The diagnostic value of machine learning for the classification of malignant bone tumor - a systematic evaluation and meta-analysis

Aug 3, 2026

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Machine learning supports early differential diagnosis of malignant bone tumors, yet reported performance varies across studies. This systematic review and meta-analysis evaluates the diagnostic value of machine learning for malignant bone tumor classification using literature searches in PubMed, Embase, Cochrane Library, and Web of Science through October 31, 2022, with QUADAS-2 risk-of-bias assessment. A bivariate mixed-effects model synthesizes sensitivity and specificity with subgroup analyses by method and modeling approach.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Methods

Results

Subgroup analyses

Conclusion

Systematic review registration

TYPE Systematic Review PUBLISHED 07 September 2023 DOI 10.3389/fonc.2023.1207175 OPEN ACCESS EDITED BY Cecilia Ana Suarez, National Scientiﬁc and Technical Research Council (CONICET), Argentina REVIEWED BY Shanu Sharma, ABES Engineering College, India Yong Lu, Shanghai Jiao Tong University, China *CORRESPONDENCE Bo Dong [297065203@qq.com](297065203@qq.com) RECEIVED 17 April 2023 ACCEPTED 23 August 2023 PUBLISHED 07 September 2023 CITATION Li Y, Dong B and Yuan P (2023) The diagnostic value of machine learning for the classiﬁcation of malignant bone tumor: a systematic evaluation and meta-analysis. Front. Oncol. 13:1207175 . doi: 10.3389/fonc.2023.1207175 COPYRIGHT © 2023 Li, Dong and Yuan. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The diagnostic value of machine learning for the classiﬁcation of malignant bone tumor: a systematic evaluation and meta-analysis Yue Li, Bo Dong* and Puwei Yuan Department of Orthopedics, Xi’an Honghui Hospital, Xi’an Jiaotong University, Xi’an Shaanxi, China Background: Malignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classiﬁcation are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors. Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2 . A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches. Results: The inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and speciﬁcity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0 .92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and speciﬁcity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0 . 86] and 0 .79 [95% CI: 0 .70,0 . 86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and speciﬁcity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0 . 51,0 .98] and 0 . 87 [95% CI: 0 . 69,0 .96] in the validation set. Conclusion: Machine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efﬁciency and clinical application prospects. Systematic review registration: [https://www.crd.york.ac.uk/prospero/](https://www.crd.york.ac.uk/prospero/), identiﬁer CRD42023387057 . KEYWORDS machine learning, meta-analysis, malignant bone tumor, diagnosis, systematic evaluation Frontiers in Oncology 01 [frontiersin.org](frontiersin.org) Introduction Malignant bone tumors are diseases caused by the growth and spread of malignant tumor cells in bone tissue and the destruction of bone structure. There are various bone tumor types, s

FAQs

What is the goal of this systematic review and meta-analysis?

To explore the diagnostic value of machine learning for malignant bone tumor classification and assess its overall performance and variation across studies.

Which databases were searched and up to what date?

PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant literature up to October 31, 2022.

What diagnostic performance did machine learning show?

In the training set, sensitivity and specificity were 0.87 and 0.91; in the validation set, they were 0.83 and 0.87.

Aug 3, 2026

Upload by Patrick