A retrospective magnetic resonance imaging-based radiomics study for predicting lymph node regression status following neoadjuvant therapy in patients with locally advanced rectal cancer

Abstract

Background

Lymph node metastasis (LNM) status serves as a key prognostic marker in patients with locally advanced rectal cancer (LARC). Neoadjuvant chemoradiotherapy (nCRT) can influence lymph node status by inducing regression, with treatment responses exhibiting substantial variability among patients. We aimed to develop and validate a random forest-based radiomics model for non-invasively predicting lymph node regression status following neoadjuvant therapy in patients with LARC using pretreatment magnetic resonance imaging (MRI).

Methods

This retrospective study included 285 patients with LARC who were treated with nCRT followed by elective resection at Qingdao University Affiliated Hospital between October 2019 and October 2023. Patients were randomly allocated to training and testing sets in a 7:3 ratio. Baseline characteristics including gender, age, body mass index (BMI), and other 13 clinical variables were compared between the groups, with no significant differences observed (P >0.05). High-resolution T2-weighted MRI scans of the rectal region were collected preoperatively. Regions of interest corresponding to primary tumor lesions were manually delineated on the MRI scans, followed by radiomic feature extraction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression model. A machine learning random forest predictive model was subsequently constructed, incorporating selected radiomics features and clinical variables.

Results

Model performance for predicting lymph node status was assessed using receiver operating characteristic curve analysis, area under the curve (AUC), and calibration curves. Four features were selected from 1,051 radiomic features to construct a radiomics model, achieving an AUC of 0.743 in the test set. Four features were also extracted from 19 clinical parameters to develop a clinical data model, with an AUC of 0.727. Integrating radiomic features and clinical data yielded a combined model with superior performance in the test set (AUC 0.794).

Conclusion

The radiomics model derived from pretreatment rectal MRI in patients with LARC demonstrated strong predictive capability for assessing metastatic lymph node responses to nCRT.