A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

Abstract This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external...

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Autores principales:	Jaeseung Shin, Joon Seok Lim, Yong-Min Huh, Jie-Hyun Kim, Woo Jin Hyung, Jae-Joon Chung, Kyunghwa Han, Sungwon Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a31cc85865d48f381b17c8ea5403991
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spelling	oai:doaj.org-article:5a31cc85865d48f381b17c8ea54039912021-12-02T13:51:06ZA radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting10.1038/s41598-021-81408-z2045-2322https://doaj.org/article/5a31cc85865d48f381b17c8ea54039912021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81408-zhttps://doaj.org/toc/2045-2322Abstract This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.Jaeseung ShinJoon Seok LimYong-Min HuhJie-Hyun KimWoo Jin HyungJae-Joon ChungKyunghwa HanSungwon KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jaeseung Shin Joon Seok Lim Yong-Min Huh Jie-Hyun Kim Woo Jin Hyung Jae-Joon Chung Kyunghwa Han Sungwon Kim A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
description	Abstract This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.
format	article
author	Jaeseung Shin Joon Seok Lim Yong-Min Huh Jie-Hyun Kim Woo Jin Hyung Jae-Joon Chung Kyunghwa Han Sungwon Kim
author_facet	Jaeseung Shin Joon Seok Lim Yong-Min Huh Jie-Hyun Kim Woo Jin Hyung Jae-Joon Chung Kyunghwa Han Sungwon Kim
author_sort	Jaeseung Shin
title	A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
title_short	A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
title_full	A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
title_fullStr	A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
title_full_unstemmed	A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
title_sort	radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5a31cc85865d48f381b17c8ea5403991
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A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

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