Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gao, Yuanjing [verfasserIn] Luo, Yanwen [verfasserIn] Zhao, Chenyang [verfasserIn] Xiao, Mengsu [verfasserIn] Ma, Li [verfasserIn] Li, Wenbo [verfasserIn] Qin, Jing [verfasserIn] Zhu, Qingli [verfasserIn] Jiang, Yuxin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Nomogram Breast cancer Ultrasound (US) Lymphatic metastasis

Übergeordnetes Werk:	Enthalten in: European radiology - Berlin : Springer, 1991, 31(2020), 2 vom: 26. Aug., Seite 928-937
Übergeordnetes Werk:	volume:31 ; year:2020 ; number:2 ; day:26 ; month:08 ; pages:928-937

Links:	Volltext

DOI / URN:	10.1007/s00330-020-07181-1

Katalog-ID:	SPR042730880

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245	1	0	\|a Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
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520			\|a Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.
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700	1		\|a Jiang, Yuxin \|e verfasserin \|4 aut
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912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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Indexfelder

author_variant	y g yg y l yl c z cz m x mx l m lm w l wl j q jq q z qz y j yj
matchkey_str	article:14321084:2020----::oormaeordoisnlssfrmrbescneutaoniaepeitooail
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publishDate	2020
allfields	10.1007/s00330-020-07181-1 doi (DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Gao, Yuanjing verfasserin aut Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer. Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213 Luo, Yanwen verfasserin aut Zhao, Chenyang verfasserin aut Xiao, Mengsu verfasserin aut Ma, Li verfasserin aut Li, Wenbo verfasserin aut Qin, Jing verfasserin aut Zhu, Qingli verfasserin aut Jiang, Yuxin verfasserin aut Enthalten in European radiology Berlin : Springer, 1991 31(2020), 2 vom: 26. Aug., Seite 928-937 (DE-627)268757526 (DE-600)1472718-3 1432-1084 nnns volume:31 year:2020 number:2 day:26 month:08 pages:928-937 https://dx.doi.org/10.1007/s00330-020-07181-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 ASE AR 31 2020 2 26 08 928-937
spelling	10.1007/s00330-020-07181-1 doi (DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Gao, Yuanjing verfasserin aut Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer. Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213 Luo, Yanwen verfasserin aut Zhao, Chenyang verfasserin aut Xiao, Mengsu verfasserin aut Ma, Li verfasserin aut Li, Wenbo verfasserin aut Qin, Jing verfasserin aut Zhu, Qingli verfasserin aut Jiang, Yuxin verfasserin aut Enthalten in European radiology Berlin : Springer, 1991 31(2020), 2 vom: 26. Aug., Seite 928-937 (DE-627)268757526 (DE-600)1472718-3 1432-1084 nnns volume:31 year:2020 number:2 day:26 month:08 pages:928-937 https://dx.doi.org/10.1007/s00330-020-07181-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 ASE AR 31 2020 2 26 08 928-937
allfields_unstemmed	10.1007/s00330-020-07181-1 doi (DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Gao, Yuanjing verfasserin aut Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer. Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213 Luo, Yanwen verfasserin aut Zhao, Chenyang verfasserin aut Xiao, Mengsu verfasserin aut Ma, Li verfasserin aut Li, Wenbo verfasserin aut Qin, Jing verfasserin aut Zhu, Qingli verfasserin aut Jiang, Yuxin verfasserin aut Enthalten in European radiology Berlin : Springer, 1991 31(2020), 2 vom: 26. Aug., Seite 928-937 (DE-627)268757526 (DE-600)1472718-3 1432-1084 nnns volume:31 year:2020 number:2 day:26 month:08 pages:928-937 https://dx.doi.org/10.1007/s00330-020-07181-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 ASE AR 31 2020 2 26 08 928-937
allfieldsGer	10.1007/s00330-020-07181-1 doi (DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Gao, Yuanjing verfasserin aut Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer. Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213 Luo, Yanwen verfasserin aut Zhao, Chenyang verfasserin aut Xiao, Mengsu verfasserin aut Ma, Li verfasserin aut Li, Wenbo verfasserin aut Qin, Jing verfasserin aut Zhu, Qingli verfasserin aut Jiang, Yuxin verfasserin aut Enthalten in European radiology Berlin : Springer, 1991 31(2020), 2 vom: 26. Aug., Seite 928-937 (DE-627)268757526 (DE-600)1472718-3 1432-1084 nnns volume:31 year:2020 number:2 day:26 month:08 pages:928-937 https://dx.doi.org/10.1007/s00330-020-07181-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 ASE AR 31 2020 2 26 08 928-937
