PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY (2020)

You have accessJournal of UrologyProstate Cancer: Markers II (PD52)1 Apr 2020PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY Patrick Leo*, Robin Elliott, Andrew Janowczyk, Nafiseh Janaki, Kaustav Bera, Rakesh Shiradkar, Ayah El-Fahmawi, Jessica Kim, Mohammed Shahait, Abhishek Shah, Hari Thulasidass, Ashutosh Tewari, Sanjay Gupta, Natalie Shih, Michael Feldman, Priti Lal, David Lee, and Anant Madabhushi Patrick Leo*Patrick Leo* More articles by this author , Robin ElliottRobin Elliott More articles by this author , Andrew JanowczykAndrew Janowczyk More articles by this author , Nafiseh JanakiNafiseh Janaki More articles by this author , Kaustav BeraKaustav Bera More articles by this author , Rakesh ShiradkarRakesh Shiradkar More articles by this author , Ayah El-FahmawiAyah El-Fahmawi More articles by this author , Jessica KimJessica Kim More articles by this author , Mohammed ShahaitMohammed Shahait More articles by this author , Abhishek ShahAbhishek Shah More articles by this author , Hari ThulasidassHari Thulasidass More articles by this author , Ashutosh TewariAshutosh Tewari More articles by this author , Sanjay GuptaSanjay Gupta More articles by this author , Natalie ShihNatalie Shih More articles by this author , Michael FeldmanMichael Feldman More articles by this author , Priti LalPriti Lal More articles by this author , David LeeDavid Lee More articles by this author , and Anant MadabhushiAnant Madabhushi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000954.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: It is currently clinically challenging to stratify patients’ risk of biochemical recurrence (BCR) of localized prostate cancer following radical prostatectomy (RP). Decipher, a 22-gene genomic classifier, is currently a part of NCCN guidelines to determine risk of metastasis and BCR following RP with improved accuracy beyond existing clinicopathologic factor based nomograms. Recently, computer extracted quantitative histomorphometric (QH) analysis of hematoxylin and eosin (H&E) images alone has shown increasing value in predicting risk of BCR following RP. In this work, we sought to compare the prognostic ability of QH against Decipher in BCR prognosis post-RP. As compared to Decipher, QH is non tissue-destructive, less time consuming, and cheaper. METHODS: A single diagnostic slide was collected from N=388 patients from three institutions and compose. Patients were split into training (N=215) and validation (N=173) sets. One institution was split across the training and testing set according to availability of Decipher results. Each slide was annotated for a single large, representative cancer region, from which 26 texture features were extracted. 41 training set patients were used to train a deep learning model for lumen segmentation, which was applied to all the cancerous regions. From the lumen segmentations, 216 features of lumen arrangement, shape, and disorder were extracted. Training set patients were used to fit an elastic-net penalized Cox regression model with these 242 features, which was applied to all patients. The Cox model output a risk score for each patient. This process was termed “Histotyping”. The concordance index (c-index) of Histotyping and Decipher in BCR-free survival were compared. RESULTS: The nine features selected by the Cox model included seven measures of average lumen shape and variation of lumen shape, one feature of lumen arrangement, and one feature of image texture. A greater variation across the tumor in lumen shape and density and a less smooth image texture were associated with increased BCR risk. Histotyping performed comparably to Decipher (c-index 0.66 vs. 0.70) in BCR prognosis. CONCLUSIONS: Computerized visual analysis of a routinely acquired H&E slides was found to perform comparably to Decipher in BCR prognosis. A limitation of this work is that Decipher is optimized for metastasis prognosis, though metastasis outcome information was not available for these patients. Source of Funding: National Cancer Institute, Department of Defense, National Science Foundation © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1089-e1090 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Patrick Leo* More articles by this author Robin Elliott More articles by this author Andrew Janowczyk More articles by this author Nafiseh Janaki More articles by this author Kaustav Bera More articles by this author Rakesh Shiradkar More articles by this author Ayah El-Fahmawi More articles by this author Jessica Kim More articles by this author Mohammed Shahait More articles by this author Abhishek Shah More articles by this author Hari Thulasidass More articles by this author Ashutosh Tewari More articles by this author Sanjay Gupta More articles by this author Natalie Shih More articles by this author Michael Feldman More articles by this author Priti Lal More articles by this author David Lee More articles by this author Anant Madabhushi More articles by this author Expand All Advertisement PDF downloadLoading ...