Can quantitative perfusion CT-based biomarkers predict renal cell carcinoma subtypes?

Purpose: To assess diagnostic accuracy of perfusion CT (pCT) based biomarkers in differentiating clear-cell renal cell carcinoma (ccRCC) from non-ccRCC.

Materials and method: This retrospective study comprised 95 patients with RCCs (70 ccRCCs and 25 non-ccRCCs) who had perfusion CT (pCT) before surgery between January 2017 and December 2022. Two readers independently recorded PCT parameters [blood flow (BF), blood volume (BV), mean transit time (MTT), and time to peak (TTP)] by drawing a circular ROI on the tumor. The open-source program "Labelme" was used to create a polygonal bounding box to outline tumor borders. The intraclass correlation coefficient (ICC) was used to determine interreader agreement. The pCT model was evaluated using multivariable logistic regression analysis with the STATA 18 program to determine the importance of each of these characteristics in predicting the type of tumor.

Results: Clear cell RCC had significantly greater MIP and lower TTP values than non-clear cell RCC (p < 0.05). RCCs showed considerably higher TTP, MTT, and lower MIP values than the normal renal cortex (p < 0.05). At a threshold of 129 HU, MIP had an AUC of 0.78, sensitivity and specificity of 80% and 70%, respectively, according to ROC analysis.

Conclusions: pCT has a high diagnostic accuracy in distinguishing between ccRCC and non-ccRCC tumors; Clinical relevance: A non-invasive, accurate, reliable, and reproducible imaging biomarker for RCC subtype prediction is possible on pCT, which may be significant for evaluating the response to antiangiogenic therapy.