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Reviews in Endocrine and Metabolic Disorders

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Open Access 12-07-2023 | Positron Emission Tomography

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

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

Published in: Reviews in Endocrine and Metabolic Disorders | Issue 1/2024

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.

Dias Lopes NM, Mendonça Lens HH, Armani A, Marinello PC, Cecchini AL. Thyroid cancer and thyroid autoimmune disease: A review of molecular aspects and clinical outcomes. Pathol Res Pract. 2020 Sep;216(9):153098. doi: https://doi.org/10.1016/j.prp.2020.153098. Epub 2020 Jul 4. PMID: 32825964.

Kravets I, Hyperthyroidism. Diagnosis and Treatment. Am Fam Physician. 2016 Mar 1;93(5):363 – 70. PMID: 26926973.

Russell MD, Orloff LA. Ultrasonography of the thyroid, parathyroids, and beyond. HNO. 2022 May;70(5):333–44. https://doi.org/10.1007/s00106-022-01162-0. Epub 2022 Apr 1. PMID: 35364686; PMCID: PMC8974803.

Giovanella L, Avram AM, Iakovou I, Kwak J, Lawson SA, Lulaj E, Luster M, Piccardo A, Schmidt M, Tulchinsky M, Verburg FA, Wolin E. EANM practice guideline/SNMMI procedure standard for RAIU and thyroid scintigraphy. Eur J Nucl Med Mol Imaging 2019 Nov;46(12):2514–25. doi: https://doi.org/10.1007/s00259-019-04472-8. Epub 2019 Aug 7. PMID: 31392371.

Cistaro A, Quartuccio N, Garganese MC, Villani MF, Altini C, Pizzoferro M, Piccardo A, Cabria M, Massollo M, Maghnie M, Campennì A, Siracusa M, Baldari S, Panareo S, Urso L, Bartolomei M, De Palma D, Grossi A, Mazzoletti A, Dondi F, Bertagna F, Giubbini R, Albano D. Prognostic factors in children and adolescents with differentiated thyroid carcinoma treated with total thyroidectomy and RAI: a real-life multicentric study. Eur J Nucl Med Mol Imaging. 2022 Mar;49(4):1374–85. https://doi.org/10.1007/s00259-021-05586-8. Epub 2021 Oct 19. PMID: 34664092; PMCID: PMC8921094.

Albano D, Dondi F, Zilioli V, Panarotto MB, Galani A, Cappelli C, Bertagna F, Giubbini R, Casella C. The role of Hashimoto thyroiditis in predicting radioiodine ablation efficacy and prognosis of low to intermediate risk differentiated thyroid cancer. Ann Nucl Med. 2021 Oct;35(10):1089–99. https://doi.org/10.1007/s12149-021-01644-1. Epub 2021 Jun 21. PMID: 34152569; PMCID: PMC8408084.

Albano D, Tulchinsky M, Dondi F, Mazzoletti A, Lombardi D, Bertagna F, Giubbini R. Thyroglobulin doubling time offers a better threshold than thyroglobulin level for selecting optimal candidates to undergo localizing [18F]FDG PET/CT in non-iodine avid differentiated thyroid carcinoma. Eur J Nucl Med Mol Imaging. 2021 Feb;48(2):461–8. Epub 2020 Aug 13. PMID: 32794104; PMCID: PMC7835169.

Albano D, Tulchinsky M, Dondi F, Mazzoletti A, Bertagna F, Giubbini R. The role of Tg kinetics in predicting 2-[18F]-FDG PET/CT results and overall survival in patients affected by differentiated thyroid carcinoma with detectable Tg and negative 131I-scan. Endocrine. 2021 Nov;74(2):332–339. doi: 10.1007/s12020-021-02755-5. Epub 2021 May 20. PMID: 34014437; PMCID: PMC8497300.

