SynthesisWorld journal of surgical oncology2025
Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.
Synthesis in World journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- An Integrated Statistical and Machine Learning Approach for Breast Cancer Classification Using Tumor Morphological Features.BioMed research international · 2026Article
- Comparative Evaluation of Breast Ultrasonography and Cytohistopathology in Diagnosing Breast Diseases.Cureus · 2025Article
- Male Breast Cancer: Epidemiology, Diagnosis, Molecular Mechanisms, Therapeutics, and Future Prospective.Oncology research · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance.
methodsA systematic search of PubMed, Embase, Cochrane Library, Scopus, and Web of Science identified 12 eligible studies (from 3,739 records) up to August 2024. Data were extracted to calculate sensitivity, specificity, and area under the curve (AUC) using bivariate models in R 4.4.1. Study quality was assessed via QUADAS-2.
resultsPooled sensitivity and specificity were 0.86 (95% CI: 0.82-0.90) and 0.82 (95% CI: 0.78-0.86), respectively, with an overall AUC of 0.90 (95% CI: 0.85-0.90). Diagnostic odds ratio (DOR) was 39.11 (95% CI: 25.04-53.17). Support vector machine (SVM) classifiers outperformed Naive Bayes, with higher sensitivity (0.88 vs. 0.86) and specificity (0.82 vs. 0.78). Heterogeneity was primarily attributed to MRI equipment (P = 0.037).
conclusionML-based MRI models demonstrate high diagnostic accuracy for breast cancer classification, with pooled sensitivity of 0.86 (95% CI: 0.82-0.90), specificity of 0.82 (95% CI: 0.78-0.86), and AUC of 0.90 (95% CI: 0.85-0.90). These results support their clinical utility as screening and diagnostic adjuncts, while highlighting the need for standardized protocols to improve generalizability.
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.