作者
Xiaofang Guo,Yaoyao He,Zheng Yuan,Tingting Nie,Yulin Liu,Haibo Xu
摘要
Background The use of peritumoral features to determine the survival time of patients with rectal cancer (RC) is still imprecise. Purpose To explore the correlation between intratumoral, peritumoral and combined features, and overall survival (OS). Study Type Retrospective. Population One hundred sixty‐six RC patients (53 women, 113 men; average age: 55 ± 12 years) who underwent radical resection after neoadjuvant therapy. Field Strength/Sequence 3 T; T 2 WI sagittal, T 1 WI axial, T 2 WI axial with fat suppression, and high‐resolution T 2 WI axial sequences, enhanced T 1 WI axial and sagittal sequences with fat suppression. Assessment 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. Statistical Tests LASSO, regression analysis, ROC, DeLong method, Kaplan–Meier curve. P < 0.05 indicated a significant difference. Results 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). Data Conclusion The proposed peritumoral model with radiomics features may serve as a tool to improve estimated survival time. Evidence Level 3 Technical Efficacy Stage 4