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

Jaeseung Shin, Joon Seok Lim, Yong Min Huh, Jie Hyun Kim, Woo Jin Hyung, Jae Joon Chung, Kyunghwa Han, Sungwon Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Article number1879
JournalScientific reports
Issue number1
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This study was supported by a Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048179).

Publisher Copyright:
© 2021, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


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