An interpretable MRI radiomics approach for preoperative prediction of lymphovascular space invasion in cervical cancer using optimal peritumoral region

Abstract

Background Lymphovascular space invasion (LVSI) is an important pathological feature associated with tumor aggressiveness and adverse prognosis in cervical cancer. However, reliable preoperative prediction of LVSI remains a challenge. This study aimed to develop and validate an MRI-based radiomics model incorporating intratumoral and peritumoral features for LVSI prediction and systematically evaluate the optimal peritumoral extent. Materials and methods In this single-center study comprising both retrospective and prospective cohorts, 204 patients with pathologically confirmed cervical cancer were randomly divided into a training cohort (n = 142) and a test cohort (n = 62). Radiomics features were extracted from the intratumoral and peritumoral regions with expansion distances of 1, 3, and 5 mm. Feature selection was performed using intraclass correlation coefficient (ICC) analysis, minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) analysis. Six machine learning algorithms, including logistic regression [LR], support vector machine, random forest, ExtraTrees, LightGBM, and multilayer perceptron, were used to construct the predictive models. The model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis. SHapley Additive Explanations (SHAP) were applied to interpret the optimal models. Results Among all ROI configurations, the Intra+P1 model demonstrated the best overall performance, particularly when it was combined with LR. The LR-based Intra+P1 model achieved an AUC of 0.866 (95% CI: 0.807–0.926) in the training cohort and 0.843 (95% CI: 0.731–0.955) in the test cohort respectively. Increasing the peritumoral expansion from 1 mm to 3 mm or 5 mm did not further improve predictive performance. The addition of clinical variables (tumor diameter and SCC level) did not provide a significant incremental predictive value. Calibration and decision curve analyses demonstrated good agreement and favorable clinical utility of the model. SHAP analysis showed that both intratumoral and peritumoral features contributed substantially to the model prediction, with texture features playing a dominant role. Conclusion This interpretable MRI-based radiomics model facilitates accurate preoperative prediction of LVSI in cervical cancer. A narrowly defined 1-mm peritumoral region provides the most informative complementary information, underscoring the importance of the tumor-invading front. This approach offers a noninvasive tool for risk stratification and may support individualized treatment decision-making.