Albano D, Dondi F, Mazzoletti A, Bellini P, Rodella C, Bertagna F. Prognostic role of 2-[18F]FDG PET/CT metabolic volume parameters in patients affected by differentiated thyroid carcinoma with high Thyroglobulin Level, negative 131I WBS and positive 2-[18F]-FDG PET/CT. Diagnostics (Basel). 2021 Nov 25;11(12):2189. doi: https://doi.org/10.3390/diagnostics11122189. PMID: 34943426; PMCID: PMC8700137.

10.

Rizzo A, Racca M, Dall’Armellina S, Delgado Bolton RC, Albano D, Dondi F, Bertagna F, Annunziata S, Treglia G. Potential Role of PSMA-Targeted PET in Thyroid Malignant Disease: A Systematic Review. Diagnostics (Basel). 2023 Feb 3;13(3):564. doi: https://doi.org/10.3390/diagnostics13030564. PMID: 36766670; PMCID: PMC9914698.

11.

Dittmann M, Gonzalez Carvalho JM, Rahbar K, Schäfers M, Claesener M, Riemann B, Seifert R. Incremental diagnostic value of [18F]tetrafluoroborate PET-CT compared to [131I]iodine scintigraphy in recurrent differentiated thyroid cancer. Eur J Nucl Med Mol Imaging. 2020 Oct;47(11):2639–46. https://doi.org/10.1007/s00259-020-04727-9. Epub 2020 Apr 4. PMID: 32248325; PMCID: PMC7515952.

12.

Fu H, Wu J, Huang J, Sun L, Wu H, Guo W, Qiu S, Chen H. 68Ga fibroblast activation protein inhibitor PET/CT in the detection of metastatic thyroid Cancer: comparison with 18F-FDG PET/CT. Radiology. 2022 Aug;304(2):397–405. doi: https://doi.org/10.1148/radiol.212430. Epub 2022 May 10. PMID: 35536131.

13.

Dondi F, Gatta R, Albano D, Bellini P, Camoni L, Treglia G, Bertagna F. Role of Radiomics features and machine learning for the histological classification of stage I and Stage II NSCLC at [18F]FDG PET/CT: a comparison between two PET/CT scanners. J Clin Med 2022 Dec 29;12(1):255. doi: https://doi.org/10.3390/jcm12010255. PMID: 36615053; PMCID: PMC9820870.

14.

Albano D, Gatta R, Marini M, Rodella C, Camoni L, Dondi F, Giubbini R, Bertagna F. Role of 18F-FDG PET/CT Radiomics features in the Differential diagnosis of Solitary Pulmonary Nodules: diagnostic accuracy and comparison between two different PET/CT scanners. J Clin Med 2021 Oct 29;10(21):5064. doi: https://doi.org/10.3390/jcm10215064. PMID: 34768584; PMCID: PMC8584460.

15.

Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020 Feb 27;9(2):14. doi: https://doi.org/10.1167/tvst.9.2.14. PMID: 32704420; PMCID: PMC7347027.

16.

Deo RC. Machine Learning in Medicine. Circulation. 2015 Nov 17;132(20):1920-30. doi: https://doi.org/10.1161/CIRCULATIONAHA.115.001593. PMID: 26572668; PMCID: PMC5831252.

17.

Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, QUADAS-2 Group. ;. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011 Oct 18;155(8):529 – 36. doi: https://doi.org/10.7326/0003-4819-155-8-201110180-00009. PMID: 22007046.

18.

Lapa C, Werner RA, Schmid JS, Papp L, Zsótér N, Biko J, Reiners C, Herrmann K, Buck AK, Bundschuh RA. Prognostic value of positron emission tomography-assessed tumor heterogeneity in patients with thyroid cancer undergoing treatment with radiopeptide therapy. Nucl Med Biol. 2015 Apr;42(4):349–54. Epub 2014 Dec 20. PMID: 25595135.

19.

Sollini M, Cozzi L, Pepe G, Antunovic L, Lania A, Di Tommaso L, Magnoni P, Erba PA, Kirienko M. [18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results. Eur J Hybrid Imaging. 2017;1(1):3. https://doi.org/10.1186/s41824-017-0009-8. Epub 2017 Oct 12. PMID: 29782578; PMCID: PMC5954705.CrossRefPubMed

20.

