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Deep learning using preoperative magnetic resonance imaging information to predict early recovery of urinary continence after robot‐assisted radical prostatectomy
Author(s) -
Sumitomo Makoto,
Teramoto Atsushi,
Toda Ryo,
Fukami Naohiko,
Fukaya Kosuke,
Zennami Kenji,
Ichino Manabu,
Takahara Kiyoshi,
Kusaka Mamoru,
Shiroki Ryoichi
Publication year - 2020
Publication title -
international journal of urology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 67
eISSN - 1442-2042
pISSN - 0919-8172
DOI - 10.1111/iju.14325
Subject(s) - prostatectomy , medicine , magnetic resonance imaging , receiver operating characteristic , urinary incontinence , deep learning , urology , artificial intelligence , prostate cancer , radiology , cancer , computer science
Objectives To investigate whether a deep learning model from magnetic resonance imaging information is an accurate method to predict the risk of urinary incontinence after robot‐assisted radical prostatectomy. Methods This study included 400 patients with prostate cancer who underwent robot‐assisted radical prostatectomy. Patients using 0 or 1 pad/day within 3 months after robot‐assisted radical prostatectomy were categorized into the “good” group, whereas the other patients were categorized into the “bad” group. Magnetic resonance imaging DICOM data, and preoperative and intraoperative covariates were assessed. To evaluate the deep learning models from the testing dataset, their sensitivity, specificity and area under the receiver operating characteristic curve were analyzed. Gradient‐weighted class activation mapping was used to visualize the regions of deep learning interest. Results The combination of deep learning and naive Bayes algorithm using axial magnetic resonance imaging in addition to clinicopathological parameters had the highest performance, with an area under the receiver operating characteristic curve of 77.5% for predicting early recovery from post‐prostatectomy urinary incontinence, whereas machine learning using clinicopathological parameters only achieved low performance, with an area under the receiver operating characteristic curve of 62.2%. The gradient‐weighted class activation mapping methods showed that deep learning focused on pelvic skeletal muscles in patients in the good group, and on the perirectal and hip joint regions in patients in the bad group. Conclusions Our results suggest that deep learning using magnetic resonance imaging is useful for predicting the severity of urinary incontinence after robot‐assisted radical prostatectomy. Deep learning algorithms might help in the choice of treatment strategy, especially for prostate cancer patients who wish to avoid prolonged urinary incontinence after robot‐assisted radical prostatectomy.