Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer

BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/II...
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Gespeichert in:

Autor*in:	Junmeng Li [verfasserIn] Chao Zhang [verfasserIn] Jia Wei [verfasserIn] Peiming Zheng [verfasserIn] Hui Zhang [verfasserIn] Yi Xie [verfasserIn] Junwei Bai [verfasserIn] Zhonglin Zhu [verfasserIn] Kangneng Zhou [verfasserIn] Xiaokun Liang [verfasserIn] Yaoqin Xie [verfasserIn] Tao Qin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	gastric cancer radiomics signature computed tomography prognosis support vector machine

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 10(2020)
Übergeordnetes Werk:	volume:10 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2020.552270

Katalog-ID:	DOAJ007215134

Internformat


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520			\|a BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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700	0		\|a Hui Zhang \|e verfasserin \|4 aut
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allfields	10.3389/fonc.2020.552270 doi (DE-627)DOAJ007215134 (DE-599)DOAJ3a9ed4fe45a741d58a6d13f9c22d6a5a DE-627 ger DE-627 rakwb eng RC254-282 Junmeng Li verfasserin aut Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy. gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Chao Zhang verfasserin aut Jia Wei verfasserin aut Peiming Zheng verfasserin aut Hui Zhang verfasserin aut Yi Xie verfasserin aut Junwei Bai verfasserin aut Zhonglin Zhu verfasserin aut Kangneng Zhou verfasserin aut Xiaokun Liang verfasserin aut Xiaokun Liang verfasserin aut Yaoqin Xie verfasserin aut Yaoqin Xie verfasserin aut Tao Qin verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.552270 kostenfrei https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 10 2020
spelling	10.3389/fonc.2020.552270 doi (DE-627)DOAJ007215134 (DE-599)DOAJ3a9ed4fe45a741d58a6d13f9c22d6a5a DE-627 ger DE-627 rakwb eng RC254-282 Junmeng Li verfasserin aut Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy. gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Chao Zhang verfasserin aut Jia Wei verfasserin aut Peiming Zheng verfasserin aut Hui Zhang verfasserin aut Yi Xie verfasserin aut Junwei Bai verfasserin aut Zhonglin Zhu verfasserin aut Kangneng Zhou verfasserin aut Xiaokun Liang verfasserin aut Xiaokun Liang verfasserin aut Yaoqin Xie verfasserin aut Yaoqin Xie verfasserin aut Tao Qin verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.552270 kostenfrei https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 10 2020
allfields_unstemmed	10.3389/fonc.2020.552270 doi (DE-627)DOAJ007215134 (DE-599)DOAJ3a9ed4fe45a741d58a6d13f9c22d6a5a DE-627 ger DE-627 rakwb eng RC254-282 Junmeng Li verfasserin aut Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy. gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Chao Zhang verfasserin aut Jia Wei verfasserin aut Peiming Zheng verfasserin aut Hui Zhang verfasserin aut Yi Xie verfasserin aut Junwei Bai verfasserin aut Zhonglin Zhu verfasserin aut Kangneng Zhou verfasserin aut Xiaokun Liang verfasserin aut Xiaokun Liang verfasserin aut Yaoqin Xie verfasserin aut Yaoqin Xie verfasserin aut Tao Qin verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.552270 kostenfrei https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 10 2020
allfieldsGer	10.3389/fonc.2020.552270 doi (DE-627)DOAJ007215134 (DE-599)DOAJ3a9ed4fe45a741d58a6d13f9c22d6a5a DE-627 ger DE-627 rakwb eng RC254-282 Junmeng Li verfasserin aut Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy. gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Chao Zhang verfasserin aut Jia Wei verfasserin aut Peiming Zheng verfasserin aut Hui Zhang verfasserin aut Yi Xie verfasserin aut Junwei Bai verfasserin aut Zhonglin Zhu verfasserin aut Kangneng Zhou verfasserin aut Xiaokun Liang verfasserin aut Xiaokun Liang verfasserin aut Yaoqin Xie verfasserin aut Yaoqin Xie verfasserin aut Tao Qin verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.552270 kostenfrei https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 10 2020
allfieldsSound	10.3389/fonc.2020.552270 doi (DE-627)DOAJ007215134 (DE-599)DOAJ3a9ed4fe45a741d58a6d13f9c22d6a5a DE-627 ger DE-627 rakwb eng RC254-282 Junmeng Li verfasserin aut Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy. gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Chao Zhang verfasserin aut Jia Wei verfasserin aut Peiming Zheng verfasserin aut Hui Zhang verfasserin aut Yi Xie verfasserin aut Junwei Bai verfasserin aut Zhonglin Zhu verfasserin aut Kangneng Zhou verfasserin aut Xiaokun Liang verfasserin aut Xiaokun Liang verfasserin aut Yaoqin Xie verfasserin aut Yaoqin Xie verfasserin aut Tao Qin verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.552270 kostenfrei https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 10 2020
language	English
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topic_facet	gastric cancer radiomics signature computed tomography prognosis support vector machine Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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authorswithroles_txt_mv	Junmeng Li @@aut@@ Chao Zhang @@aut@@ Jia Wei @@aut@@ Peiming Zheng @@aut@@ Hui Zhang @@aut@@ Yi Xie @@aut@@ Junwei Bai @@aut@@ Zhonglin Zhu @@aut@@ Kangneng Zhou @@aut@@ Xiaokun Liang @@aut@@ Yaoqin Xie @@aut@@ Tao Qin @@aut@@
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callnumber-first	R - Medicine
author	Junmeng Li
spellingShingle	Junmeng Li misc RC254-282 misc gastric cancer misc radiomics signature misc computed tomography misc prognosis misc support vector machine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
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topic_title	RC254-282 Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer gastric cancer radiomics signature computed tomography prognosis support vector machine
topic	misc RC254-282 misc gastric cancer misc radiomics signature misc computed tomography misc prognosis misc support vector machine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc gastric cancer misc radiomics signature misc computed tomography misc prognosis misc support vector machine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc gastric cancer misc radiomics signature misc computed tomography misc prognosis misc support vector machine misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
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title_full	Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
author_sort	Junmeng Li
journal	Frontiers in Oncology
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author_browse	Junmeng Li Chao Zhang Jia Wei Peiming Zheng Hui Zhang Yi Xie Junwei Bai Zhonglin Zhu Kangneng Zhou Xiaokun Liang Yaoqin Xie Tao Qin
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title_sort	intratumoral and peritumoral radiomics of contrast-enhanced ct for prediction of disease-free survival and chemotherapy response in stage ii/iii gastric cancer
callnumber	RC254-282
title_auth	Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
abstract	BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.
abstractGer	BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.
abstract_unstemmed	BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.
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title_short	Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
url	https://doi.org/10.3389/fonc.2020.552270 https://doaj.org/article/3a9ed4fe45a741d58a6d13f9c22d6a5a https://www.frontiersin.org/articles/10.3389/fonc.2020.552270/full https://doaj.org/toc/2234-943X
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Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P&lt;0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gastric cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics signature</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">computed tomography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">support vector machine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. Including cancer and carcinogens</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chao Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jia Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peiming Zheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hui Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yi Xie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junwei Bai</subfield><subfield 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Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer

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