ReviewDiagnostic and interventional radiology (Ankara, Turkey)2019
Artificial intelligence at the intersection of pathology and radiology in prostate cancer.
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 97 citations in OpenAlex.
- Review
- Impact of AI-Generated ADC Maps on Computer-Aided Diagnosis of Prostate Cancer: A Feasibility Study.Academic radiology · 2025Article
- Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy.Scientific reports · 2025Article
- Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.Scientific reports · 2025Article
- The next frontier in antibody-drug conjugates: challenges and opportunities in cancer and autoimmune therapy.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance.Frontiers in urology · 2025Article
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools.Diagnostics (Basel, Switzerland) · 2024Review
- Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis.Journal of bone oncology · 2024Article
- Review
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.F1000Research · 2024Article
- Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI.BMC medical imaging · 2023Article
- A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer.Life (Basel, Switzerland) · 2023Review
- Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels.Journal of clinical medicine · 2023Article
- Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.Magma (New York, N.Y.) · 2023Article
- Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma.The American journal of pathology · 2023Article
- A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.Microbiology spectrum · 2022Article
- Tasks for artificial intelligence in prostate MRI.European radiology experimental · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
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.