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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2021

01-12-2021 | Magnetic Resonance Imaging | Research article

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

Authors: Ran Wei, Hao Wang, Lanyun Wang, Wenjuan Hu, Xilin Sun, Zedong Dai, Jie Zhu, Hong Li, Yaqiong Ge, Bin Song

Published in: BMC Medical Imaging | Issue 1/2021

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Abstract

Background

To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.

Methods

The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve.

Results

Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively.

Conclusions

Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.
Literature
1.
Guo Z, Ge M, Chu YH, Asioli S, Lloyd RV. Recent advances in the classification of low-grade papillary-like thyroid neoplasms and aggressive papillary thyroid carcinomas: evolution of diagnostic criteria. Adv Anat Pathol. 2018;25(4):263–72.CrossRefPubMed
2.
Song E, Jeon MJ, Oh HS, Han M, Lee YM, Kim TY, Chung KW, Kim WB, Shong YK, Song DE, et al. Do aggressive variants of papillary thyroid carcinoma have worse clinical outcome than classic papillary thyroid carcinoma? Eur J Endocrinol. 2018;179(3):135–42.CrossRefPubMed
3.
Lu Y, Moreira AL, Hatzoglou V, Stambuk HE, Gonen M, Mazaheri Y, Deasy JO, Shaha AR, Tuttle RM, Shukla-Dave A. Using diffusion-weighted MRI to predict aggressive histological features in papillary thyroid carcinoma: a novel tool for pre-operative risk stratification in thyroid cancer. Thyroid. 2015;25(6):672–80.CrossRefPubMedPubMedCentral
4.
Hu A, Clark J, Payne RJ, Eski S, Walfish PG, Freeman JL. Extrathyroidal extension in well-differentiated thyroid cancer: macroscopic vs microscopic as a predictor of outcome. Arch Otolaryngol Head Neck Surg. 2007;133(7):644–9.CrossRefPubMed
5.
Jukkola A, Bloigu R, Ebeling T, Salmela P, Blanco G. Prognostic factors in differentiated thyroid carcinomas and their implications for current staging classifications. Endocr Relat Cancer. 2004;11(3):571–9.CrossRefPubMed
6.
Cushing SL, Palme CE, Audet N, Eski S, Walfish PG, Freeman JL. Prognostic factors in well-differentiated thyroid carcinoma. Laryngoscope. 2004;114(12):2110–5.CrossRefPubMed
7.
8.
Cady B, Rossi R. An expanded view of risk-group definition in differentiated thyroid carcinoma. Surgery. 1988;104(6):947–53.CrossRefPubMed
9.
Chung SR, Baek JH, Choi YJ, Sung TY, Song DE, Kim TY, Lee JH. Sonographic assessment of the extent of extrathyroidal extension in thyroid cancer. Korean J Radiol. 2020;21(10):1187–95.CrossRefPubMedPubMedCentral
10.
Vaisman F, Momesso D, Bulzico DA, Pessoa CH, da Cruz MD, Dias F, Corbo R, Vaisman M, Tuttle RM. Thyroid lobectomy is associated with excellent clinical outcomes in properly selected differentiated thyroid cancer patients with primary tumors greater than 1 cm. J Thyroid Res. 2013;2013:398194.CrossRefPubMedPubMedCentral
11.
Hay ID. Management of patients with low-risk papillary thyroid carcinoma. Endocr Pract. 2007;13(5):521–33.CrossRefPubMed
12.
Haugen BR. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: what is new and what has changed? Cancer. 2017;123(3):372–81.CrossRefPubMed
13.
Miller B, Burkey S, Lindberg G, Snyder WH 3rd, Nwariaku FE. Prevalence of malignancy within cytologically indeterminate thyroid nodules. Am J Surg. 2004;188(5):459–62.CrossRefPubMed
14.
Liang J, Huang X, Hu H, Liu Y, Zhou Q, Cao Q, Wang W, Liu B, Zheng Y, Li X, et al. Predicting malignancy in thyroid nodules: radiomics score versus 2017 American College of Radiology thyroid imaging, reporting and data system. Thyroid. 2018;28(8):1024–33.CrossRefPubMed
15.
Lee CY, Kim SJ, Ko KR, Chung KW, Lee JH. Predictive factors for extrathyroidal extension of papillary thyroid carcinoma based on preoperative sonography. J Ultrasound Med. 2014;33(2):231–8.CrossRefPubMed
16.
Gweon HM, Son EJ, Youk JH, Kim JA, Park CS. Preoperative assessment of extrathyroidal extension of papillary thyroid carcinoma: comparison of 2- and 3-dimensional sonography. J Ultrasound Med. 2014;33(5):819–25.CrossRefPubMed
17.
