Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer

作者：Xuelei Qubie, Weijuan Chen, Jun Chen, Jun Ma, Xin Wei, Xiling Gu, Wei Zhang, Xiaojing He · 发表于：Frontiers in Oncology · 年份：2025 · DOI：10.3389/fonc.2025.1610892 · 被引用次数：3 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Surgical Treatments、Colorectal and Anal Carcinomas

Objective: The present research aimed to evaluate the diagnostic performance of a magnetic resonance imaging (MRI)-based radiomics model for predicting lymph node staging in patients with stage T3 rectal cancer (RC). Methods: This retrospective study included 225 patients with RC who underwent surgical resection without neoadjuvant therapy treatment. Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning classifiers were employed to construct radiomics signatures differentiating between N0/N1 (low nodal burden) and N2 (high nodal burden) stages prediction in the training cohort. The predictive performance of each classifier was evaluated using receiver operating characteristic curve analysis, with area under the curve (AUC) comparisons conducted via DeLong's test. Decision curve analysis (DCA) and calibration curves were utilized to assess the clinical utility and calibration performance of the developed models, respectively. Results: A total of 1,746 radiomics features were extracted from the imaging data, of which 16 features were selected to construct a radiomics signature for lymph node staging in RC. The logistic regression classifier demonstrated the best predictive performance, achieving an AUC of 0.900 [95% confidence interval (CI), 0.848-0.952] in the training cohort. The model's robustness was...