Prediction of lymphovascular invasion in rectal cancer based on multimodal magnetic resonance imaging radiomics model

Abstract

BACKGROUND

Lymphovascular invasion (LVI) is an independent prognostic factor in rectal cancer, but its assessment relies on postoperative pathology. Radiomics-based analysis of multimodal magnetic resonance imaging (MRI) can provide noninvasive preoperative prediction of LVI status, supporting precision treatment decisions.

AIM

To construct a machine learning model based on multimodal MRI radiomics features for noninvasive preoperative prediction of LVI status in rectal cancer, providing decision support for individualized clinical treatment.

METHODS

A total of 278 patients with pathologically confirmed rectal cancer after surgery were retrospectively included and divided into training set (222 cases) and test set (56 cases) at an 8:2 ratio. Three sequences were used for scanning: Fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and T1-weighted contrast-enhanced imaging. PyRadiomics software was used to extract radiomics features, which were then screened through stability assessment, variance filtering, correlation analysis, univariate screening, and least absolute shrinkage and selection operator regression for key features. Single-modal models, multimodal radiomics model, clinical model, and clinical-radiomics combined model were constructed respectively. Model performance was evaluated using receiver operating characteristic curves.

RESULTS

Among 278 patients, 121 (43.5%) were LVI-positive. Twenty-three key features were selected from initial 4200 features. Multivariate analysis showed that tumor diameter ≥ 4 cm, carcinoembryonic antigen ≥ 5 ng/mL, poor differentiation, T3-4 staging, N1-2 staging, and positive perineural invasion were independent predictors of LVI. In the test set, single-modal models achieved area under the curve (AUC) of 0.708-0.775, multimodal radiomics model achieved AUC of 0.835, clinical model achieved AUC of 0.782, and the combined model performed best (AUC = 0.867, sensitivity = 0.840, specificity = 0.806). Hosmer-Lemeshow test showed good calibration for all models (P > 0.05). Decision curve analysis demonstrated that the combined model had maximum net benefit within threshold probability range of 0.15-0.65.

CONCLUSION

Machine learning models based on multimodal MRI radiomics features can effectively predict LVI status in rectal cancer, with the combined model showing optimal performance, providing a valuable quantitative tool for preoperative clinical assessment and individualized treatment decision-making.

Keywords: Rectal cancer; Lymphovascular invasion; Radiomics; Multimodal magnetic resonance imaging; Machine learning; Prediction model

Core Tip: This study developed a machine learning model integrating multimodal magnetic resonance imaging radiomics features and clinical factors to predict lymphovascular invasion in rectal cancer before surgery. Using fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, feature-level fusion achieved high predictive performance. The combined clinical-radiomics model demonstrated the best accuracy (area under the curve = 0.867), offering a noninvasive, quantitative tool to guide individualized treatment planning.