Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.

IF 6.9 2区医学 Q1 ENDOCRINOLOGY & METABOLISM

Reviews in Endocrine & Metabolic Disorders Pub Date : 2024-02-01 Epub Date: 2023-07-12 DOI:10.1007/s11154-023-09822-4

Francesco Dondi, Roberto Gatta, Giorgio Treglia, Arnoldo Piccardo, Domenico Albano, Luca Camoni, Elisa Gatta, Maria Cavadini, Carlo Cappelli, Francesco Bertagna

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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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放射组学和机器学习在核医学中对甲状腺疾病的应用：系统综述。

背景：近年来，越来越多的证据表明，放射组学和机器学习（ML）应用于不同的核医学成像模式，在评估甲状腺疾病方面发挥了作用。因此，本系统性综述旨在分析这些技术在这种情况下的诊断性能：方法：对 PubMed/MEDLINE、Scopus 和 Web of Science 数据库进行了广泛的文献检索，以找到有关放射组学或 ML 在核医学成像中评估不同甲状腺疾病的作用的已发表文章：结果：17 项研究被纳入系统综述。结果：17 项研究被纳入系统综述，放射组学和 ML 被应用于 18 F-FDG PET 评估甲状腺偶发瘤、评估细胞学不确定的甲状腺结节、评估甲状腺癌以及使用核医学技术对甲状腺疾病进行分类：尽管放射组学和ML的一些固有局限性可能会影响本综述的结果，但这些技术在评估甲状腺疾病方面似乎大有可为。要将放射组学和ML方法应用于临床，还需要在多中心研究中对初步结果进行验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Reviews in Endocrine & Metabolic Disorders 医学-内分泌学与代谢

CiteScore

14.70

自引率

1.20%

发文量

审稿时长

>12 weeks

期刊介绍： Reviews in Endocrine and Metabolic Disorders is an international journal dedicated to the field of endocrinology and metabolism. It aims to provide the latest advancements in this rapidly advancing field to students, clinicians, and researchers. Unlike other journals, each quarterly issue of this review journal focuses on a specific topic and features ten to twelve articles written by world leaders in the field. These articles provide brief overviews of the latest developments, offering insights into both the basic aspects of the disease and its clinical implications. This format allows individuals in all areas of the field, including students, academic clinicians, and practicing clinicians, to understand the disease process and apply their knowledge to their specific areas of interest. The journal also includes selected readings and other essential references to encourage further in-depth exploration of specific topics.