An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study
Amogh Hiremath,Rakesh Shiradkar,Pingfu Fu,Amr Mahran,Ardeshir R. Rastinehad,Ashutosh K. Tewari,Sree Harsha Tirumani,Andrei S. Purysko,Lee Ponsky,Anant Madabhushi,Anant Madabhushi +10 more
- Vol. 3, Iss: 7
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TLDR
In this article, an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI is presented.Abstract:
BACKGROUND Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging-Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI. METHODS In this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong's test. We used log-rank tests and Kaplan-Meier curves to analyse survival. FINDINGS We investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0·81 (95% CI 0·76-0·85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0·74 [95% CI 0·69-0·80], p=0·0005) and PIN (0·76 [0·71-0·81], p<0·0001) nomograms. In the subset of patients who had undergone radical prostatectomy (n=81), the ClaD clinical nomogram resulted in a significant separation in Kaplan-Meier survival curves between patients with and without biochemical recurrence (HR 5·92 [2·34-15·00], p=0·044), whereas the DIN (1·22 [0·54-2·79], p=0·65) and PIN nomograms did not (1·30 [0·62-2·71], p=0·51). INTERPRETATION Risk stratification of patients with prostate cancer using the integrated ClaD nomogram could help to identify patients with prostate cancer who are at low risk, very low risk, and favourable intermediate risk, who might be candidates for active surveillance, and could also help to identify patients with lethal prostate cancer who might benefit from adjuvant therapy. FUNDING National Cancer Institute of the US National Institutes of Health, National Institute for Biomedical Imaging and Bioengineering, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, US Department of Defense, US National Institute of Diabetes and Digestive and Kidney Diseases, The Ohio Third Frontier Technology Validation Fund, Case Western Reserve University, Dana Foundation, and Clinical and Translational Science Collaborative.read more
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Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review
Nikita Sushentsev,Nádia Moreira da Silva,Michael Yeung,Tristan Barrett,Evis Sala,Michael S. Roberts,Leonardo Rundo +6 more
TL;DR: In this article , the authors systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automatic traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa), and benign conditions.
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Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the <i>AJR</i> Special Series on AI Applications
TL;DR: Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI ApplicationsBaris Turkbey, MD1 and Masoom A. Haider, MD2,3,4Audio Available | Share
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Artificial intelligence in multiparametric magnetic resonance imaging: A review
TL;DR: In this article , the authors provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in multiparametric magnetic resonance imaging.
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Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review
Nithesh Naik,Theodoros Tokas,Dasharathraj K Shetty,B M Zeeshan Hameed,Sarthak Shastri,Milap Shah,Sufyan Ibrahim,Bhavan Prasad Rai,Piotr Chlosta,Bhaskar K. Somani +9 more
TL;DR: This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment, and presents a systematic outline and summary of these deep learning models and technologies used for prostate cancer management.
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Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? - A systematic review.
Olivier Rouvière,Tristan Jaouen,P. Baseilhac,Mohammed Lamine Benomar,Raphael Escande,Sebastien Crouzet,Rémi Souchon +6 more
TL;DR: In this article , the authors performed a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI).
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