Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm
Materials and methods: We retrospectively analyzed data from 147 patients who received MRI-targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI-based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3Dcancer probability and estimate ECE risk. Conventional ECE predictors including MRILikert scores, capsular contact length of MRI-visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison. Postsurgical specimens were processed using whole-mount histopathology section-ing, and a genitourinary pathologist assessed each quadrant for ECE presence. ECEpredictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong’s test.
Results: Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient-level ECE prediction. Unfold AI achieved 68% sensitivity,78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior(0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than in both the anterior (60%) and posterior prostate (40%).
Conclusions: Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve-spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI-based cancer mapping may lead to better oncological and functional outcomes for patients.
Authors: Alan Priester 1,2 | Sakina Mohammed Mota 1, Kyla P. Grunden 2, Joshua Shubert 1, Shannon Richardson 2, Anthony Sisk 3, Ely R. Felker 4, James Sayre 5, Leonard S. Marks 2, Shyam Natarajan 1,2, Wayne G. Brisbane 2
1 Avenda Health, Inc., United States2 Department of Urology, David Geffen School of Medicine, United States3 Department of Pathology, David GeffenSchool of Medicine, United States4 Department of Radiology, David GeffenSchool of Medicine, United States5 Department of Radiological Sciences and Biostatistics, University of California, Los Angeles, United States
Priester, A., Mota, S. M., Grunden, K. P., Shubert, J., Richardson, S., Sisk, A., Felker, E. R., Sayre, J., Marks, L. S., Natarajan, S., & Brisbane, W. G. (2024). Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm. BJUI Compass. https://doi.org/10.1002/bco2.421