Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study.

BACKGROUND: 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). METHODS: 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. FINDINGS: 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. INTERPRETATION: We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy. FUNDING: None.

Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients.

INTRODUCTION: We aimed to develop an explainable machine learning (ML) model to predict side-specific extraprostatic extension (ssEPE) to identify patients who can safely undergo nerve-sparing radical prostatectomy using preoperative clinicopathological variables. METHODS: A retrospective sample of clinicopathological data from 900 prostatic lobes at our institution was used as the training cohort. Primary outcome was the presence of ssEPE. The baseline model for comparison had the highest performance out of current biopsy-derived predictive models for ssEPE. A separate logistic regression (LR) model was built using the same variables as the ML model. All models were externally validated using a testing cohort of 122 lobes from another institution. Models were assessed by area under receiver-operating-characteristic curve (AUROC), precision-recall curve (AUPRC), calibration, and decision curve analysis. Model predictions were explained using SHapley Additive exPlanations. This tool was deployed as a publicly available web application. RESULTS: Incidence of ssEPE in the training and testing cohorts were 30.7 and 41.8%, respectively. The ML model achieved AUROC 0.81 (LR 0.78, baseline 0.74) and AUPRC 0.69 (LR 0.64, baseline 0.59) on the training cohort. On the testing cohort, the ML model achieved AUROC 0.81 (LR 0.76, baseline 0.75) and AUPRC 0.78 (LR 0.75, baseline 0.70). The ML model was explainable, well-calibrated, and achieved the highest net benefit for clinically relevant cutoffs of 10-30%. CONCLUSIONS: We developed a user-friendly application that enables physicians without prior ML experience to assess ssEPE risk and understand factors driving these predictions to aid surgical planning and patient counselling (https://share.streamlit.io/jcckwong/ssepe/main/ssEPE_V2.py).

The Bladder Utility Symptom Scale: A Novel Patient Reported Outcome Instrument for Bladder Cancer.

PURPOSE: Health related quality of life is important in bladder cancer care and clinical decision making because patients must choose between diverse treatment modalities with unique morbidities. A patient reported outcome measure of overall health related quality of life for bladder cancer regardless of disease severity and treatment could benefit clinical care and research. MATERIALS AND METHODS: Prospective questionnaire development was completed in 3 parts. In study 1 the BUSS (Bladder Utility Symptom Scale) questions were created by experts using a conceptual framework of bladder cancer health related quality of life generated through patient focus groups. In study 2 patients with bladder cancer, including those treated with surgery, radiation and chemotherapy, completed the BUSS and 5 health related quality of life instruments at baseline and 4 weeks to assess validity and test-retest reliability. External validity was then explored in study 3 by administering the BUSS to 578 patients online and at clinics. Construct validity was assessed by whole and subscale Spearman rank correlations, and by comparisons of BUSS scores across known groups. RESULTS: The BUSS had high whole scale correlation with the FACT-Bl (Functional Assessment of Cancer Therapy-Bladder) (rs = 0.82, p <0.0001) and substantial to high subscale correlations with the EQ-5D™-3L (EuroQol 5 Dimensions Questionnaire-3 Levels) (eg emotional well-being rs = 0.69, p <0.0001). BUSS scores were lower in patients with comorbidity and advanced disease. Cognitive debriefing and the 94% completion rate suggested good comprehensibility. There was excellent test-retest reliability (ICC = 0.79). Limitations included an extended time from diagnosis in many patients. CONCLUSIONS: The BUSS is a reliable and valid patient reported outcome instrument for health related quality of life in all patients with bladder cancer regardless of the treatment received or the stage of disease.

A case of transitional cell carcinoma seeding of small bowel anastomosis after radical cystoprostatectomy.

We present a case in which a man developed transitional cell carcinoma of the small bowel 6 months after having undergone a cystoprostatectomy and ileal conduit urinary diversion.