Frontiers in Oncology (Sep 2022)

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer

  • Qingwen Zeng,
  • Qingwen Zeng,
  • Yanyan Zhu,
  • Leyan Li,
  • Zongfeng Feng,
  • Zongfeng Feng,
  • Xufeng Shu,
  • Ahao Wu,
  • Lianghua Luo,
  • Yi Cao,
  • Yi Tu,
  • Jianbo Xiong,
  • Fuqing Zhou,
  • Zhengrong Li,
  • Zhengrong Li

DOI
https://doi.org/10.3389/fonc.2022.883109
Journal volume & issue
Vol. 12

Abstract

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BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values <0.001). A radiomic nomogram incorporating a radiomic signature and two clinical characteristics (age and CT-reported N stage) represented good discrimination in the training cohort with an AUC of 0.902 (95% CI: 0.853–0.951), in the internal validation cohort with an AUC of 0.972 (95% CI: 0.945–1.000) and in the external validation cohort with an AUC of 0.891 (95% CI: 0.825–0.958).ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.

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