Diagnostic performance of machine learning and deep learning algorithms for thyroid cancer metastasis: a systematic review and meta-analysis
ML and DL algorithms demonstrate favorable diagnostic performance in identifying metastasis in thyroid cancer and may serve as supportive tools in clinical decision-making. Their consistent results across different metastasis types and technical settings highlight their potential to complement existing diagnostic approaches. These findings encourage further exploration and refinement of AI-based methods for integration into routine oncologic practice.
Authors
- Saeid Anvari (ORCID: https://orcid.org/0000-0002-5905-8122)
- Maryam Jafari (ORCID: https://orcid.org/0000-0003-4875-6133)
- Mohaya Farzin (ORCID: https://orcid.org/0000-0003-4739-2454)
- Amirhossein Larijani (ORCID: https://orcid.org/0000-0002-2282-2924)
- Hossein Hemmati (ORCID: https://orcid.org/0000-0002-9393-5782)
- Ervin Zadgari (ORCID: https://orcid.org/0009-0007-5939-6782)
- Mohammad Amouzadeh Lichahi (ORCID: https://orcid.org/0009-0006-8767-0446)
- Seyedeh Mohadeseh Mosavi Mirkalaie
Institutions
- Guilan University of Medical Sciences (IR)
- Razi Hospital (IR)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1186/s12911-025-03307-x
- Primary Topic
- Thyroid Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00