Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study

Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT f...
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Autor*in:	Chen-Yi Xie [verfasserIn] Yi-Huai Hu [verfasserIn] Joshua Wing-Kei Ho [verfasserIn] Lu-Jun Han [verfasserIn] Hong Yang [verfasserIn] Jing Wen [verfasserIn] Ka-On Lam [verfasserIn] Ian Yu-Hong Wong [verfasserIn] Simon Ying-Kit Law [verfasserIn] Keith Wan-Hang Chiu [verfasserIn] Jian-Hua Fu [verfasserIn] Varut Vardhanabhuti [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 13(2021), 9, p 2145
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:9, p 2145

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers13092145

Katalog-ID:	DOAJ055328644

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245	1	0	\|a Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
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520			\|a Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
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650		4	\|a neoadjuvant chemoradiotherapy
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.3390/cancers13092145 doi (DE-627)DOAJ055328644 (DE-599)DOAJ68854aadbb1f4e7dbeae4d6e6608be6c DE-627 ger DE-627 rakwb eng RC254-282 Chen-Yi Xie verfasserin aut Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival. esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yi-Huai Hu verfasserin aut Joshua Wing-Kei Ho verfasserin aut Lu-Jun Han verfasserin aut Hong Yang verfasserin aut Jing Wen verfasserin aut Ka-On Lam verfasserin aut Ian Yu-Hong Wong verfasserin aut Simon Ying-Kit Law verfasserin aut Keith Wan-Hang Chiu verfasserin aut Jian-Hua Fu verfasserin aut Varut Vardhanabhuti verfasserin aut In Cancers MDPI AG, 2010 13(2021), 9, p 2145 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:9, p 2145 https://doi.org/10.3390/cancers13092145 kostenfrei https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c kostenfrei https://www.mdpi.com/2072-6694/13/9/2145 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2021 9, p 2145
spelling	10.3390/cancers13092145 doi (DE-627)DOAJ055328644 (DE-599)DOAJ68854aadbb1f4e7dbeae4d6e6608be6c DE-627 ger DE-627 rakwb eng RC254-282 Chen-Yi Xie verfasserin aut Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival. esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yi-Huai Hu verfasserin aut Joshua Wing-Kei Ho verfasserin aut Lu-Jun Han verfasserin aut Hong Yang verfasserin aut Jing Wen verfasserin aut Ka-On Lam verfasserin aut Ian Yu-Hong Wong verfasserin aut Simon Ying-Kit Law verfasserin aut Keith Wan-Hang Chiu verfasserin aut Jian-Hua Fu verfasserin aut Varut Vardhanabhuti verfasserin aut In Cancers MDPI AG, 2010 13(2021), 9, p 2145 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:9, p 2145 https://doi.org/10.3390/cancers13092145 kostenfrei https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c kostenfrei https://www.mdpi.com/2072-6694/13/9/2145 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2021 9, p 2145
allfields_unstemmed	10.3390/cancers13092145 doi (DE-627)DOAJ055328644 (DE-599)DOAJ68854aadbb1f4e7dbeae4d6e6608be6c DE-627 ger DE-627 rakwb eng RC254-282 Chen-Yi Xie verfasserin aut Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival. esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yi-Huai Hu verfasserin aut Joshua Wing-Kei Ho verfasserin aut Lu-Jun Han verfasserin aut Hong Yang verfasserin aut Jing Wen verfasserin aut Ka-On Lam verfasserin aut Ian Yu-Hong Wong verfasserin aut Simon Ying-Kit Law verfasserin aut Keith Wan-Hang Chiu verfasserin aut Jian-Hua Fu verfasserin aut Varut Vardhanabhuti verfasserin aut In Cancers MDPI AG, 2010 13(2021), 9, p 2145 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:9, p 2145 https://doi.org/10.3390/cancers13092145 kostenfrei https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c kostenfrei https://www.mdpi.com/2072-6694/13/9/2145 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2021 9, p 2145
