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A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.
BJU Int. 2019 09; 124(3):487-495.BI

Abstract

OBJECTIVES

To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes.

SUBJECTS AND METHODS

Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests.

RESULTS

Continence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively).

CONCLUSION

Using APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP.

Authors+Show Affiliations

Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.School of Electronics Engineering and Computer Science, Peking University, Beijing, China.Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.Department of Information Systems, University of Maryland, Baltimore, MD, USA.Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

30811828

Citation

Hung, Andrew J., et al. "A Deep-learning Model Using Automated Performance Metrics and Clinical Features to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy." BJU International, vol. 124, no. 3, 2019, pp. 487-495.
Hung AJ, Chen J, Ghodoussipour S, et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. 2019;124(3):487-495.
Hung, A. J., Chen, J., Ghodoussipour, S., Oh, P. J., Liu, Z., Nguyen, J., Purushotham, S., Gill, I. S., & Liu, Y. (2019). A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU International, 124(3), 487-495. https://doi.org/10.1111/bju.14735
Hung AJ, et al. A Deep-learning Model Using Automated Performance Metrics and Clinical Features to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy. BJU Int. 2019;124(3):487-495. PubMed PMID: 30811828.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. AU - Hung,Andrew J, AU - Chen,Jian, AU - Ghodoussipour,Saum, AU - Oh,Paul J, AU - Liu,Zequn, AU - Nguyen,Jessica, AU - Purushotham,Sanjay, AU - Gill,Inderbir S, AU - Liu,Yan, Y1 - 2019/03/20/ PY - 2019/2/28/pubmed PY - 2020/5/19/medline PY - 2019/2/28/entrez KW - artificial intelligence KW - prostatectomy KW - quality of life KW - robotic surgical procedures KW - urinary incontinence SP - 487 EP - 495 JF - BJU international JO - BJU Int. VL - 124 IS - 3 N2 - OBJECTIVES: To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes. SUBJECTS AND METHODS: Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests. RESULTS: Continence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively). CONCLUSION: Using APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP. SN - 1464-410X UR - https://www.unboundmedicine.com/medline/citation/30811828/A_deep_learning_model_using_automated_performance_metrics_and_clinical_features_to_predict_urinary_continence_recovery_after_robot_assisted_radical_prostatectomy_ L2 - https://doi.org/10.1111/bju.14735 DB - PRIME DP - Unbound Medicine ER -