Nakajo M, Jinguji M, Shinaji T, Tani A, Nakabeppu Y, Nakajo M, Nakajo A, Natsugoe S, Yoshiura T. 18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters. Br J Radiol. 2019 Feb;92(1094):20180620. https://doi.org/10.1259/bjr.20180620. Epub 2018 Nov 7. PMID: 30273012; PMCID: PMC6404810.

21.

Werner RA, Bundschuh RA, Higuchi T, Javadi MS, Rowe SP, Zsótér N, Kroiss M, Fassnacht M, Buck AK, Kreissl MC, Lapa C. Volumetric and texture analysis of pretherapeutic 18F-FDG PET can predict overall survival in medullary thyroid cancer patients treated with Vandetanib. Endocrine. 2019 Feb;63(2):293–300. Epub 2018 Sep 11. PMID: 30206772; PMCID: PMC6394453.

22.

Aksu A, Karahan Şen NP, Acar E, Çapa Kaya G. Evaluating focal 18F-FDG uptake in thyroid gland with Radiomics. Nucl Med Mol Imaging. 2020 Oct;54(5):241–8. https://doi.org/10.1007/s13139-020-00659-2. Epub 2020 Jul 28. PMID: 33088353; PMCID: PMC7560675.

23.

Ceriani L, Milan L, Virili C, Cascione L, Paone G, Trimboli P, Giovanella L. Thyroid. 2021 Jan;31(1):88–95. Epub 2020 Jul 6. PMID: 32517585. Radiomics Analysis of [18F]-Fluorodeoxyglucose-Avid Thyroid Incidentalomas Improves Risk Stratification and Selection for Clinical Assessment.

24.

Giovanella L, Milan L, Piccardo A, Bottoni G, Cuzzocrea M, Paone G, Ceriani L. Radiomics analysis improves 18FDG PET/CT-based risk stratification of cytologically indeterminate thyroid nodules. Endocr 2022 Jan;75(1):202–10. doi: https://doi.org/10.1007/s12020-021-02856-1. Epub 2021 Sep 1. PMID: 34468949; PMCID: PMC8763930.

25.

de Koster EJ, Noortman WA, Mostert JM, Booij J, Brouwer CB, de Keizer B, de Klerk JMH, Oyen WJG, van Velden FHP, de Geus-Oei LF, Vriens D. ; EfFECTS trial study group. Quantitative classification and radiomics of [18F]FDG-PET/CT in indeterminate thyroid nodules. Eur J Nucl Med Mol Imaging 2022 Jun;49(7):2174–88. doi: 10.1007/s00259-022-05712-0. Epub 2022 Feb 9. PMID: 35138444; PMCID: PMC9165273.

26.

Dondi F, Pasinetti N, Gatta R, Albano D, Giubbini R, Bertagna F. Comparison between Two Different Scanners for the Evaluation of the Role of 18F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas. J Clin Med. 2022 Jan 26;11(3):615. doi: https://doi.org/10.3390/jcm11030615. PMID: 35160067; PMCID: PMC8836668.

27.

Ma L, Ma C, Liu Y, Wang X. Thyroid diagnosis from SPECT images using convolutional neural network with optimization. Comput Intell Neurosci 2019 Jan 15;2019:6212759. doi: https://doi.org/10.1155/2019/6212759. PMID: 30766599; PMCID: PMC6350547.

28.

Kavitha M, Lee CH, Shibudas K, Kurita T, Ahn BC. Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on 131I post-ablation whole-body planar scans. Sci Rep. 2020 May 8;10(1):7738. doi: https://doi.org/10.1038/s41598-020-64455-w. Erratum in: Sci Rep. 2020 Jul 9;10(1):11579. PMID: 32385375; PMCID: PMC7211007.

29.

