PD38-07 STIMULATED RAMAN HISTOLOGY INTERPRETATION BY ARTIFICIAL INTELLIGENCE CAN PROVIDE REAL-TIME PATHOLOGIC FEEDBACK OF PROSTATE BIOPSIES

Abstract

You have accessJournal of UrologyCME1 Apr 2023PD38-07 STIMULATED RAMAN HISTOLOGY INTERPRETATION BY ARTIFICIAL INTELLIGENCE CAN PROVIDE REAL-TIME PATHOLOGIC FEEDBACK OF PROSTATE BIOPSIES Miles Mannas, Alec Mitchell, Fang-Ming Deng, Adrian Ion-Margineanu, Derek Jones, Deepthi Hoskoppal, Jonothan Melamed, Steve Pastore, Christian Freudiger, Daniel Orringer, and Samir Taneja Miles MannasMiles Mannas More articles by this author , Alec MitchellAlec Mitchell More articles by this author , Fang-Ming DengFang-Ming Deng More articles by this author , Adrian Ion-MargineanuAdrian Ion-Margineanu More articles by this author , Derek JonesDerek Jones More articles by this author , Deepthi HoskoppalDeepthi Hoskoppal More articles by this author , Jonothan MelamedJonothan Melamed More articles by this author , Steve PastoreSteve Pastore More articles by this author , Christian FreudigerChristian Freudiger More articles by this author , Daniel OrringerDaniel Orringer More articles by this author , and Samir TanejaSamir Taneja More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003336.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There is a delay between prostate biopsy and pathologic diagnosis; this delay has limited the use pathologic feedback during the diagnosis and treatment of prostate cancer (PCa). Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue. We hypothesized that an artificial intelligence convolutional neural network (CNN) could rapidly interpret prostate biopsy SRH. METHODS: Prostate biopsies were prospectively taken ex-vivo from prostatectomy specimens and scanned in a SRH microscope at 20 microns depth using two Raman shifts: 2845cm-1 and 2930cm-1, to create SRH images. The cores were then processed and H&E-stained as per normal pathologic protocols and used for ground truth pathologic assessment. 303 ex-vivo prostate biopsies taken from 100 radical prostatectomy specimens were used to train the SRH Inception-ResNet-v2 CNN. With a two-sided alpha level of 5%, it was calculated 32 biopsies would provide 90% power to detect a difference in concordance kappa when testing the CNN. Concordance and diagnostic accuracy of the CNN were tested on training and validation patches, and the 32 leave-one out prostate biopsies from 23 radical prostatectomy specimens. RESULTS: The CNN showed a 99.6% weighted accuracy on the training patches and 98.6% when tested on the validation set. The CNN also showed very good kappa concordance (k=0.925, p<0.001) when classifying the 32 prostate biopsies as benign or malignant, giving a diagnostic accuracy of 96.9%, with a scan time of 2-2.75 minutes, Table 1. Additionally, if a region containing PCa was first scanned with SRH, the AI could identify PCa in ∼1 minute. CONCLUSIONS: Artificial intelligence applied to SRH can rapidly classify fresh, unprocessed, unstained prostate biopsies as benign or malignant. Source of Funding: NIH UL1TR001145 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e995 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Miles Mannas More articles by this author Alec Mitchell More articles by this author Fang-Ming Deng More articles by this author Adrian Ion-Margineanu More articles by this author Derek Jones More articles by this author Deepthi Hoskoppal More articles by this author Jonothan Melamed More articles by this author Steve Pastore More articles by this author Christian Freudiger More articles by this author Daniel Orringer More articles by this author Samir Taneja More articles by this author Expand All Advertisement PDF downloadLoading ...