Pd60-05 Automated Detection of Prostate Cancer on Multiparametric Mri Using Deep Neural Networks Trained on Spatial Coordinates and Pathology of Biopsy Cores
Year: 2019
Type: article
Abstract: You have accessJournal of UrologyProstate Cancer: Detection & Screening VII (PD60)1 Apr 2019PD60-05 AUTOMATED DETECTION OF PROSTATE CANCER ON MULTIPARAMETRIC MRI USING DEEP NEURAL NETWORKS TRAINED ON SPATIAL COORDINATES AND PATHOLOGY OF BIOPSY CORES Leo Chen*, Nicholas Bien, Richard Fan, Robin Cheong, Pranav Rajpurkar, Alan Thong, Nancy Wang, Sarir Ahmadi, Mirabela Rusu, James Brooks, Andrew Ng, and Geoffrey Sonn Leo Chen*Leo Chen* More articles by this author , Nicholas BienNicholas Bien More articles by this author , Richard FanRichard Fan More articles by this author , Robin CheongRobin Cheong More articles by this author , Pranav RajpurkarPranav Rajpurkar More articles by this author , Alan ThongAlan Thong More articles by this author , Nancy WangNancy Wang More articles by this author , Sarir AhmadiSarir Ahmadi More articles by this author , Mirabela RusuMirabela Rusu More articles by this author , James BrooksJames Brooks More articles by this author , Andrew NgAndrew Ng More articles by this author , and Geoffrey SonnGeoffrey Sonn More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557214.92207.3fAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The role of multiparametric MRI in clinical care is rapidly expanding due to its ability to improve prostate cancer detection. However, MRI interpretation suffers from a false negative rate of around 5-7% and high interobserver variability even among experts, thereby limiting its predictive value. Advances in machine learning and artificial intelligence have the potential to improve and standardize prostate cancer detection on MRI, though studies have relied on radiologist interpretations such as PIRADS scores of segmented lesions as ground truth. We sought to improve over existing methods by directly training on the 3D location and pathology of biopsy cores. METHODS: MR-ultrasound fusion biopsies were performed at a single institution using a robotic fusion biopsy device (Artemis, Eigen) for patients that had multiparametric prostate MRIs. Patients underwent both targeted and standard template biopsies. Core level pathology was prospectively collected into a database. The spatial coordinates of both targeted and standard template cores were calculated and plotted onto the MR images. A weakly supervised convolutional neural network model was trained to predict cancer on MR images, using the spatial geometry and pathology of the biopsy core tracts as ground truth. Implementation was done using the Python programming language. RESULTS: Over 10,000 MRI-US fusion biopsy cores were collected from over 600 patients in 2015-2018, yielding over 40,000 data points. A preliminary binary classification model based on T2 sequences alone correctly predicted benign versus cancerous cores with an AUROC of 0.78. There is ongoing work on incorporating DWI and ADC sequences to improve the accuracy of the deep learning model. CONCLUSIONS: We present a deep neural network model to predict prostate cancer on MRI that is trained on spatial coordinates and pathology of biopsy cores as ground truth. To our knowledge, this dataset is the largest that has been reported. Our model is blind to radiologist interpretation and lesion segmentation: it thereby aims to eliminate the interobserver variability of radiologist interpretations and improve the detection of cancer in patients with false negative MRIs. Ultimately, our goal is the automated generation of probabilistic heat maps of clinically significant prostate cancer based on automated processing of MRIs, improving the speed and accuracy of detecting clinically significant cancer, while reducing overdetection of clinically insignificant cancer. Source of Funding: None Stanford, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1098-e1098 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Leo Chen* More articles by this author Nicholas Bien More articles by this author Richard Fan More articles by this author Robin Cheong More articles by this author Pranav Rajpurkar More articles by this author Alan Thong More articles by this author Nancy Wang More articles by this author Sarir Ahmadi More articles by this author Mirabela Rusu More articles by this author James Brooks More articles by this author Andrew Ng More articles by this author Geoffrey Sonn More articles by this author Expand All Advertisement PDF downloadLoading ...
Source: The Journal of Urology
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2941599536