Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer

• Published in: Updates in surgery Vol. 75; no. 8; pp. 2225 - 2234
• Main Authors: Yang, Hongjie, Jiang, Peishi, Dong, Longchun, Li, Peng, Sun, Yi, Zhu, Siwei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023
• Subjects: Humans; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic Metastasis - diagnostic imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Original Article; Prospective Studies; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - pathology; Retrospective Studies; Surgery; Tomography, X-Ray Computed; Rectal cancer; Lateral lymph node metastasis; Prediction model; Radiomics
• ISSN: 2038-131X; 2038-3312
• DOI: 10.1007/s13304-023-01618-0

This study aimed to develop a radiomics model for predicting lateral lymph node (LLN) metastasis in rectal cancer patients using MR-T2WI and CT images, and assess its clinical value. This prospective study included rectal cancer patients with complete MR-T2WI and portal enhanced CT images who underwent LLN dissection at Tianjin Union Medical Center between June 2017 and November 2022. Primary lesions and LLN were segmented using 3D slicer. Radiomics features were extracted from the region of interest using pyradiomics in Python. Least absolute shrinkage and selection operator algorithm and backward stepwise regression were employed for feature selection. Three LLN metastasis radiomics prediction models were established via multivariable logistic regression analysis. The performance of the model was evaluated using receiver operating characteristic curve analysis, and the area under the curve (AUC), sensitivity, specificity were calculated for the training, validation, and test sets. A nomogram was constructed for visualization, and decision curve analysis (DCA) was performed to evaluate clinical value. We included 94 eligible patients in the analysis. For each patient, we extracted a total of 1344 radiomics features. The CT combined with MR-T2WI model had the highest AUC for all sets compared to CT and MR-T2WI models. AUC values for the CT combined with MR-T2WI model in the training, validation, and test sets were 0.957, 0.901, and 0.936, respectively. DCA revealed high prediction value for the combined MR-T2WI and CT model. A radiomics model based on CT and MR-T2WI data effectively predicted LLN metastasis in rectal cancer patients preoperatively. Graphical abstract