Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Abstract

Objective: The aim of this study is to evaluate whether radiomic imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers for analyzing the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature, clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training cohort, internal and external validation cohort, the discrimination accuracies of radiomic signature for predicting PM were 0.751(95%CI, 0.703-0.799), 0.802(95%CI, 0.691-0.912), and 0.745(95%CI, 0.683-0.806), respectively. Furthermore, for training cohort, internal and external validation cohort, the discrimination accuracies of radiomic nomogram for predicting PM were 0.792(95%CI, 0.748-0.836), 0.870(95%CI, 0.795-0.946) and 0.815(95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomic signature could predict peritoneal metastasis and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

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