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Can contrast-enhancement computed tomography texture and histogram analyses help to differentiate malignant from benign thyroid nodules?

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Abstract

Purpose

We aimed to determine the ability of contrast-enhanced computed tomography (CECT) texture and histogram analyses to differentiate between benign and malignant thyroid nodules.

Materials and methods

The clinical data from 49 patients with 60 thyroid nodules were retrospectively analyzed. Nodules were classified as malignant or benign based on their histological results. Five texture and histogram parameters of thyroid nodules from CECT images, including entropy, mean, standard deviation, skewness, and kurtosis, were compared and analyzed between the two groups. Regions of interest in axial CECT images were delineated manually by two radiologists. Interobserver agreement in texture and histogram parameters between the two radiologists was assessed using the intraclass correlation coefficient (ICC). The Mann–Whitney U test and receiver operating characteristic curve analysis were conducted to estimate the diagnostic capability of texture parameters.

Results

Interobserver reproducibility (ICC = 0.919–0.969) was excellent. Among the 60 nodules, 36 were malignant and 24 were benign. Entropy of malignant thyroid nodules was significantly higher compared with benign thyroid nodules (P = 0.005). A trend toward a higher kurtosis value was observed in malignant thyroid nodules (P = 0.062). When an entropy value of 6.55 was used as a cutoff for differentiating benign from malignant thyroid nodules, the optimal area under the curve, sensitivity, and specificity were 0.716 (0.585–0.847, 95% confidence interval, P = 0.005), 75.0%, and 62.5%, respectively.

Conclusions

CECT texture and histogram analyses can be used to differentiate benign from malignant thyroid nodules.

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References

  1. Dean DS, Gharib H. Epidemiology of thyroid nodules. Best Pract Res Clin Endocrinol Metab. 2008;22:901–11.

    Article  Google Scholar 

  2. Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 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;26:1–133.

    Article  Google Scholar 

  3. Gopinath B, Shanthi N. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images. Australas Phys Eng Sci Med. 2013;36:219–30.

    Article  Google Scholar 

  4. Kim D, Kim DW, Heo YJ, Baek JW, Lee YJ, Park YM, et al. Computed tomography features of benign and malignant calcified thyroid nodules: a single-center study. J Comput Assist Tomogr. 2017;41:937–40.

    Article  Google Scholar 

  5. Ben-David E, Sadeghi N, Rezaei MK, Muradyan N, Brown D, Joshi A, et al. Semiquantitative and quantitative analyses of dynamic contrast-enhanced magnetic resonance imaging of thyroid nodules. J Comput Assist Tomogr. 2015;39:855–9.

    Article  Google Scholar 

  6. Yerubandi V, Chin BB, Sosa JA, Hoang JK. Incidental thyroid nodules at non-FDG PET nuclear medicine imaging: evaluation of prevalence and malignancy rate. AJR Am J Roentgenol. 2016;206:420–5.

    Article  Google Scholar 

  7. Frates MC, Benson CB, Charboneau JW, Cibas ES, Clark OH, Coleman BG, et al. Management of thyroid nodules detected at US: society of radiologists in ultrasound consensus conference statement. Radiology. 2005;237:794–800.

    Article  Google Scholar 

  8. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3:573–89.

    Article  Google Scholar 

  9. Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013;13:140–9.

    Article  Google Scholar 

  10. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10:137–43.

    Article  Google Scholar 

  11. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell ung cancer: histopathologic correlates for texture parameters at CT. Radiology. 2013;266:326–36.

    Article  Google Scholar 

  12. Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol. 2006;13:713–20.

    Article  Google Scholar 

  13. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266:177–84.

    Article  Google Scholar 

  14. Raja JV, Khan M, Ramachandra VK, Al-Kadi O. Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa. Dentomaxillofac Radiol. 2012;41:475–80.

    Article  CAS  Google Scholar 

  15. Zhang H, Graham CM, Elci O, Griswold ME, Zhang X, Khan MA, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269:801–9.

    Article  Google Scholar 

  16. Buch K, Fujita A, Li B, Kawashima Y, Qureshi MM, Sakai O. Using texture analysis to determine human papillomavirus status of oropharyngeal squamous cell carcinomas on CT. AJNR. 2015;36:1343–8.

    Article  CAS  Google Scholar 

  17. Scalco E, Fiorino C, Cattaneo GM, Sanguineti G, Rizzo G. Texture analysis for the assessment of structural changes in parotid glands induced by radiotherapy. Radiother Oncol. 2013;109:384–7.

    Article  Google Scholar 

  18. 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;99:1–8.

    Article  Google Scholar 

  19. Peng W, Liu C, Xia S, Shao D, Chen Y, Liu R, et al. Thyroid nodule recognition in computed tomography using first order statistics. Biomed Eng Online. 2017;16:67.

    Article  Google Scholar 

  20. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37:1483–503.

    Article  Google Scholar 

  21. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9.

    Article  CAS  Google Scholar 

  22. Liu C, Chen S, Yang Y, Shao D, Peng W, Wang Y, et al. The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images. Quant Imaging Med Surg. 2019;9:642–53.

    Article  Google Scholar 

  23. Tomita H, Kuno H, Sekiya K, Otani K, Sakai O, Li B, et al. Quantitative assessment of thyroid nodules using dual-energy computed tomography: iodine concentration measurement and multiparametric texture analysis for differentiating between malignant and benign lesions. Int J Endocrinol. 2020;2020:5484671.

    Article  Google Scholar 

  24. Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, et al. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol. 2020;75:108–15.

    Article  CAS  Google Scholar 

  25. Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol. 2014;21:1587–96.

    Article  Google Scholar 

  26. Takahashi N, Takeuchi M, Sasaguri K, Leng S, Froemming A, Kawashima A. CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT. Abdom Radiol (NY). 2016;41:1142–51.

    Article  Google Scholar 

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Funding

This work was funded by the National Key Research and Development Program (2017YFC0113403).

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Correspondence to Dehong Luo.

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The authors declare that they have no conflict of interest in this study.

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The retrospective study was approved by the institutional review board, and the requirement for informed consent was waived.

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Guo, W., Bai, W., Liu, J. et al. Can contrast-enhancement computed tomography texture and histogram analyses help to differentiate malignant from benign thyroid nodules?. Jpn J Radiol 38, 1135–1141 (2020). https://doi.org/10.1007/s11604-020-01018-z

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  • DOI: https://doi.org/10.1007/s11604-020-01018-z

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