American Thyroid Association Guidelines Taskforce on Thyroid N, Differentiated Thyroid C, Cooper DS, Doherty GM, Haugen BR, Kloos RT, Lee SL, Mandel SJ, Mazzaferri EL, McIver B, et al. Revised American Thyroid Association management guidelines for patients with thyroid nodules and differentiated thyroid cancer. Thyroid. 2009;19(11):1167–214.CrossRef
18.
Baloch ZW, LiVolsi VA, Asa SL, Rosai J, Merino MJ, Randolph G, Vielh P, DeMay RM, Sidawy MK, Frable WJ. Diagnostic terminology and morphologic criteria for cytologic diagnosis of thyroid lesions: a synopsis of the National Cancer Institute Thyroid Fine-Needle Aspiration State of the Science Conference. Diagn Cytopathol. 2008;36(6):425–37.CrossRefPubMed
19.
Hu S, Zhang H, Wang X, Sun Z, Ge Y, Li J, Dou W. Can diffusion-weighted MR imaging be used as a tool to predict extrathyroidal extension in papillary thyroid carcinoma? Acad Radiol. 2020.
20.
Wang H, Liu K, Ren J, Liu W, Chen Y, Song B. Magnetic resonance imaging characteristics of papillary thyroid carcinoma for the prediction of cervical central compartment lymph node metastasis. J Comput Assist Tomogr. 2019;43(6):963–9.CrossRefPubMed
21.
Schob S, Voigt P, Bure L, Meyer HJ, Wickenhauser C, Behrmann C, Hohn A, Kachel P, Dralle H, Hoffmann KT, et al. Diffusion-weighted imaging using a readout-segmented, multishot EPI sequence at 3 T distinguishes between morphologically differentiated and undifferentiated subtypes of thyroid carcinoma-a preliminary study. Transl Oncol. 2016;9(5):403–10.CrossRefPubMedPubMedCentral
22.
Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, et al. Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol. 2018;39(2):208–16.CrossRefPubMedPubMedCentral
23.
Xu X, Zhang X, Tian Q, Wang H, Cui LB, Li S, Tang X, Li B, Dolz J, Ayed IB, et al. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J Magn Reson Imaging. 2019;49(5):1489–98.CrossRefPubMed
24.
Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology. 2018;287(3):732–47.CrossRefPubMed
25.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.CrossRefPubMed
26.
Li Z, Li H, Wang S, Dong D, Yin F, Chen A, Wang S, Zhao G, Fang M, Tian J, et al. MR-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively. J Magn Reson Imaging. 2019;49(5):1420–6.CrossRefPubMed
27.
Xie T, Wang Z, Zhao Q, Bai Q, Zhou X, Gu Y, Peng W, Wang H. Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer. Front Oncol. 2019;9:505.CrossRefPubMedPubMedCentral
28.
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.CrossRefPubMedPubMedCentral
29.
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.PubMedPubMedCentral
30.
Ma S, Xie H, Wang H, Yang J, Han C, Wang X, Zhang X. Preoperative prediction of extracapsular extension: radiomics signature based on magnetic resonance imaging to stage prostate cancer. Mol Imaging Biol. 2020;22(3):711–21.CrossRefPubMed
31.
32.
Yin P, Mao N, Zhao C, Wu J, Sun C, Chen L, Hong N. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol. 2019;29(4):1841–7.CrossRefPubMed
33.
Ren J, Tian J, Yuan Y, Dong D, Li X, Shi Y, Tao X. Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage I–II and III–IV head and neck squamous cell carcinoma. Eur J Radiol. 2018;106:1–6.CrossRefPubMed
34.
Wang H, Song B, Ye N, Ren J, Sun X, Dai Z, Zhang Y, Chen BT. Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma. Eur J Radiol. 2020;122:108755.CrossRefPubMed
35.
Yin P, Mao N, Zhao C, Wu J, Chen L, Hong N. A triple-classification radiomics model for the differentiation of primary chordoma, giant cell tumor, and metastatic tumor of sacrum based on T2-weighted and contrast-enhanced T1-weighted MRI. J Magn Reson Imaging. 2019;49(3):752–9.CrossRefPubMed
36.
Wang T, Gao T, Yang J, Yan X, Wang Y, Zhou X, Tian J, Huang L, Zhang M. Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur J Radiol. 2019;114:128–35.CrossRefPubMed
37.
38.
Arebey M, Hannan MA, Begum RA, Basri H. Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach. J Environ Manag. 2012;104:9–18.CrossRef
39.
Metadata
Title
Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer
Authors
Ran Wei
Hao Wang
Lanyun Wang
Wenjuan Hu
Xilin Sun
Zedong Dai
Jie Zhu
Hong Li
Yaqiong Ge
Bin Song
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2021
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-021-00553-z

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