AUTHOR=Tong Yahan , Li Jiaying , Chen Jieyu , Hu Can , Xu Zhiyuan , Duan Shaofeng , Wang Xiaojie , Yu Risheng , Cheng Xiangdong 

TITLE=A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.865548

DOI=10.3389/fonc.2022.865548

ISSN=2234-943X

ABSTRACT=Purpose To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma.
Materials and Methods From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n=101; center 2 as the external testing sets, n=60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination. Radiomics features were extracted from portal-venous phase CT images. Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to select features, and then radiomics signature was constructed using logistic regression analysis. A radiomics nomogram was built incorporating the radiomics signature and independent clinical predictors. The model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). 
Results The radiomics signature, which was constructed using two selected features, was significantly associated with dMMR gastric adenocarcinoma in the training and internal tesing sets (P<0.05). The radiomics signature model showed a moderate discrimination ability with an area under the ROC curve (AUC) of 0.81 in the training sets, which was confirmed with an AUC of 0.78 in the internal testing sets. The radiomics nomogram consisted of the radiomics signature and clinical factors (age, sex and location) and showed excellent discrimination in the training, internal testing and external testing sets with AUCs of 0.93, 0.82 and 0.83, respectively. Further, calibration curves and DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.
Conclusions The radiomics nomogram combining radiomics signature and clinical characteristics (age, sex and location) may be used to individually predict dMMR gastric adenocarcinoma, which may aid clinical treatment strategy.