ArticleDiagnostics (Basel, Switzerland)2020
Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 44 citations in OpenAlex.
- Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.Scientific reports · 2022Trial
- Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.European journal of radiology · 2026Article
- Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data.Medical physics · 2026Article
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.European radiology · 2026Article
- Assessing inter-observer variability in prostate and GTV segmentation on mpMRI: A comparison between radiation oncologists and AI-based method.Journal of applied clinical medical physics · 2026Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy.Diagnostics (Basel, Switzerland) · 2025Article
- A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing.Acta oncologica (Stockholm, Sweden) · 2024Article
- Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.Radiology · 2024Article
- Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study.Insights into imaging · 2023Article
- Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging.World journal of radiology · 2023Review
- Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.Current urology reports · 2023Review
- A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI.Insights into imaging · 2023Article
- Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.Korean journal of radiology · 2023Article
- Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?Insights into imaging · 2023Article
- Amide Proton Transfer-weighted MRI combined with serum prostate-specific antigen levels for differentiating malignant prostate lesions from benign prostate lesions: a retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses.IEEE open journal of engineering in medicine and biology · 2023Article
- Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment.Scientific reports · 2022Article
- Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges.European radiology experimental · 2022Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOpportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports-serving as the ground truth-was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI.
conclusionsThe results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.