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Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics

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Abstract

Purpose

The aim of this study was to evaluate the ability of 18F-FDG PET/CT texture analysis to predict the exact pathological outcome of thyroid incidentalomas.

Methods

18F-FDG PET/CT images between March 2010 and September 2018 were retrospectively reviewed in patients with focal 18F-FDG uptake in the thyroid gland and who underwent fine needle aspiration biopsy from this area. The focal uptake in the thyroid gland was drawn in 3D with 40% SUVmax threshold. Features were extracted from volume of interest (VOI) using the LIFEx package. The features obtained were compared in benign and malignant groups, and statistically significant variables were evaluated by receiver operating curve (ROC) analysis. The correlation between the variables with area under curve (AUC) value over 0.7 was examined; variables with correlation coefficient less than 0.6 were evaluated with machine learning algorithms.

Results

Sixty patients (70% train set, 30% test set) were included in the study. In univariate analysis, a statistically significant difference was observed in 6 conventional parameters, 5 first-, and 16 second-order features between benign and malignant groups in train set (p < 0.05). The feature with the highest benign-malignant discriminating power was GLRLMRLNU (AUC:0.827). AUC value of SUVmax was calculated as 0.758. GLRLMRLNU and SUVmax were evaluated to build a model to predict the exact pathology outcome. Random forest algorithm showed the best accuracy and AUC (78.6% and 0.849, respectively).

Conclusion

In the differentiation of benign-malignant thyroid incidentalomas, GLRLMRLNU and SUVmax combination may be more useful than SUVmax to predict the outcome.

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References

  1. Thuillier P, Roudaut N, Crouzeix G, Cavarec M, Robin P, Abgral R, et al. Malignancy rate of focal thyroid incidentaloma detected by FDG PET–CT: results of a prospective cohort study. Endocr Connect. 2017;6:413–21.

    Article  Google Scholar 

  2. Choi JY, Lee KS, Kim HJ, Shim YM, Kwon OJ, Park K, et al. Focal thyroid lesions incidentally identified by integrated 18F-FDG PET/CT: clinical significance and improved characterization. J Nucl Med. 2006;47:609–15.

    PubMed  Google Scholar 

  3. Pagano L, Sama MT, Morani F, Prodam F, Rudoni M, Boldorini R, et al. Thyroid incidentaloma identified by 18F-fluorodeoxyglucose positron emission tomography with CT (FDG-PET/CT): clinical and pathological relevance. Clin Endocrinol. 2011;75:528–34.

    Article  CAS  Google Scholar 

  4. Bertagna F, Treglia G, Piccardo A, Giubbini R. Diagnostic and clinical significance of F-18-FDG-PET/CT thyroid incidentalomas. J Clin Endocrinol Metab. 2012;97:3866–75.

    Article  CAS  Google Scholar 

  5. Shie P, Cardarelli R, Sprawls K, Fulda KG, Taur A. Systematic review: prevalence of malignant incidental thyroid nodules identified on fluorine-18 fluorodeoxyglucose positron emission tomography. Nucl Med Commun. 2009;30:742–8.

    Article  Google Scholar 

  6. Soelberg KK, Bonnema SJ, Brix TH, Hegedüs L. Risk of malignancy in thyroid incidentalomas detected by 18F-fluorodeoxyglucose positron emission tomography: a systematic review. Thyroid. 2012;22:918–25.

    Article  CAS  Google Scholar 

  7. Cohen MS, Arslan N, Dehdashti F, Doherty GM, Lairmore TC, Brunt LM, et al. Risk of malignancy in thyroid incidentalomas identified by fluorodeoxyglucose-positron emission tomography. Surgery. 2001;130:941–6.

    Article  CAS  Google Scholar 

  8. Hagenimana N, Dallaire J, Vallée É, Belzile M. Thyroid incidentalomas on 18FDG-PET/CT: a metabolico-pathological correlation. J Otolaryngol Head Neck Surg. 2017:46–22.

  9. Kim JM, Ryu JS, Kim TY, Kim WB, Kwon GY, Gong G, et al. 18F-Fluorodeoxyglucose positron emission tomography does not predict malignancy in thyroid nodules cytologically diagnosed as follicular neoplasm. J Clin Endocrinol Metab. 2007;92:1630–4.

    Article  CAS  Google Scholar 

  10. Are C, Hsu JF, Schoder H, Shah JP, Larson SM, Shaha AR. FDG-PET detected thyroid incidentalomas: need for further investigation? Ann Surg Oncol. 2007;14:239–47.

    Article  Google Scholar 

  11. Kim BH, Kim SJ, Kim H, Jeon YK, Kim SS, Kim IJ, et al. Diagnostic value of metabolic tumor volume assessed by 18F-FDG PET/CT added to SUVmax for characterization of thyroid 18F-FDG incidentaloma. Nucl Med Commun. 2013;34:868–76.

    Article  CAS  Google Scholar 

  12. Shi H, Yuan Z, Yuan Z, Yang C, Zhang J, Shou Y, et al. Diagnostic value of volume-based fluorine-18-fluorodeoxyglucose PET/CT parameters for characterizing thyroid incidentaloma. Korean J Radiol. 2018;19:342–51.

