Histogram analysis in diffusion-weighted imaging in differentiating breast masses

Background: Gray-scale histogram analysis has been submitted to evaluate the heterogeneity of the diffusion distribution among different sorts of tumors in the body. Measures obtained from apparent diffusion coefficient (ADC) histograms reflect the histopathological heterogeneity, distributions of cellular density, and tissue degeneration. This can supply a more credible base for recognition, categorization, and prognosis assessment of benign and malignant tumors. The aim of this work was to assess the role of ADC histogram analysis in differentiating benign from malignant breast lesions. Results: Among ADC histogram parameters, there was significant difference between benign and malignant lesions regarding to ADC mean being 1.59 ± 0.32 for benign tumors versus 0.871 ± 0.29 for malignant tumors (P value < 0.001), ADC minimum being 1.09 ± 0.44 for benign lesions versus 0.432 ± 0.327 malignant lesions (P value < 0.001), ADC maximum being 1.92 ± 0.387 for benign lesions versus 1.27 ± 0.390 for malignant lesions (P value < 0.001), and kurtosis being 3.71 ± 2.54 for benign lesions versus 6.23 ± 3.82 for malignant lesions (P value = 0.007). Among ADC histogram parameters, ADC mean had the highest diagnostic performance with AUC (0.959), specificity (95.7%), and accuracy (93.3%). Conclusion: ADC histogram analysis is used as sensitive and specific technique in differentiating benign from malignant breast lesions with ADC mean showing the highest diagnostic performance among ADC histogram parameters. © The Author(s) 2024.