allfieldsSound	10.1007/s00330-020-07181-1 doi (DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.64 bkl Gao, Yuanjing verfasserin aut Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer. Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213 Luo, Yanwen verfasserin aut Zhao, Chenyang verfasserin aut Xiao, Mengsu verfasserin aut Ma, Li verfasserin aut Li, Wenbo verfasserin aut Qin, Jing verfasserin aut Zhu, Qingli verfasserin aut Jiang, Yuxin verfasserin aut Enthalten in European radiology Berlin : Springer, 1991 31(2020), 2 vom: 26. Aug., Seite 928-937 (DE-627)268757526 (DE-600)1472718-3 1432-1084 nnns volume:31 year:2020 number:2 day:26 month:08 pages:928-937 https://dx.doi.org/10.1007/s00330-020-07181-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 ASE AR 31 2020 2 26 08 928-937
language	English
source	Enthalten in European radiology 31(2020), 2 vom: 26. Aug., Seite 928-937 volume:31 year:2020 number:2 day:26 month:08 pages:928-937
sourceStr	Enthalten in European radiology 31(2020), 2 vom: 26. Aug., Seite 928-937 volume:31 year:2020 number:2 day:26 month:08 pages:928-937
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nomogram Breast cancer Ultrasound (US) Lymphatic metastasis
dewey-raw	610
isfreeaccess_bool	false
container_title	European radiology
authorswithroles_txt_mv	Gao, Yuanjing @@aut@@ Luo, Yanwen @@aut@@ Zhao, Chenyang @@aut@@ Xiao, Mengsu @@aut@@ Ma, Li @@aut@@ Li, Wenbo @@aut@@ Qin, Jing @@aut@@ Zhu, Qingli @@aut@@ Jiang, Yuxin @@aut@@
publishDateDaySort_date	2020-08-26T00:00:00Z
hierarchy_top_id	268757526
dewey-sort	3610
id	SPR042730880
language_de	englisch
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Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. 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author	Gao, Yuanjing
spellingShingle	Gao, Yuanjing ddc 610 bkl 44.64 misc Nomogram misc Breast cancer misc Ultrasound (US) misc Lymphatic metastasis Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
authorStr	Gao, Yuanjing
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topic_title	610 ASE 44.64 bkl Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients Nomogram (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Ultrasound (US) (dpeaa)DE-He213 Lymphatic metastasis (dpeaa)DE-He213
topic	ddc 610 bkl 44.64 misc Nomogram misc Breast cancer misc Ultrasound (US) misc Lymphatic metastasis
topic_unstemmed	ddc 610 bkl 44.64 misc Nomogram misc Breast cancer misc Ultrasound (US) misc Lymphatic metastasis
topic_browse	ddc 610 bkl 44.64 misc Nomogram misc Breast cancer misc Ultrasound (US) misc Lymphatic metastasis
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title	Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
ctrlnum	(DE-627)SPR042730880 (DE-599)SPRs00330-020-07181-1-e (SPR)s00330-020-07181-1-e
title_full	Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
author_sort	Gao, Yuanjing
journal	European radiology
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author_browse	Gao, Yuanjing Luo, Yanwen Zhao, Chenyang Xiao, Mengsu Ma, Li Li, Wenbo Qin, Jing Zhu, Qingli Jiang, Yuxin
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title_sort	nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
title_auth	Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
abstract	Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.
abstractGer	Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.
abstract_unstemmed	Objectives To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. Key Points • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.
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container_issue	2
title_short	Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients
url	https://dx.doi.org/10.1007/s00330-020-07181-1
remote_bool	true
author2	Luo, Yanwen Zhao, Chenyang Xiao, Mengsu Ma, Li Li, Wenbo Qin, Jing Zhu, Qingli Jiang, Yuxin
author2Str	Luo, Yanwen Zhao, Chenyang Xiao, Mengsu Ma, Li Li, Wenbo Qin, Jing Zhu, Qingli Jiang, Yuxin
ppnlink	268757526
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00330-020-07181-1
up_date	2024-07-03T14:31:21.347Z
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Methods Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. Results In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. 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Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

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