Abstract
BACKGROUND: Breast cancer's diagnostic challenge is amplified by its heterogeneity. Diffusion-Weighted Imaging (DWI) offers promising avenues for precise tumor characterization through Apparent Diffusion Coefficient (ADC) metrics. PURPOSE: To investigate the diagnostic utility of advanced ADC metrics in distinguishing breast lesions using Magnetic Resonance Imaging (MRI). METHODS: A retrospective cohort analysis of MRI data from 125 pathologically confirmed breast tumors was conducted. ADC values were independently measured by two physicians at the lesion sites and reference points (contralateral normal breast parenchyma, pectoralis major, and interventricular septum), from which advanced ADC metrics were calculated. Statistical analyses were applied to differentiate ADC metrics between malignant and benign groups. ROC curves assessed the diagnostic efficacy of individual ADC metrics. A binary logistic regression model incorporating ADC metrics and age was developed, with its diagnostic superiority evaluated through multidimensional comparisons. RESULTS: Of the 125 lesions, 77 were malignant and 48 benign. Significant differences in ADC metrics were found between malignant and benign tumors. Diagnostic analysis showed minimum ADC value (ADC_min) as the most effective single indicator, while the combined model, including age and average ADC value (ADC_avg), outperformed individual ADC metrics, demonstrating superior diagnostic accuracy (area under the curve (AUC) = 0.964). The combined model nomogram also showed improved clinical utility and a significant increase in diagnostic performance. CONCLUSIONS: Advanced ADC metrics significantly enhance the diagnostic accuracy for differentiating between benign and malignant breast lesions. The development of a combined model further refines breast cancer diagnostics, supporting the advancement towards precision medicine.