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.
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 cancerpatients who wish to avoid prolonged urinary incontinence after robot-assisted radical prostatectomy.