Dondi F, Gatta R, Treglia G, Piccardo A, Albano D, Camoni L, Gatta E, Cavadini M, Cappelli C, Bertagna F. Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.
Rev Endocr Metab Disord 2024;
25:175-186. [PMID:
37434097 PMCID:
PMC10808150 DOI:
10.1007/s11154-023-09822-4]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND
In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting.
METHODS
A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases.
RESULTS
Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques.
CONCLUSION
Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.
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