Abstract
Background & Objective: Assessment of muscularis propria invasion is a crucial step in the management of urothelial carcinoma since it necessitates aggressive treatment. The diagnosis of muscle invasion is a challenging process for pathologists. Artificial intelligence is developing rapidly and being implemented in various fields of pathology. The purpose of this study was to develop an algorithm for the detection of muscularis propria invasion in urothelial carcinoma. Methods: The Training cohort consisted of 925 images from 50 specimens of urothelial carcinoma. Ninety-seven images from 10 new specimens were used as a validation cohort. Clinical validation used 127 whole specimens with a total of 617 slides. The algorithm determined areas where tumor and muscularis propria events were in nearest proximity, and presented these areas to the pathologist. Results: Analytical evaluation showed a sensitivity of 72% for muscularis propria and 65% for tumor, and a specificity of 46% and 77% for muscularis propria and tumor detection, respectively. The incorporation of the spatial proximity factor between muscularis propria and tumor in the clinical validation significantly improved the detection of muscularis propria invasion, as the algorithm managed to identify all except for one case with muscle invasive bladder cancer in the clinical validation cohort. The case missed by the algorithm was nested urothelial carcinoma, a rare subtype with unusual morphologic features. The pathologist managed to identify muscle invasion based on the images provided by the algorithm in a short time, with an average of approximately 5 s. Conclusion: The algorithm we developed may greatly aid in accurate identification of muscularis propria invasion by imitating the thought process of the pathologist.