ArticleJournal of medical imaging (Bellingham, Wash.)2018
Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data.Medical physics · 2026Article
- IR-GPT: AI Foundation Models to Optimize Interventional Radiology.Cardiovascular and interventional radiology · 2025Review
- ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases.Medical image analysis · 2025Article
- RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.Computers in biology and medicine · 2024Article
- The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.Current oncology reports · 2023Review
- Joint Cancer Segmentation and PI-RADS Classification on Multiparametric MRI Using MiniSegCaps Network.Diagnostics (Basel, Switzerland) · 2023Article
- Bridging the gap between prostate radiology and pathology through machine learning.Medical physics · 2022Article
- Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate.Journal of magnetic resonance imaging : JMRI · 2022Article
- Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning.Cancers · 2022Article
- A New Framework for Precise Identification of Prostatic Adenocarcinoma.Sensors (Basel, Switzerland) · 2022Article
- Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.Medical image analysis · 2022Article
- Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine.Journal of personalized medicine · 2021Review
- Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2021Article
- Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.Medical physics · 2021Article
- Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.Medical image analysis · 2021Article
- Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.European radiology · 2020Article
- Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.Medical physics · 2020Article
- Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.Cancers · 2020Review
- Artificial intelligence at the intersection of pathology and radiology in prostate cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2019Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high
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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.