Loading...
Diagnostic accuracy of machine learning models on mammography in breast cancer classification: a meta-analysis
Hanis, Tengku Muhammad Musa, Kamarul Imran
Hanis, Tengku Muhammad
Musa, Kamarul Imran
Editors
Other contributors
Affiliation
Epub Date
Issue Date
2022-07-05
Submitted date
Alternative
Abstract
In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85–0.90), 0.83 (95% CI: 0.78–0.87) and 0.84 (95% CI: 0.81–0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks’ linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.
Citation
Hanis TM, Islam MA, Musa KI. Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis. Diagnostics. 2022; 12(7):1643. https://doi.org/10.3390/diagnostics12071643
Publisher
Journal
Research Unit
PubMed ID
35885548 (pubmed)
PubMed Central ID
Embedded videos
Additional Links
Type
Journal article
Language
en
Description
© 2022 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/diagnostics12071643
Series/Report no.
ISSN
2075-4418
EISSN
2075-4418
ISBN
ISMN
Gov't Doc #
Sponsors
This research was funded by the Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education, Malaysia (FRGS/1/2019/SKK03/USM/02/1).
Rights
Licence for published version: Creative Commons Attribution 4.0 International