Histogram analysis of apparent diffusion coefficient maps for assessing thymic epithelial tumors: correlation with world health organization classification and clinical staging.

Objective: To investigate the value of apparent diffusion coefficients (ADCs) histogram analysis for assessing World Health Organization (WHO) pathological classification and Masaoka clinical stages of thymic epithelial tumours. Methods: 37 patients with histologically confirmed thymic epithelial tumours were enrolled. ADC measurements were performed using hot-spot ROI (ADC(HS-ROI)) and histogram-based approach. ADC histogram parameters included mean ADC (ADC(mean)), median ADC (ADC(median)), 10 and 90 percentile of ADC (ADC(10) and ADC(90)), kurtosis and skewness, One-way ANOVA, independent-sample t-test, and receiver operating characteristic were used for statistical analyses. Results: There were significant differences in ADC(mean), ADC(media), ADC(10), ADC(90) and ADC(HS-ROI) among low-risk thymoma (type A, AB, B1; n = 14), high-risk thymoma (type 82, B3; n = 9) and thymic carcinoma (type C, n = 14) groups (all p-values <0.05), while no significant difference in skewness (p = 0.181) and kurtosis (p = 0.088). ADC(10) showed best differentiating ability (cut-off value, <= 0.689 x 10(-3) mm(2) s(-1); AUC, 0.957; sensitivity, 95.65%; specificity, 92.86%) for discriminating low-risk thymoma from high-risk thymoma and thymic carcinoma. Advanced Masaoka stages (Stage III and IV; n = 24) tumours showed significant lower ADC parameters and higher kurtosis than early Masaoka stage (Stage I and II; n = 13) tumours (all p-values <0.05), while no significant difference on skewness (p = 0.063). ADC(10) showed best differentiating ability (cut-off value, <= 0.689 x 10(-3) mm(2) s(-1); AUC, 0.913; sensitivity, 91.30%; specificity, 85.71%) for discriminating advanced and early Masaoka stage epithelial tumours. Conclusion: ADC histogram analysis may assist in assessing the WHO pathological classification and Masaoka clinical stages of thymic epithelial tumours. Advances in knowledge: 1. ADC histogram analysis could help to assess WHO pathological classification of thymic epithelial tumours. 2. ADC histogram analysis could help to evaluate Masaoka clinical stages of thymic epithelial tumours. 3. ADC 10 might be a promising imaging biomarker for assessing and characterizing thymic epithelial tumours.