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

• Published in: Scientific reports Vol. 10; no. 1; p. 19503
• Main Authors: Purkayastha, Subhanik, Zhao, Yijun, Wu, Jing, Hu, Rong, McGirr, Aidan, Singh, Sukhdeep, Chang, Ken, Huang, Raymond Y., Zhang, Paul J., Silva, Alvin, Soulen, Michael C., Stavropoulos, S. William, Zhang, Zishu, Bai, Harrison X.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.11.2020; Nature Publishing Group
• Subjects: 631/114/1305; 692/700/139; Adult; Aged; Aged, 80 and over; Bayes Theorem; Bayesian analysis; Carcinoma, Renal Cell - diagnosis; Carcinoma, Renal Cell - diagnostic imaging; Decision making; Diagnosis, Differential; Female; Humanities and Social Sciences; Humans; Kidney cancer; Kidney Neoplasms - diagnosis; Kidney Neoplasms - diagnostic imaging; Learning algorithms; Lesions; Machine Learning; Magnetic Resonance Imaging; Male; Middle Aged; multidisciplinary; Neoplasm Grading - methods; Radiomics; Renal cell carcinoma; Retrospective Studies; ROC Curve; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-76132-z

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.