    Article  Google Scholar 

  13. Parvez A, Tau N, Hussey D, Maganti M, Metser U, et al. 18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival. Ann Nucl Med. 2018;32:410–6.

    Article  CAS  Google Scholar 

  14. Zhou L, Zhang Z, Chen YC, Zhao ZY, Yin XD, Jiang HB. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol. 2019;12:292–300.

    Article  Google Scholar 

  15. Mao N, Yin P, Wang Q, Liu M, Dong J, Zhang X, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol. 2019;16:485–91.

    Article  Google Scholar 

  16. Abdollahi H, Mofid B, Shiri I, Razzaghdoust A, Saadipoor A, Mahdavi A, et al. Machine-learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med. 2019;124:555–67.

    Article  Google Scholar 

  17. Yang L, Yang J, Zhou X, Huang L, Zhao W, Wang T, et al. Development of a radiomics nomogram on the 2D and 3D CT features to predict the survival of non-small cancer patients. Eur Radiol. 2019;29:2196–206.

    Article  Google Scholar 

  18. Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patient. BMC Cancer. 2018;18:1148.

    Article  Google Scholar 

  19. Sollini M, Cozzi L, Pepe G, Antunovic L, Lania A, Di Tommaso L, et al. [18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results. Eur J Hybrid Imaging. 2017;1:3.

    Article  CAS  Google Scholar 

  20. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9.

    Article  CAS  Google Scholar 

  21. Ouyang FS, Guo BL, Zhang B, Dong YH, Zhang L, Mo XK, et al. Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma. Oncotarget. 2017;24:74869–79.

    Article  Google Scholar 

  22. Huang Z, Zhang W, He D, Cui X, Tian S, Yin H, et al. Development and validation of a radiomics model based on T2WI images for preoperative prediction of microsatellite instability status in rectal cancer: study protocol clinical trial (SPIRIT Compliant). Medicine (Baltimore). 2020;99:e19428.

    Article  Google Scholar 

  23. Schernberg A, Reuze S, Orlhac F, et al. A score combining baseline neutrophilia and primary tumor SUV peak measured from FDG PET is associated with outcome in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging. 2018;45:187–95.

    Article  Google Scholar 

  24. Boughdad S, Nioche C, Orlhac F, et al. Influence of age on radiomic features in 18 F-FDG PET in normal breast tissue and in breast cancer tumors. Oncotarget. 2018;20(9):30855–68.

    Article  Google Scholar 

  25. Zhou H, Jiang J, Lu J, Wang M, Zhang H, Zuo C. Dual-model radiomic biomarkers predict development of mild cognitive impairment progression to Alzheimer’s disease. Front Neurosci. 2019;12:1045.

    Article  Google Scholar 

  26. Chan YH. Bioistatistics 104: correlation analysis. Singap Med J. 2003;44:614–9.

    CAS  Google Scholar 

  27. Sharma C. Diagnostic accuracy of fine needle aspiration cytology of thyroid and evaluation of discordant cases. J Egypt Natl Canc Inst. 2015;27:147–53.

    Article  Google Scholar 

  28. Machała E, Sopiński J, Iavorska I, Kołomecki K. Correlation of fine needle aspiration cytology of thyroid gland with histopathological results. Pol Przegl Chir. 2018;21(90):1–5.

    Article  Google Scholar 

  29. Sukumaran R, Kattoor J, Pillai KR, Ramadas PT, Nayak N, Somanathan T, et al. Fine needle aspiration cytology of thyroid lesions and its correlation with histopathology in a series of 248 patients. Indian J Surg Oncol. 2014;5:237–41.

    Article  Google Scholar 

  30. Cook GJR, Azad G, Owczarczyk K. Challenges and promises of PET radiomic. Int J Radiat Oncol Biol Phys. 2018;15(102):1083–9.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the members of the Dokuz Eylül University, Department of Nuclear Medicine for their technical assistance and support.

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Authors and Affiliations

  1. Department of Nuclear Medicine, School of Medicine, Dokuz Eylul University, İzmir, Turkey

    Ayşegül Aksu, Nazlı Pınar Karahan Şen & Gamze Çapa Kaya

  2. Department of Nuclear Medicine, Kent Hospital, İzmir, Turkey

    Emine Acar

  3. Department of Translational Oncology, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey

    Emine Acar

Authors
  1. Ayşegül Aksu
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  2. Nazlı Pınar Karahan Şen
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  3. Emine Acar
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  4. Gamze Çapa Kaya
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Correspondence to Ayşegül Aksu.

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Conflict of Interest

Ayşegül Aksu, Nazlı Pınar Karahan Şen, Emine Acar, and Gamze Çapa Kaya declare that they have no conflict of interest.

Ethics Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

The institutional review board of our institute approved this retrospective study, and the requirement to obtain informed consent was waived.

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Aksu, A., Karahan Şen, N.P., Acar, E. et al. Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics. Nucl Med Mol Imaging 54, 241–248 (2020). https://doi.org/10.1007/s13139-020-00659-2

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  • DOI: https://doi.org/10.1007/s13139-020-00659-2

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