A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion. Issue 2 (14th March 2021)

• Record Type: Journal Article
• Authors: Khosravi, Pegah; Lysandrou, Maria; Eljalby, Mahmoud; Li, Qianzi; Kazemi, Ehsan; Zisimopoulos, Pantelis; Sigaras, Alexandros; Brendel, Matthew; Barnes, Josue; Ricketts, Camir; Meleshko, Dmitry; Yat, Andy; McClure, Timothy D.; Robinson, Brian D.; Sboner, Andrea; Elemento, Olivier; Chughtai, Bilal; Hajirasouliha, Iman
• Is Part Of: Journal of magnetic resonance imaging. Volume 54:Issue 2(2021)
• Page Start: 462
• Page End: 471
• Publication Date: 2021-03-14
• Subjects: artificial intelligence -- biopsy -- deep neural networks -- MRI images -- PI‐RADS -- prostate cancer; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27599
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. Purpose: To develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. Study Type: Retrospective. Population: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in‐house and 172 from external publicly available databases). Field Strength/Sequence: 1.5 to 3.0 Tesla, T2‐weighted image pulse sequences. Assessment: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high‐risk tumor from low‐risk tumor. Statistical Tests: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. Results: Our computational method (https://github.com/ih-lab/AI-biopsy ) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86–0.92]) and 0.78 (95% CI: [0.74–0.82]) to classify cancer vs. benign and high‐ vs. low‐risk of prostate disease, respectively. Data Conclusion: AI‐biopsy provided a data‐driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI‐biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag‐and‐drop web interface (https://ai-biopsy.eipm-research.org ) that allows radiologists to review AI‐assessed MR images in real time. Level of Evidence: 1 Technical Efficacy Stage: 2