Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.
메타분석
1/5 보강
[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
- 연구 설계 systematic review
APA
Dondi F, Gatta R, et al. (2024). Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.. Reviews in endocrine & metabolic disorders, 25(1), 175-186. https://doi.org/10.1007/s11154-023-09822-4
MLA
Dondi F, et al.. "Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.." Reviews in endocrine & metabolic disorders, vol. 25, no. 1, 2024, pp. 175-186.
PMID
37434097 ↗
Abstract 한글 요약
[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 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.
[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 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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