Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer

• Published in: World journal of urology Vol. 38; no. 10; pp. 2329 - 2347
• Main Authors: Suarez-Ibarrola, Rodrigo, Hein, Simon, Reis, Gerd, Gratzke, Christian, Miernik, Arkadiusz
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-10-2020; Springer Nature B.V
• Subjects: Algorithms; Cytology; Deep learning; Gene expression; Kidney cancer; Learning algorithms; Literature reviews; Lithiasis; Medicine; Medicine & Public Health; Nephrology; Oncology; Predictions; Prostate cancer; Prostate carcinoma; Radiomics; Renal cell carcinoma; Surgical outcomes; Topic Paper; Urinary tract diseases; Urology; Deep learning; Urolithiasis; Renal cell carcinoma; Convolutional neural network; Machine learning; Artificial neural network; Artificial intelligence; Prostate cancer; Bladder cancer
• ISSN: 0724-4983; 1433-8726
• DOI: 10.1007/s00345-019-03000-5

Purpose
The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.
Methods
A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).
Results
In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.
Conclusions
The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.