A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer

• Published in: Asian journal of surgery Vol. 47; no. 1; pp. 450 - 458
• Main Authors: Yan, Hao, Yang, Hongjie, Jiang, Peishi, Dong, Longchun, Zhang, Zhichun, Zhou, Yuanda, Zeng, Qingsheng, Li, Peng, Sun, Yi, Zhu, Siwei
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2024
• Subjects: Humans; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic metastasis; Lymphatic Metastasis - diagnostic imaging; Lymphatic Metastasis - pathology; Magnetic Resonance Imaging - methods; Prognosis; Radiomics; Rectal neoplasms; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - pathology; Retrospective Studies; Lymphatic metastasis; Prognosis; Rectal neoplasms; Radiomics
• ISSN: 1015-9584; 0219-3108
• DOI: 10.1016/j.asjsur.2023.09.156

The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients. This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets. The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0. Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.