allfieldsGer	10.3390/cancers13092145 doi (DE-627)DOAJ055328644 (DE-599)DOAJ68854aadbb1f4e7dbeae4d6e6608be6c DE-627 ger DE-627 rakwb eng RC254-282 Chen-Yi Xie verfasserin aut Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival. esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yi-Huai Hu verfasserin aut Joshua Wing-Kei Ho verfasserin aut Lu-Jun Han verfasserin aut Hong Yang verfasserin aut Jing Wen verfasserin aut Ka-On Lam verfasserin aut Ian Yu-Hong Wong verfasserin aut Simon Ying-Kit Law verfasserin aut Keith Wan-Hang Chiu verfasserin aut Jian-Hua Fu verfasserin aut Varut Vardhanabhuti verfasserin aut In Cancers MDPI AG, 2010 13(2021), 9, p 2145 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:9, p 2145 https://doi.org/10.3390/cancers13092145 kostenfrei https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c kostenfrei https://www.mdpi.com/2072-6694/13/9/2145 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2021 9, p 2145
allfieldsSound	10.3390/cancers13092145 doi (DE-627)DOAJ055328644 (DE-599)DOAJ68854aadbb1f4e7dbeae4d6e6608be6c DE-627 ger DE-627 rakwb eng RC254-282 Chen-Yi Xie verfasserin aut Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival. esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yi-Huai Hu verfasserin aut Joshua Wing-Kei Ho verfasserin aut Lu-Jun Han verfasserin aut Hong Yang verfasserin aut Jing Wen verfasserin aut Ka-On Lam verfasserin aut Ian Yu-Hong Wong verfasserin aut Simon Ying-Kit Law verfasserin aut Keith Wan-Hang Chiu verfasserin aut Jian-Hua Fu verfasserin aut Varut Vardhanabhuti verfasserin aut In Cancers MDPI AG, 2010 13(2021), 9, p 2145 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:9, p 2145 https://doi.org/10.3390/cancers13092145 kostenfrei https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c kostenfrei https://www.mdpi.com/2072-6694/13/9/2145 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2021 9, p 2145
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topic_facet	esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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container_title	Cancers
authorswithroles_txt_mv	Chen-Yi Xie @@aut@@ Yi-Huai Hu @@aut@@ Joshua Wing-Kei Ho @@aut@@ Lu-Jun Han @@aut@@ Hong Yang @@aut@@ Jing Wen @@aut@@ Ka-On Lam @@aut@@ Ian Yu-Hong Wong @@aut@@ Simon Ying-Kit Law @@aut@@ Keith Wan-Hang Chiu @@aut@@ Jian-Hua Fu @@aut@@ Varut Vardhanabhuti @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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language_de	englisch
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callnumber-first	R - Medicine
author	Chen-Yi Xie
spellingShingle	Chen-Yi Xie misc RC254-282 misc esophageal squamous cell carcinoma misc neoadjuvant chemoradiotherapy misc prognosis misc radiogenomic misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
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topic_title	RC254-282 Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy prognosis radiogenomic
topic	misc RC254-282 misc esophageal squamous cell carcinoma misc neoadjuvant chemoradiotherapy misc prognosis misc radiogenomic misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc esophageal squamous cell carcinoma misc neoadjuvant chemoradiotherapy misc prognosis misc radiogenomic misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc esophageal squamous cell carcinoma misc neoadjuvant chemoradiotherapy misc prognosis misc radiogenomic misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
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title_full	Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
author_sort	Chen-Yi Xie
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author_browse	Chen-Yi Xie Yi-Huai Hu Joshua Wing-Kei Ho Lu-Jun Han Hong Yang Jing Wen Ka-On Lam Ian Yu-Hong Wong Simon Ying-Kit Law Keith Wan-Hang Chiu Jian-Hua Fu Varut Vardhanabhuti
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title_sort	using genomics feature selection method in radiomics pipeline improves prognostication performance in locally advanced esophageal squamous cell carcinoma—a pilot study
callnumber	RC254-282
title_auth	Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
abstract	Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
abstractGer	Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
abstract_unstemmed	Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (<i<p</i< < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
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title_short	Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
url	https://doi.org/10.3390/cancers13092145 https://doaj.org/article/68854aadbb1f4e7dbeae4d6e6608be6c https://www.mdpi.com/2072-6694/13/9/2145 https://doaj.org/toc/2072-6694
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Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study

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