Validation of artificial intelligence diagnostic support system for prostate needle biopsy

You have accessJournal of UrologyCME1 Apr 2023MP20-15 VALIDATION OF ARTIFICIAL INTELLIGENCE DIAGNOSTIC SUPPORT SYSTEM FOR PROSTATE NEEDLE BIOPSY Naoto Tokuyama, Akira Saito, Bin Shen, Ryu Muraoka, Takuya Ishida, Takeshi Kashima, Ryo Iseki, Kunihiko Yoshioka, Takashi Arakawa, Masahiko Kuroda, and Yoshio Ohno Naoto TokuyamaNaoto Tokuyama More articles by this author , Akira SaitoAkira Saito More articles by this author , Bin ShenBin Shen More articles by this author , Ryu MuraokaRyu Muraoka More articles by this author , Takuya IshidaTakuya Ishida More articles by this author , Takeshi KashimaTakeshi Kashima More articles by this author , Ryo IsekiRyo Iseki More articles by this author , Kunihiko YoshiokaKunihiko Yoshioka More articles by this author , Takashi ArakawaTakashi Arakawa More articles by this author , Masahiko KurodaMasahiko Kuroda More articles by this author , and Yoshio OhnoYoshio Ohno More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003245.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The incidence rate of prostate cancer has been increasing in Asia. The number of prostate needle biopsies is also increasing for early detection and treatment. However, prostate cancer has some borderline lesions which are difficult to diagnose, that may become the burden on pathologists. Although there were some studies of artificial intelligence (AI) to support prostate cancer pathological diagnosis worldwide, there were few large-scale validations using data from Asian patients. We have developed a simple diagnostic support system that can be used immediately in clinical practice based on Asian data. METHODS: A dataset of 12,230 hematoxylin-eosin stained prostate needle biopsy specimens was converted to digital images by whole slide image scanner, and annotated about cancer sites and Gleason patterns by pathologists. From these data sets, a cancer diagnosis and Gleason score discrimination system was created by deep learning. For the external validation, we obtained other 12,230 data sets from Shin-yurigaoka General Hospital, and confirmed the discrimination accuracy of cancer or non-cancer, and Gleason score based on International society of urological pathology (ISUP) grade. RESULTS: As a result of external validation, a high detection rate of 99.85% was achieved for the sensitivity and 69.62% for specificity by a needle unit. About 1 in 1000 needles was misdiagnosed. Most of these were cancerous ducts smaller than 1 mm or small ducts in the inflammatory background. Aggregating the results based on a patient, the cancer detection rate was 100%. On the other hand, the discrimination rate for ISUP grade was following, ISUP1:58.5%, ISUP2:31.1%, ISUP3:33.6%, ISUP4:25.9%, and ISUP5:81.9%. CONCLUSIONS: The results of the external validation showed that the discrimination of cancerous areas could be diagnosed with high sensitivity, which may prevent the misdiagnosis of cancerous areas and contribute to the reduction of the pathologist's working time. However, the Gleason score was not sufficiently discriminated due to inter-observer variability. Therefore, pathologist's visual inspection was considered essential for Gleason score evaluation. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e281 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Naoto Tokuyama More articles by this author Akira Saito More articles by this author Bin Shen More articles by this author Ryu Muraoka More articles by this author Takuya Ishida More articles by this author Takeshi Kashima More articles by this author Ryo Iseki More articles by this author Kunihiko Yoshioka More articles by this author Takashi Arakawa More articles by this author Masahiko Kuroda More articles by this author Yoshio Ohno More articles by this author Expand All Advertisement PDF downloadLoading ...