DCE-MRI kinetic heterogeneity facilitates the discrimination of benignity and malignancy of breast in combination with the DWI and morphological features.

PURPOSE: This study aimed to validate the diagnostic value of kinetic heterogeneity (KH) across multiple cohorts and to develop a nomogram integrating KH, apparent diffusion coefficient (ADC), and key morphological and clinical features for preoperative discrimination between benign and malignant breast masses.

METHODS

This retrospective study included 501 female patients with 541 confirmed breast mass lesions. Training (n = 280) and internal validation (n = 119) cohorts were collected (2018-2019). Temporal validation (n = 101, 2024) and independent test cohorts (n = 41, 2025-2026), from a different scanner system) were also included. KH was computed using voxel-wise time-intensity curve analysis. Independent predictors were identified using logistic regression to construct a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, and compared with KH-, ADC-, and combined models.

RESULTS

Age, lesion margin, ADC, and KH were independent predictors. The nomogram achieved AUCs of 0.897 (95% confidence interval [CI]: 0.838-0.955) and 0.909 (95% CI: 0.811-1.000) in the temporal validation and independent test cohorts, outperforming the ADC + KH (0.855, 0.890), ADC (0.829, 0.848), and KH (0.803, 0.757) models. Sensitivities were 93.8% and 94.1%, with negative predictive values (NPVs) of 92.7% and 95.2%, respectively. Calibration and decision curve analysis demonstrated good agreement and positive net benefit across a wide range of threshold probabilities.

CONCLUSION

The nomogram integrating KH, ADC, margin, and age demonstrated superior diagnostic performance in preoperatively discriminating between benign and malignant breast mass lesions.