Multi-parametric MRI Radiomics models for preoperative assessment of lymph vascular space invasion status in early-stage cervical cancer: A two-center retrospective study.

Abstract

Objective: To preoperatively predict lymphovascular space invasion (LVSI) in early-stage cervical cancer (CC) using multi-parametric MRI (mpMRI) radiomics models.

Methods: This dual-center study included 196 early-stage CC patients (Center A: 142, Dec2020-Apr2023; Center B: 54, May-Oct2023). Center A was partitioned into training (n = 99) and internal validation (n = 43) cohorts; Center B served as external validation. Radiomics features were extracted from T2WI, DWI, and CE-MRI sequences. Feature stability was assessed via intra-class 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 eleven 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 > 0.05 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.