xinyu aut Kong, Lingyan aut Zhou, Yidong aut Sun, Qiang (orcid)0000-0001-7547-1470 aut Enthalten in Journal of translational medicine London : BioMed Central, 2003 21(2023), 1 vom: 09. Nov. (DE-627)369084136 (DE-600)2118570-0 1479-5876 nnns volume:21 year:2023 number:1 day:09 month:11 https://dx.doi.org/10.1186/s12967-023-04523-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1 09 11
allfieldsSound	10.1186/s12967-023-04523-7 doi (DE-627)SPR053683552 (SPR)s12967-023-04523-7-e DE-627 ger DE-627 rakwb eng Pan, Bo verfasserin aut Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213 Xu, Ying aut Yao, Ru aut Cao, Xi aut Zhou, Xingtong aut Hao, Zhixin aut Zhang, Yanna aut Wang, Changjun aut Shen, Songjie aut Luo, Yanwen aut Zhu, Qingli aut Ren, Xinyu aut Kong, Lingyan aut Zhou, Yidong aut Sun, Qiang (orcid)0000-0001-7547-1470 aut Enthalten in Journal of translational medicine London : BioMed Central, 2003 21(2023), 1 vom: 09. Nov. (DE-627)369084136 (DE-600)2118570-0 1479-5876 nnns volume:21 year:2023 number:1 day:09 month:11 https://dx.doi.org/10.1186/s12967-023-04523-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1 09 11
language	English
source	Enthalten in Journal of translational medicine 21(2023), 1 vom: 09. Nov. volume:21 year:2023 number:1 day:09 month:11
sourceStr	Enthalten in Journal of translational medicine 21(2023), 1 vom: 09. Nov. volume:21 year:2023 number:1 day:09 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Breast cancer 70-gene signature (MammaPrint) Prognosis Nomogram Risk prediction
isfreeaccess_bool	true
container_title	Journal of translational medicine
authorswithroles_txt_mv	Pan, Bo @@aut@@ Xu, Ying @@aut@@ Yao, Ru @@aut@@ Cao, Xi @@aut@@ Zhou, Xingtong @@aut@@ Hao, Zhixin @@aut@@ Zhang, Yanna @@aut@@ Wang, Changjun @@aut@@ Shen, Songjie @@aut@@ Luo, Yanwen @@aut@@ Zhu, Qingli @@aut@@ Ren, Xinyu @@aut@@ Kong, Lingyan @@aut@@ Zhou, Yidong @@aut@@ Sun, Qiang @@aut@@
publishDateDaySort_date	2023-11-09T00:00:00Z
hierarchy_top_id	369084136
id	SPR053683552
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053683552</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231110064709.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231110s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12967-023-04523-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053683552</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12967-023-04523-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Pan, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Breast cancer</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">70-gene signature (MammaPrint)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prognosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nomogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Ying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Ru</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Xi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Xingtong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hao, Zhixin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Yanna</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Changjun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Songjie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Yanwen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Qingli</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ren, Xinyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kong, Lingyan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Yidong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Qiang</subfield><subfield code="0">(orcid)0000-0001-7547-1470</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of translational medicine</subfield><subfield code="d">London : BioMed Central, 2003</subfield><subfield code="g">21(2023), 1 vom: 09. Nov.</subfield><subfield code="w">(DE-627)369084136</subfield><subfield code="w">(DE-600)2118570-0</subfield><subfield code="x">1479-5876</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:09</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12967-023-04523-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">09</subfield><subfield code="c">11</subfield></datafield></record></collection>
author	Pan, Bo
spellingShingle	Pan, Bo misc Breast cancer misc 70-gene signature (MammaPrint) misc Prognosis misc Nomogram misc Risk prediction Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
authorStr	Pan, Bo
ppnlink_with_tag_str_mv	@@773@@(DE-627)369084136
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1479-5876
topic_title	Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients Breast cancer (dpeaa)DE-He213 70-gene signature (MammaPrint) (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Risk prediction (dpeaa)DE-He213
topic	misc Breast cancer misc 70-gene signature (MammaPrint) misc Prognosis misc Nomogram misc Risk prediction
topic_unstemmed	misc Breast cancer misc 70-gene signature (MammaPrint) misc Prognosis misc Nomogram misc Risk prediction
topic_browse	misc Breast cancer misc 70-gene signature (MammaPrint) misc Prognosis misc Nomogram misc Risk prediction
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of translational medicine
hierarchy_parent_id	369084136
hierarchy_top_title	Journal of translational medicine
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)369084136 (DE-600)2118570-0
title	Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
ctrlnum	(DE-627)SPR053683552 (SPR)s12967-023-04523-7-e
title_full	Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
author_sort	Pan, Bo
journal	Journal of translational medicine
journalStr	Journal of translational medicine
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Pan, Bo Xu, Ying Yao, Ru Cao, Xi Zhou, Xingtong Hao, Zhixin Zhang, Yanna Wang, Changjun Shen, Songjie Luo, Yanwen Zhu, Qingli Ren, Xinyu Kong, Lingyan Zhou, Yidong Sun, Qiang
container_volume	21
format_se	Elektronische Aufsätze
author-letter	Pan, Bo
doi_str_mv	10.1186/s12967-023-04523-7
normlink	(ORCID)0000-0001-7547-1470
normlink_prefix_str_mv	(orcid)0000-0001-7547-1470
title_sort	nomogram prediction of the 70-gene signature (mammaprint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among chinese breast cancer patients
title_auth	Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
abstract	Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. © The Author(s) 2023
abstractGer	Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. © The Author(s) 2023
abstract_unstemmed	Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing. © The Author(s) 2023
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
url	https://dx.doi.org/10.1186/s12967-023-04523-7
remote_bool	true
author2	Xu, Ying Yao, Ru Cao, Xi Zhou, Xingtong Hao, Zhixin Zhang, Yanna Wang, Changjun Shen, Songjie Luo, Yanwen Zhu, Qingli Ren, Xinyu Kong, Lingyan Zhou, Yidong Sun, Qiang
author2Str	Xu, Ying Yao, Ru Cao, Xi Zhou, Xingtong Hao, Zhixin Zhang, Yanna Wang, Changjun Shen, Songjie Luo, Yanwen Zhu, Qingli Ren, Xinyu Kong, Lingyan Zhou, Yidong Sun, Qiang
ppnlink	369084136
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s12967-023-04523-7
up_date	2024-07-03T21:17:33.161Z
_version_	1803594186791845888
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053683552</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231110064709.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231110s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12967-023-04523-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053683552</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12967-023-04523-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Pan, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%. Conclusions To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Breast cancer</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">70-gene signature (MammaPrint)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prognosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nomogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Ying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Ru</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Xi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Xingtong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hao, Zhixin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Yanna</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Changjun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Songjie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Yanwen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Qingli</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ren, Xinyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kong, Lingyan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Yidong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Qiang</subfield><subfield code="0">(orcid)0000-0001-7547-1470</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of translational medicine</subfield><subfield code="d">London : BioMed Central, 2003</subfield><subfield code="g">21(2023), 1 vom: 09. Nov.</subfield><subfield code="w">(DE-627)369084136</subfield><subfield code="w">(DE-600)2118570-0</subfield><subfield code="x">1479-5876</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:09</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12967-023-04523-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">09</subfield><subfield code="c">11</subfield></datafield></record></collection>
score	7.402276

Nicht das Richtige dabei?

Schreiben Sie uns!

Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?