Table_3_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.xlsx

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.

背景 DNA错配修复（DNA mismatch repair, MMR）缺陷作为包括胃癌在内的实体肿瘤免疫治疗疗效的预测因子，已受到广泛关注。本研究旨在开发并验证一种基于计算机断层扫描（computed tomography, CT）的放射组学列线图，用于术前预测胃癌（gastric cancer, GC）的MMR缺陷状态。
方法 本回顾性分析纳入来自两家不同医院队列的225例和91例胃癌患者。队列1被随机划分为训练队列（n=176）与内部验证队列（n=76），而队列2则作为外部验证队列。基于可重复的放射组学特征，通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归分析构建放射组学特征标签。本研究采用多变量logistic回归分析，基于放射组学特征与术前临床特征构建基于放射组学的模型。最终将该预测模型以放射组学列线图的形式呈现，并在训练队列、内部验证队列及外部验证队列中进行评估。
结果 由15个稳健特征构成的放射组学特征标签，在训练队列、内部验证队列及外部验证队列中均与MMR蛋白状态呈显著关联（所有P值均<0.001）。整合了放射组学特征标签与两项临床特征（年龄及CT报告的N分期）的放射组学列线图，在训练队列中展现出良好的区分效能，AUC为0.902（95%置信区间：0.853~0.951）；在内部验证队列中AUC为0.972（95%置信区间：0.945~1.000）；在外部验证队列中AUC为0.891（95%置信区间：0.825~0.958）。
结论 基于CT的放射组学列线图在术前预测胃癌MMR蛋白状态方面表现良好。此外，该模型是一种无创工具，可用于预测MMR蛋白状态并指导新辅助治疗。