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Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I–II) from high-grade (Fuhrman III–IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49–0.68), accuracy of 0.77 (95% CI 0.68–0.84), sensitivity of 0.38 (95% CI 0.29–0.48), and specificity of 0.86 (95% CI 0.78–0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50–0.69), accuracy of 0.81 (95% CI 0.72–0.88), sensitivity of 0.12 (95% CI 0.14–0.30), and specificity of 0.97 (95% CI 0.87–0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.

Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm