CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer - Zeng, Qingwen; Zhu, Yanyan; Li, Leyan; Feng, Zongfeng; Shu, Xufeng; Wu, Ahao; Luo, Lianghua; Cao, Yi; Tu, Yi; Xiong, Jianbo; Zhou, Fuqing; Li, Zhengrong (Frontiers in Oncology, 2022)
Background: DNA 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 radiomics nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC). Methods: In 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 radiomics features, a radiomics 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 radiomics features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomics nomogram, which was evaluated in the training, internal validation and external validation cohorts. Results: The radiomics 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 radiomics nomogram incorporating a radiomics signature and 2 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 external validation cohort with an AUC of 0.891 (95% CI: 0.825-0.958) Conclusion: The CT-based radiomics 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.