Feng Wu

Multiparametric MRI radiomics models for preoperative assessment of lymph vascular space invasion status in early-stage cervical cancer: a 2-centre retrospective study

Abstract Objective To preoperatively predict lymphovascular space invasion (LVSI) in early-stage cervical cancer (CC) using multiparametric MRI (mpMRI) radiomics models. Methods This dual-centre study included 196 early-stage CC patients (Centre A: 142, Dec 2020-Apr 2023; Centre B: 54, May-Oct 2023). Centre A was partitioned into training (n = 99) and internal validation (n = 43) cohorts; Centre B served as external validation. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted MRI (CE-MRI) sequences. Feature stability was assessed via intraclass correlation and Dice coefficient, with selection through linear correlation and F-tests. Seven radiomics models (single/combined sequences) were built using the top-performing algorithm among 11 machine learning methods. A combination model (CMIC) integrated the optimal mpMRI model’s rad-score with clinical factors. Performance was evaluated by ROC, calibration curves, and DCA across all cohorts. Results The AdaBoost-based mpMRI model (CE-MRI + DWI + T2WI) utilized 12 selected features. It achieved AUCs of 0.953 (95% CI: 0.916-0.989) in training, 0.868 (0.755-0.981) in internal validation, and 0.797 (0.677-0.916) externally. The CMIC model showed comparable performance (training: 0.957; validation: 0.864; external: 0.847), with no significant differences versus the mpMRI model (P > .05 all cohorts). Conclusion The AdaBoost-driven mpMRI radiomics model effectively predicts LVSI in early-stage CC. Both mpMRI and CMIC models demonstrate robust preoperative predictive capability. Advances in knowledge This mpMRI radiomics approach using AdaBoost outperforms single-sequence models for LVSI prediction, enabling personalized treatment strategies for early-stage CC.