SynthesisCancer medicine2025
Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.
Synthesis in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Healthcare Utilization and Inequalities in Breast Cancer Early Detection Behaviors Among Women in Türkiye: A Nationally Representative Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- Outcomes That Matter in Gastrointestinal Cancer Care: A Focus Group Study of Patient-Centred Outcomes Identified by Patients and Care Partners.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers.Current oncology reports · 2026Review
- Article
- Three-Dimensional Bronchovascular Modelling in Sublobar Pulmonary Resection: A Tool for Personalised Thoracic Surgery.Journal of personalized medicine · 2026Review
- Interventions to Improve Timely Detection and Diagnosis of Cancer Among the Adult Population: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- AI-driven integration of genomic and exposome data for cancer risk: the combined risk score (CRS).Human genomics · 2026Review
- Prior curative-intent treatment strategy affects progression outcomes of first-line immunotherapy in metastatic non-small cell lung cancer: A retrospective cohort study.Experimental and therapeutic medicine · 2026Article
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Interpretable machine learning for prediction of leptomeningeal metastasis in lung adenocarcinoma: a multi-centre retrospective study via "Prompt" model.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
introductionPredictive models could support clinicians in identifying patients who may benefit from cancer investigations. We aimed to examine published evidence on machine learning models (ML) developed to estimate cancer risk based on symptoms and other patient characteristics.
methodsUsing MEDLINE, Scopus, and EMBASE, we performed a systematic review of studies published in 2014-2024, which included data on signs/symptoms for cancer risk prediction. We used the QUADAS-AI tools to assess study quality. We performed a quantitative synthesis of diagnostic performance, including accuracy, sensitivity, specificity, area under the curve (AUC). Adherence to TRIPOD guidelines was assessed.
resultsAmong the 5646 initially identified articles, 34 met inclusion criteria. Included studies most frequently examined lung (n = 9 studies), mesothelioma (n = 7), and gastrointestinal cancers (n = 4) and used hospital electronic health records (n = 8) or publicly available online datasets (n = 13). In addition to signs/symptoms (n = 34), most models included sociodemographic characteristics (n = 27) and lifestyle factors (n = 20). In 70% of studies, internal validation was performed. ML models demonstrated variable performance, with AUC values ranging from 0.60 to 1 during validation. Random Forest, Support Vector Machine, Decision Tree, and Multilayer Perceptron showed the best predictive performance. Most of the studies (94.1%) had a high risk of bias for the index test.
conclusionML models have been reported to demonstrate potential in managing complex data for cancer risk prediction. However, the current evidence is heterogeneous and frequently limited by bias and incomplete reporting. Further validation and thorough assessments of real-world performance are necessary before these models can be considered reliable for clinical use.
trial registrationInternational Prospective Register of Systematic Reviews (PROSPERO) registration number: CRD42024548088.
Indexed as
Identifiers
What Socratic 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 Socratic graph.