Liu Y, Xu Y, Meng X, Wang X, Bai T. A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms. Curr Med Imaging. 2020;16(3):199–205. doi: https://doi.org/10.2174/1573405615666190115155223. PMID: 32133949.

30.

Currie GM, Iqbal BM. Re-modelling 99m-Technetium pertechnetate thyroid uptake; Statistical, Machine Learning and Deep Learning Approaches. J Nucl Med Technol 2021 Dec 7:jnmt.121.263081. doi: https://doi.org/10.2967/jnmt.121.263081. Epub ahead of print. PMID: 34876477.

31.

Guo Y, Xu J, Li X, Zheng L, Pan W, Qiu M, Mao S, Huang D, Yang X. Classification and diagnosis of residual thyroid tissue in SPECT images based on fine-tuning deep convolutional neural network. Front Oncol 2021 Oct 28;11:762643. doi: https://doi.org/10.3389/fonc.2021.762643. PMID: 34778083; PMCID: PMC8581297.

32.

Qiao T, Liu S, Cui Z, Yu X, Cai H, Zhang H, Sun M, Lv Z, Li D. Deep learning for intelligent diagnosis in thyroid scintigraphy. J Int Med Res 2021 Jan;49(1):300060520982842. doi: https://doi.org/10.1177/0300060520982842. PMID: 33445994; PMCID: PMC7812409.

33.

Yang Y, Zheng B, Li Y, Li Y, Ma X. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT. Med Phys. 2023 Jan;50(1):152–62. https://doi.org/10.1002/mp.15901. Epub 2022 Aug 17. PMID: 35925871.

34.

Kim SJ, Chang S. Predictive value of intratumoral heterogeneity of F-18 FDG uptake for characterization of thyroid nodules according to Bethesda categories of fine needle aspiration biopsy results. Endocrine. 2015 Dec;50(3):681–8. https://doi.org/10.1007/s12020-015-0620-z. Epub 2015 May 7. PMID: 25948075.

35.

Treglia G, Rufini V, Piccardo A, Imperiale A. Update on Management of Medullary Thyroid Carcinoma: Focus on Nuclear Medicine. Semin Nucl Med. 2023 Jan 24:S0001-2998(23)00003-X. doi: https://doi.org/10.1053/j.semnuclmed.2023.01.003. Epub ahead of print. PMID: 36702731.

36.

Piccardo A, Trimboli P, Foppiani L, Treglia G, Ferrarazzo G, Massollo M, Bottoni G, Giovanella L. PET/CT in thyroid nodule and differentiated thyroid cancer patients. The evidence-based state of the art. Rev Endocr Metab Disord. 2019 Mar;20(1):47–64. doi: https://doi.org/10.1007/s11154-019-09491-2. PMID: 30900067.

37.

Sollini M, Cozzi L, Chiti A, Kirienko M. Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: where do we stand? Eur J Radiol. 2018 Feb;99:1–8. https://doi.org/10.1016/j.ejrad.2017.12.004. Epub 2017 Dec 7. PMID: 29362138.

38.

Deo RC. Machine Learning in Medicine. Circulation. 2015 Nov 17;132(20):1920-30. https://doi.org/10.1161/CIRCULATIONAHA.115.001593. PMID: 26572668; PMCID: PMC5831252.

39.

Lee JW, Lee SM. Radiomics in Oncological PET/CT: clinical applications. Nucl Med Mol Imaging. 2018 Jun;52(3):170–89. https://doi.org/10.1007/s13139-017-0500-y. Epub 2017 Oct 20. PMID: 29942396; PMCID: PMC5995782.

40.

Roth MY, Witt RL, Steward DL. Molecular testing for thyroid nodules: Review and current state. Cancer. 2018 Mar 1;124(5):888–898. doi: https://doi.org/10.1002/cncr.30708. Epub 2017 Dec 26. PMID: 29278433.

41.

