ReviewAcademic radiology2016
Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.
Review in Academic radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Developmental trends and knowledge frameworks in the application of radiomics in prostate cancer: a bibliometric analysis from 2000 to 2024.Discover oncology · 2024Article
- Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.Frontiers in oncology · 2023Review
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
- The role of MRI in prostate cancer: current and future directions.Magma (New York, N.Y.) · 2022Review
- Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.European radiology · 2022Article
- Article
- The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.Diagnostics (Basel, Switzerland) · 2021Article
- Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks.Journal of digital imaging · 2021Article
- Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.Diagnostics (Basel, Switzerland) · 2021Article
- Precise Identification of Prostate Cancer from DWI Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Radiomics and Prostate MRI: Current Role and Future Applications.Journal of imaging · 2021Review
- Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.Contrast media & molecular imaging · 2021Article
- Radiomics Improves Cancer Screening and Early Detection.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Review
- Article
- [Artificial intelligence and radiomics in MRI-based prostate diagnostics].Der Radiologe · 2020Review
- Expression and significance of PTEN and Claudin-3 in prostate cancer.Oncology letters · 2019Article
- Artificial intelligence at the intersection of pathology and radiology in prostate cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2019Review
- Artificial intelligence in cancer imaging: Clinical challenges and applications.CA: a cancer journal for clinicians · 2019Review
- A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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.