| 1 |
Raue F, Frank-raue K. Thyroid cancer: risk-stratified management and individualized therapy [J]. Clin Cancer Res, 2016, 22(20): 5012-5021.
|
| 2 |
中华人民共和国国家卫生健康委员会. 甲状腺癌诊疗规范(2018年版) [J/CD]. 中华普通外科学文献(电子版), 2019, 13(1): 7-21.
|
| 3 |
Castellana M, Piccardo A, Virili C, et al. Can ultrasound systems for risk stratification of thyroid nodules identify follicular carcinoma? [J]. Cancer Cytopathol, 2020, 128(4): 250-259.
|
| 4 |
Haugen BR, Alexander EK, Bible KC, 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 [J]. Thyroid, 2016, 26(1): 1-133.
|
| 5 |
Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee [J]. J Am Coll Radiol, 2017, 14(5): 587-595.
|
| 6 |
Borowczyk M, Wolinski K, Wieckowska B, et al. Sonoimagedata features differentiating follicular thyroid cancer from follicular adenoma-a meta-analysis [J]. Cancers, 2021, 13(5): 938.
|
| 7 |
Kamraan SC, Marqusee E, Kim MI, et al. Thyroid nodule size and prediction of cancer [J]. J Clin Endocrinol Metab, 2013, 98(2): 564-570.
|
| 8 |
Angell TE, Vyas CM, Medici M, et al. Differential growth rates of benign vs. malignant thyroid nodules [J]. J Clin Endocrinol Metab, 2017, 102(12): 4642-4647.
|
| 9 |
Kim M, Han M, Lee JH, et al. Tumour growth rate of follicular thyroid carcinoma is not different from that of follicular adenoma [J]. Clin Endocrinol, 2018, 88(6): 936-942.
|
| 10 |
谢文婷, 陈轶洁, 黄伟钦, 等. 甲状腺滤泡癌与腺瘤的超声声像比较研究 [J]. 肿瘤, 2020, 40(5): 348-354.
|
| 11 |
Chng CL, Kurzawinski TR, Beale T. Value of sonoimagedata features in predicting malignancy in thyroid nodules diagnosed as follicular neoplasm on cytology [J]. Clin Endocrinol, 2015, 83(5): 711-716.
|
| 12 |
Lai XJ, Jiang Y, Zhang B, et al. Preoperative sonoimagedata features of follicular thyroid carcinoma predict biological behavior: a retrospective study [J]. Medicine, 2018, 97(41): e12814.
|
| 13 |
Wu MH, Chen KY, Hsieh MS, et al. Risk stratification in patients with follicular neoplasm on cytology: use of quantitative characteristics and sonoimagedata patterns [J]. Front Endocrinol, 2021, 12: 614630.
|
| 14 |
Kuo TC, Wu MH, Chen KY, et al. Ultrasonoimagedata features for differentiating follicular thyroid carcinoma and follicular adenoma [J]. Asian J Surg, 2020, 43(1): 339-346.
|
| 15 |
林婉玲, 丁金旺, 包凌云. 甲状腺滤泡癌的术前超声特征与病理亚型的关系 [J]. 中国临床医学影像杂志, 2020, 31(2): 92-94.
|
| 16 |
Lee SH, Baek JS, Lee JY, et al. Predictive factors of malignancy in thyroid nodules with a cytological diagnosis of follicular neoplasm [J]. Endocr Pathol, 2013, 24(4): 177-183.
|
| 17 |
Zhang JZ, Hu B. Sonoimagedata features of thyroid follicular carcinoma in comparison with thyroid follicular adenoma [J]. J Ultrasound Med, 2014, 33(2): 221-227.
|
| 18 |
刘鑫. 常规超声对甲状腺滤泡肿瘤的鉴别诊断价值 [J]. 中国医药指南, 2019, 17(31): 45-46.
|
| 19 |
Yu QA, Liu KP, Xie CM, et al. Development and validation of a preoperative prediction model for follicular thyroid carcinoma [J]. Clin Endocrinol, 2019, 91(2): 348-355.
|
| 20 |
Sillery JC, Reading CC, Charboneau JW, et al. Thyroid follicular carcinoma: sonoimagedata features of 50 cases [J]. AJR Am J Roentgenol, 2010, 194(1): 44-54.
|
| 21 |
Kim H, Shin JH, Hahn SY, et al. Prediction of follicular thyroid carcinoma associated with distant metastasis in the preoperative and postoperative model [J]. Head Neck, 2019, 41(8): 2507-2513.
|
| 22 |
Ou D, Yao JC, Jin J, et al. Ultrasonic identification and regression analysis of 294 thyroid follicular tumors [J]. J Cancer Res Ther, 2020, 16(5): 1056-1062.
|
| 23 |
张晓燕. 甲状腺滤泡癌的超声诊断及进展 [J]. 中国介入影像与治疗学, 2012, 9(12): 885-888.
|
| 24 |
牛卫东, 赵诚, 臧翊辰, 等. 甲状腺滤泡癌和滤泡性腺瘤的超声诊断与鉴别价值 [J/CD]. 中华临床医师杂志(电子版), 2015, 9(22): 4105-4109.
