SynthesisRadiology. Artificial intelligence2024
Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.
Synthesis in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- MRI-derived prostate volume assessment: manual segmentation versus two-axis ellipsoid approximation and its implications for PSA density.European radiology · 2026Article
- Reference values for PSA, PSA density, and MRI prostate volume in healthy German men.European radiology · 2026Article
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance.Bioengineering (Basel, Switzerland) · 2026Article
- AI-based prostate volume estimation from multi-planar MRI under variable acquisition protocols.European journal of radiology open · 2026Article
- Preoperative dual-ROI mpMRI radiomics integrating intratumoral and periprostatic adipose tissue features with clinical factors for predicting positive surgical margins after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Fine-Grained Perception for Fundus and Prostate Medical Image Segmentation.Sensors (Basel, Switzerland) · 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
- mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.Cancers · 2026Article
- Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.Radiological physics and technology · 2026Article
- Radiomics-Based Characterization of Aggressive Prostate Cancer Variants: Diagnostic Challenges and Opportunities.Cancers · 2026Review
- ProBot: Robot-Ultrasound Probe for Transrectal and Transperineal Prostate Biopsy.IEEE transactions on bio-medical engineering · 2026Article
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Conditional diffusion model for high-accuracy brain tumor segmentation in MRI images.Scientific reports · 2025Article
- AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025Review
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- A modified deep learning approach for seminal vesicle region localization in prostate MRI.Scientific reports · 2025Article
- Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study.Abdominal radiology (New York) · 2025Article
- An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer.Nature cancer · 2025Article
- Changes in prostate volume during prostate SBRT delivered on an MR-Linac and correlation with acute toxicity.Clinical and translational radiation oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings.
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.