Association Analysis Between Intratumoral and Peritumoral MRI Radiomics Features and Overall Survival of Neoadjuvant Therapy in Rectal Cancer.

Abstract

The use of peritumoral features to determine the survival time of patients with rectal cancer (RC) is still imprecise. To explore the correlation between intratumoral, peritumoral and combined features, and overall survival (OS). Retrospective. One hundred sixty-six RC patients (53 women, 113 men; average age: 55 ± 12 years) who underwent radical resection after neoadjuvant therapy. 3 T; T2WI sagittal, T1WI axial, T2WI axial with fat suppression, and high-resolution T2WI axial sequences, enhanced T1WI axial and sagittal sequences with fat suppression. Radiologist A segmented 166 patients, and radiologist B randomly segmented 30 patients. Intratumoral and peritumoral features were extracted, and features with good stability (ICC ≥0.75) were retained through intra-observer analysis. Seven classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Extremely randomized trees (ET), eXtreme Gradient Boosting (XGBoost), and LightGBM (LGBM), were applied to select the classifier with the best performance. Next, the Rad-score of best classifier and the clinical features were selected to establish the models, thus, nomogram was built to identify the association with 1-, 3-, and 5-year OS. LASSO, regression analysis, ROC, DeLong method, Kaplan-Meier curve. P < 0.05 indicated a significant difference. Only Node (irregular tumor nodules in the surrounding mesentery) and ExtraMRF (lymph nodes outside the perirectal mesentery) were significantly different in 20 clinical features. Twelve intratumoral, 3 peritumoral, and 14 combined features related to OS were selected. LR, SVM, and RF classier showed the best efficacy in the intratumoral, peritumoral, and combined model, respectively. The combined model (AUC = 0.954 and 0.821) had better survival association than the intratumoral model (AUC = 0.833 and 0.813) and the peritumoral model (AUC = 0.824 and 0.687). The proposed peritumoral model with radiomics features may serve as a tool to improve estimated survival time. 3 TECHNICAL EFFICACY: Stage 4.