Dynamic contrast-enhanced MRI-derived intratumoral heterogeneity quantification score: Improving lymphovascular invasion and invasive breast cancer recurrence-free survival predictions.

[INTRODUCTION] To investigate whether preoperative magnetic resonance imaging (MRI)-based quantification of intratumoral heterogeneity (ITH) in invasive breast cancer (IBC) can predict lymphovascular invasion (LVI) and recurrence-free survival (RFS).

[METHODS] In this retrospective cohort study, 223 patients with histopathologically confirmed IBC who underwent preoperative dynamic contrast-enhanced MRI were analyzed. ITH scores were calculated using texture features and pixel distribution. Peak tumor enhancement phase-driven clustering defined tumor subregions for radiomics and habitats feature extraction. Six machine-learning models integrating MRI morphological features (MRI-MF) and significant indicators contributed to the development of the prognostic nomogram. Performance was assessed via area under the receiver operating characteristic curve (AUC), Cox proportional hazards regression, and Kaplan-Meier analyses.

[RESULTS] In the validation set, the ITH score exhibited superior predictive accuracy (AUC: 0.791, 95 % confidence interval [CI]: 0.706-0.870) compared to habitats (AUC: 0.762, 95 % CI: 0.676-0.837) and radiomics (AUC: 0.739, 95 % CI: 0.648-0.826) analyses. The CatBoost-driven hybrid model, combining ITH and MRI-MF, achieved the highest performance (AUC: 0.869, 95 % CI: 0.763-0.948). Kaplan-Meier analysis indicated significantly shorter RFS for patients with LVI (+), high ITH scores, and high nomogram risk (all p < 0.005).

[CONCLUSION] The ITH score predicts LVI and RFS in patients with IBC, with the CatBoost-driven hybrid model improving personalized prognosis and treatment.

[IMPLICATIONS FOR PRACTICE] The findings of this study underscore the potential of noninvasive ITH quantification for precise risk assessment, with possible implications for preoperative decision-making in IBC management.