Livhits MJ, Zhu CY, Kuo EJ, Nguyen DT, Kim J, Tseng CH, Leung AM, Rao J, Levin M, Douek ML, Beckett KR, Cheung DS, Gofnung YA, Smooke-Praw S, Yeh MW. Effectiveness of Molecular Testing Techniques for Diagnosis of Indeterminate Thyroid Nodules: A Randomized Clinical Trial. JAMA Oncol. 2021 Jan 1;7(1):70–77. doi: https://doi.org/10.1001/jamaoncol.2020.5935. PMID: 33300952; PMCID: PMC7729582.

42.

Bose S, Sacks W, Walts AE. Update on Molecular Testing for Cytologically Indeterminate Thyroid Nodules. Adv Anat Pathol. 2019 Mar;26(2):114–123. doi: https://doi.org/10.1097/PAP.0000000000000211. PMID: 30664001.

43.

Burguera B, Gharib H. Thyroid incidentalomas. Prevalence, diagnosis, significance, and management. Endocrinol Metab Clin North Am. 2000 Mar;29(1):187–203. doi: https://doi.org/10.1016/s0889-8529(05)70123-7. PMID: 10732271.

44.

Kant R, Davis A, Verma V. Thyroid Nodules: Advances in Evaluation and Management. Am Fam Physician. 2020 Sep 1;102(5):298–304. PMID: 32866364.

45.

Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 2015 american thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid Cancer: the american thyroid Association Guidelines Task Force on thyroid nodules and differentiated thyroid Cancer. Thyroid. 2016 Jan;26(1):1–133. https://doi.org/10.1089/thy.2015.0020. PMID: 26462967; PMCID: PMC4739132.

46.

Albano D, Bellini P, Dondi F, Calabrò A, Casella C, Taboni S, Lombardi D, Treglia G, Bertagna F. Temporal Evolution and Prognostic Role of Indeterminate Response Sub-Groups in Patients with Differentiated Thyroid Cancer after Initial Therapy with Radioiodine. Cancers (Basel). 2023 Feb 16;15(4):1270. doi: https://doi.org/10.3390/cancers15041270. PMID: 36831612; PMCID: PMC9954717.

47.

Gul M, Bonjoc KC, Gorlin D, Wong CW, Salem A, La V, Filippov A, Chaudhry A, Imam MH, Chaudhry AA. Diagnostic utility of Radiomics in thyroid and Head and Neck cancers. Front Oncol 2021 Jul 7;11:639326. doi: https://doi.org/10.3389/fonc.2021.639326. PMID: 34307123; PMCID: PMC8293690.

48.

Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers (Basel). 2021 May 18;13(10):2436. doi: https://doi.org/10.3390/cancers13102436. PMID: 34069887; PMCID: PMC8157383.

49.

Gherghe M, Lazar AM, Mutuleanu MD, Stanciu AE, Martin S. Radiomics Analysis of [18F]FDG PET/CT Thyroid Incidentalomas: How Can It Improve Patients’ Clinical Management? A Systematic Review from the Literature. Diagnostics (Basel). 2022 Feb 12;12(2):471. doi: https://doi.org/10.3390/diagnostics12020471. PMID: 35204561; PMCID: PMC8870948.

50.

Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel). 2022 May 27;12(6):1329. doi: https://doi.org/10.3390/diagnostics12061329. PMID: 35741138; PMCID: PMC9221970.

Title: Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review
Authors: Francesco Dondi
Roberto Gatta
Giorgio Treglia
Arnoldo Piccardo
Domenico Albano
Luca Camoni
Elisa Gatta
Maria Cavadini
Carlo Cappelli
Francesco Bertagna
Publication date: 12-07-2023
Publisher: Springer US
Keywords: Positron Emission Tomography
Thyroid Disease
Thyroid Disease
Thyroid Cancer
Thyroid Cancer
Thyroid Cancer
Published in: Reviews in Endocrine and Metabolic Disorders / Issue 1/2024
Print ISSN: 1389-9155
Electronic ISSN: 1573-2606
DOI: https://doi.org/10.1007/s11154-023-09822-4

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