Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes.

Objective: The aim of the study was to assess and compare dual-energy CT (DECT) based quantitative parameters to differentiate between clear cell renal cell carcinoma (ccRCC) and non-ccRCC. Materials and Methods: This was a retrospective study including RCC patients who underwent DECT prior to surgery between January 2017 and December 2022. Two DECT parameters-iodine concentration (IC) and iodine ratio (IR)-were measured by two independent readers who manually drew circular regions of interest on the most enhancing part of the tumor. Inter-reader agreement was calculated using the intraclass correlation coefficient. Machine learning (ML) models trained to classify the histologic subtype as ccRCC and non-ccRCC, and grade of ccRCC as low or high, were evaluated for their accuracy. Results: A total of 112 patients (mean age: 65 years; male:female: 61:51), with 87 ccRCCs and 25 non-ccRCCs, were included. There was good inter-reader agreement for both IC and IR with a Pearson coefficient of 0.89. The individual DECT parameters had an accuracy of 77.7% (IC) and 77.5% (IR) for distinguishing ccRCC and non-ccRCC. Random Forest classifier and AdaBoost were the best ML models with an accuracy of 89.2% each. When ML algorithms were combined, the performance was improved, with AdaBoost performing the best with an accuracy of 100%. To distinguish low- and high-grade ccRCCs, IC and IR had an accuracy of 77.9 and 77.6%, respectively, while the ML models all did equally well with an accuracy of 77.6%. Combining ML algorithms again led to improved performance, with AdaBoost being the best overall ML model. Conclusion: DECT-based quantitative imaging biomarkers have moderate diagnostic accuracy, which can be greatly improved using ML to differentiate between ccRCC and non-ccRCC and predict the grade of ccRCC.