ArticleAbdominal radiology (New York)2022
Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled it
- RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer.International journal of cancer · 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
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- Differential detection rates of clinically significant prostate cancer in transition zone versus peripheral zone lesions: Implications for transperineal magnetic resonance imaging-targeted biopsy strategy.Prostate international · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy.Frontiers in oncology · 2026Review
- Review
- Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses.Research in diagnostic and interventional imaging · 2025Article
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- IR-GPT: AI Foundation Models to Optimize Interventional Radiology.Cardiovascular and interventional radiology · 2025Review
- Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.American journal of clinical and experimental urology · 2025Review
- The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.Scientific reports · 2024Article
- Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning.Scientific reports · 2024Article
- Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.Journal of imaging · 2024Article
- Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.IEEE transactions on bio-medical engineering · 2024Article
- New advances of the androgen receptor in prostate cancer: report from the 1st International Androgen Receptor Symposium.Journal of translational medicine · 2024Article
- Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.Prostate cancer · 2024Review
- Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.Biomedicines · 2023Article
- What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?Military Medical Research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo present fully automated DL-based prostate cancer detection system for prostate MRI.
methodsMRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.
results525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively.
conclusionWe developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.
Indexed as
Identifiers
What OpenQuestion holds
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.