Quantitative CT-based biomarkers for predicting Renal cell carcinoma subtypes: a comparison of Dual-Energy CT, Perfusion CT, and CT texture parameters

Purpose To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distinguishing between clear-cell renal cell carcinoma (ccRCC) and non-ccRCC. Methods This retrospective study included 66 patients with RCC (52 ccRCC and 14 non-ccRCC) who underwent DECT and PCT imaging before surgery between January 2017 and December 2022. Two independent radiologists measured DECT parameters (iodine concentration and iodine ratio) and PCT parameters (blood flow, blood volume, mean transit time, and time to peak) using circular ROIs placed on tumors. For CTTA, the largest tumor cross-section in the corticomedullary phase was manually annotated using the "labelme" tool, and texture features were extracted with Python libraries including "scipy" and "numpy." Multivariate logistic regression analysis was performed to assess the ability of PCT, DECT, and CTTA models to predict tumor subtypes. Results All three imaging modalities demonstrated high diagnostic accuracy, with F1 scores of 0.9107, 0.9358, and 0.9348 for PCT, DECT, and CTTA, respectively. Inter-reader agreement for PCT and DECT parameters was excellent (Pearson correlation > 0.85). None of the three models were significantly different (p > 0.05). While each modality could effectively differentiate between ccRCC and non-ccRCC, higher iodine ratio (IR) on DECT and increased entropy on CTTA were independent predictors of ccRCC, with F1 scores of 0.9345 and 0.9272, respectively (p < 0.001). The combined ML model integrating DECT, PCT, and CTTA parameters yielded the highest diagnostic accuracy, with an F1 score of 0.954. Conclusions The diagnostic accuracy of PCT, DECT, and CTTA in distinguishing between ccRCC and non-ccRCC tumors was equivalent and high. However, among these three methods, only IR on DECT and entropy on CTTA were identified as independent predictors of the RCC subtype; hence, these two quantitative markers may be more applicable in clinical practice. Clinical relevance: Accurate, non-invasive biomarkers are essential to differentiate RCC subtypes, aiding in prognosis and guiding targeted therapies, particularly in ccRCC, where treatment options differ significantly.