MR texture analysis in differentiation of small and very small renal cell carcinoma subtypes

Purpose To explore the diagnostic efficacy of MR-based texture analysis in differentiation of small (≤ 4 cm) and very small (≤ 2 cm) renal cell carcinoma subtypes. Methods One hundred and eight patients with pT1a (≤ 4 cm) renal cell carcinoma and pretreatment MRI were enrolled in this retrospective study. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from whole-tumor images. Among subtypes, patient age, tumor size, histological grading and texture parameters were compared. Diagnostic model using combination of texture parameters was constructed using logistic regression and validated using fivefold cross-validation. AUC with 95% CI, accuracy, sensitivity and specificity for subtype differentiation are reported. Further we explored the distinguishing ability of texture parameters and diagnostic model in very small (≤ 2 cm) RCC subgroups. Results Significant texture parameters among RCC subtypes were identified. For small (≤ 4 cm) renal cell carcinoma subtyping, combining models based on texture parameters achieved good AUCs for differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC (0.79, 0.74 and 0.81). Further, in subgroups of very small (≤ 2 cm) RCCs, diagnostic models had better differentiating performances, achieving AUCs of 0.88, 0.99, 0.96 in differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC. Conclusion MR texture analysis may help to differentiate small (≤ 4 cm) and very small (≤ 2 cm) RCC subtypes. This non-invasive method can potentially provide additional information for localized RCC treatment and surveillance strategy. Graphical abstract