Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer.
Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA).
Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors.
Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75-0·78] and pooled AUPRC of 0·61 [0·58-0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 [44%] in the logistic regression model, none in the Sayyid model, 13 [12%] in the Soeterik non-MRI model, and five [5%] in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives.
We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy.
None.
The Lancet. Digital health. 2023 May 19 [Epub ahead of print]
Jethro C C Kwong, Adree Khondker, Eric Meng, Nicholas Taylor, Cynthia Kuk, Nathan Perlis, Girish S Kulkarni, Robert J Hamilton, Neil E Fleshner, Antonio Finelli, Theodorus H van der Kwast, Amna Ali, Munir Jamal, Frank Papanikolaou, Thomas Short, John R Srigley, Valentin Colinet, Alexandre Peltier, Romain Diamand, Yolene Lefebvre, Qusay Mandoorah, Rafael Sanchez-Salas, Petr Macek, Xavier Cathelineau, Martin Eklund, Alistair E W Johnson, Andrew Feifer, Alexandre R Zlotta
Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada., Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada., Faculty of Medicine, Queen's University, Kingston, ON, Canada., Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada., Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada., Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Laboratory Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada., Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada., Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada., Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Division of Urology, Department of Surgery, Jules Bordet Institute, Brussels, Belgium., Department of Medical Imagery, Jules Bordet Institute, Brussels, Belgium., Division of Urology, Department of Surgery, L'Institut Mutualiste Montsouris, Paris, France., Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden., Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada., Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada., Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada. Electronic address: .