Quantitative CT biomarkers for renal cell carcinoma subtype differentiation: a comparison of DECT, PCT, and CT...

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.

This retrospective study included 66 patients with RCC (52 ccRCC and 14 non-ccRCC) who underwent DECT and PCT imaging before surgery (2017-2022). This DECT parameters (iodine concentration, iodine ratio [IR]) and PCT parameters (blood flow, blood volume, mean transit time, time to peak) were measured using circular regions of interest (ROIs). CT texture analysis features were extracted from manually annotated corticomedullary-phase images. A machine learning (ML) model was developed to differentiate RCC subtypes, with performance evaluated using k-fold cross-validation. Multivariate logistic regression analysis was performed to assess the predictive value of each imaging modality.

All 3 imaging modalities demonstrated high diagnostic accuracy, with F1 scores of 0.9107, 0.9358, and 0.9348 for PCT, DECT, and CTTA, respectively. None of the 3 models were significantly different (P > 0.05). While each modality could effectively differentiate between ccRCC and non-ccRCC, higher 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). Dual-energy CT achieved the highest individual performance, with IR being the best predictor (F1 = 0.902). Iodine ratio was significantly higher in ccRCC (65.12 ± 23.73) compared to non-ccRCC (35.17 ± 17.99, P < 0.001), yielding an Area under curve (AUC) of 0.91, sensitivity of 87.5%, and specificity of 89.3%. Entropy on CTTA was the strongest texture feature, with higher values in ccRCC (7.94 ± 0.336) than non-ccRCC (6.43 ± 0.297, P < 0.001), achieving an AUC of 0.94, sensitivity of 83.0%, and specificity of 92.3%. The combined ML model integrating DECT, PCT, and CTTA parameters yielded the highest diagnostic accuracy, with an F1 score of 0.954.

PCT, DECT, and CTTA effectively differentiate RCC subtypes. However, IR (DECT) and entropy (CTTA) emerged as key independent markers, suggesting their clinical utility in RCC characterization.

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.