ReviewNature reviews. Urology2013
Artificial neural networks and prostate cancer--tools for diagnosis and management.
Review in Nature reviews. Urology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Prediction models for prostate cancer to be used in the primary care setting: a systematic review.BMJ open · 2020Pooled it
- An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer.Insights into imaging · 2025Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction.iScience · 2024Article
- Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.Cancer medicine · 2024Article
- Biological characteristics of molecular subtypes of ulcerative colitis characterized by ferroptosis and neutrophil infiltration.Scientific reports · 2024Article
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol.Systematic reviews · 2023Article
- External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection.World journal of urology · 2023Article
- Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network.Journal of personalized medicine · 2023Article
- Artificial Neural Network Modeling to Predict Neonatal Metabolic Bone Disease in the Prenatal and Postnatal Periods.JAMA network open · 2023Article
- Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models.PloS one · 2023Article
- Development, Validation and Comparison of Artificial Neural Network and Logistic Regression Models Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.Allergy, asthma & immunology research · 2023Article
- Comorbidity and osteoporotic fracture: approach through predictive modeling techniques using the OSTEOMED registry.Aging clinical and experimental research · 2022Observational
- Novel biomarkers predict prognosis and drug-induced neuroendocrine differentiation in patients with prostate cancer.Frontiers in endocrinology · 2022Article
- Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer.Frontiers in oncology · 2022Article
- MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial intelligence in brachytherapy: a summary of recent developments.The British journal of radiology · 2021Review
- Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.BMC urology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.
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.