Wayne Brisbane1, Alan Priester2,3, Sakina Mohammed Mota3, Joshua Shubert3, Jeremy Bong3, James Sayre4,
Brittany Berry-Pusey3, Shyam Natarajan2,3,
1University of Florida, Department of Urology
2David Geffen School of Medicine, Department of Urology
3Avenda Health, Inc.
4University of California, Los Angeles, Department of Radiological Sciences and Biostatistics
Introduction:
Focal therapy (FT) for prostate cancer is gaining prominence as an alternative to whole-gland treatment. FT efficacy relies on predicting disease margins, but their underestimation and patient-specific optimization remain largely unaddressed problems. An artificial intelligence (AI)-based cancer mapping and decision support tool was built using pre-biopsy MRI, targeted biopsy data, and PSA to aid urologists in identifying cancer margins. A reader study was conducted to compare this AI-based software against the standard of care (SOC) in determining the extent of clinically significant prostate cancer (csPCa).
Methods:
Seven urologists and three radiologists from five institutions with 2 – 23 years of expertise each evaluated 50 prostatectomy cases (total of 1000 reads). Cases were prospectively eligible for FT, with GG 2-3 csPCa, ≥1 region of interest (ROI), and disease that appeared localized to a single hemisphere or the anterior gland. Each case included T2-weighted MRI, ROI segmentation, and pathology reports with conventional locations. Readers were asked to produce contours on each image that prioritized the inclusion of all csPCa, excluding non-csPCa tissue as a secondary objective. First, readers manually defined margins using all given data (SOC). Then, after ≥4 weeks had passed, readers produced AI-assisted margins using custom software (iQuest, Avenda Health, CA) [Fig. B]. Margins from each method [Fig. C] were evaluated against WM pathology data [Fig. D] as ground truth. Statistical tests were performed using generalized estimating equations.
Results:
AI margins had superior sensitivity (97.4% vs. 38.2%, p < 0.0001) to SOC margins in classifying csPCa [Fig. A]. AI-assisted margins also had superior balanced accuracy, i.e. (specificity + sensitivity)/2, to SOC margins (84.7% vs. 67.2%, p < 0.0001). On average, AI-assisted margins completely encapsulated csPCa in 72.8% of cases, compared to only 1.6% of cases with SOC methods (p < 0.0001). Furthermore, the average time spent fell from 3.5 minutes (SOC) to 2.0 minutes (AI-assisted, p < 0.0001).
Conclusions:
AI-assisted cancer mapping helps address the systematic underestimation of csPCa by SOC methods. This study establishes that AI-assisted margins greatly improve csPCa encapsulation, which could improve oncological efficacy for focal treatments.