|
| 25 |
陈铃, 刘晗, 张建兴, 等. 改良ACR TI-RADS超声词典鉴别甲状腺滤泡性肿瘤良恶性的临床研究 [J]. 实用医学杂志, 2019, 35(14): 2296-2299.
|
| 26 |
Chen L, Zhang JX, Meng LC, et al. A new ultrasound nomogram for differentiating benign and malignant thyroid nodules [J]. Clin Endocrinol, 2019, 90(2): 351-359.
|
| 27 |
Li W, Song Q, Lan Y, et al. The value of sonography in distinguishing follicular thyroid carcinoma from adenoma [J]. Cancer Manag Res, 2021, 13: 3991-4002.
|
| 28 |
喻倩, 马步云. 甲状腺滤泡癌与甲状腺滤泡性腺瘤,结节性甲状腺肿的超声特征分析 [J]. 中国临床医生杂志, 2021, 49(9): 1105-1108.
|
| 29 |
李华鹃, 杨玉萍, 徐晓红. 超声诊断甲状腺滤泡型肿瘤的研究进展 [J]. 影像研究与医学应用, 2021, 5(3): 1-2.
|
| 30 |
Huang QS, Xie LJ, Huang LY, et al. Development and validation of an ultrasonic diagnostic model for differentiating follicular thyroid carcinoma from follicular adenoma [J]. Int J Gen Med, 2021, 14: 5069-5078.
|
| 31 |
Kwon MR, Shin JH, Park H, et al. Radiomics based on thyroid ultrasound can predict distant metastasis of follicular thyroid carcinoma [J]. J Clin Med, 2020, 9(7): 2156.
|
| 32 |
Shin JH, Han BK, Ko EY, et al. Differentiation of widely invasive and minimally invasive follicular thyroid carcinoma with sonography [J]. Eur J Radiol, 2010, 74(3): 453-457.
|
| 33 |
张吉臻, 胡兵. 甲状腺滤泡癌的超声声像图特征 [J/CD]. 中华医学超声杂志(电子版), 2013, (6): 484-488.
|
| 34 |
Iared W, Shigueoka DC, Cristófoli JC, et al. Use of color Doppler ultrasonography for the prediction of malignancy in follicular thyroid neoplasms: systematic review and meta-analysis [J]. J Ultrasound Med, 2010, 29(3): 419-425.
|
| 35 |
Jeong SH, Hong HS, Lee EH. Can nodular hyperplasia of the thyroid gland be differentiated from follicular adenoma and follicular carcinoma by ultrasonography? [J]. Ultrasound Q, 2016, 32(4): 349-355.
|
| 36 |
邬宏恂, 王隽. 甲状腺滤泡状癌声像图分析 [J]. 临床超声医学杂志, 2007, 9(9): 535-538.
|
| 37 |
Nemec U, Nemec SF, Novotny C, et al. Quantitative evaluation of contrast-enhanced ultrasound after intravenous administration of a microbubble contrast agent for differentiation of benign and malignant thyroid nodules: assessment of diagnostic accuracy [J]. Eur Radiol, 2012, 22(6): 1357-1365.
|
| 38 |
Xi XH, Gao LY, Wu Q, et al. Differentiation of thyroid nodules difficult to diagnose with contrast-enhanced ultrasonography and real-time elastography [J]. Front Oncol, 2020, 10: 112.
|
| 39 |
瞿嫣慧. 甲状腺滤泡性肿瘤的超声诊断价值研究 [D]. 上海: 上海交通大学, 2018.
|
| 40 |
Wu Q, Qu YH, Li Y, et al. Logistic regression analysis of contrast-enhanced ultrasound and conventional ultrasound of follicular thyroid carcinoma and follicular adenoma [J]. Gland Surg, 2021, 10(10): 2890-2900.
|
| 41 |
李宁, 杨丽春, 王丽伟, 等. 声辐射力弹性成像联合超声造影对甲状腺滤泡型肿瘤的诊断价值 [J]. 放射学实践, 2020, 35(5): 663-667.
|
| 42 |
Radzina M, Ratniece M, Putrins DS, et al. Performance of contrast-enhanced ultrasound in thyroid nodules: review of current state and future perspectives [J]. Cancers, 2021, 13(21): 5469.
|
| 43 |
徐芳. 声脉冲辐射力成像技术对甲状腺良、恶性结节的鉴别诊断价值 [D]. 苏州: 苏州大学, 2014.
|
| 44 |
Swan KZ, Nielsen VE, Bonnema SJ. Evaluation of thyroid nodules by shear wave elastography: a review of current knowledge [J]. J Endocrinol Invest, 2021, 44(10): 2043-2056.
|
| 45 |
Samir AE, Dhyani M, Anvari A, et al. Shear-wave elastography for the preoperative risk stratification of follicular-patterned lesions of the thyroid: diagnostic accuracy and optimal measurement plane [J]. Radiology, 2015, 277(2): 565-573.
|
| 46 |
Kim HJ, Kwak MK, Choi IH, et al. Utility of shear wave elastography to detect papillary thyroid carcinoma in thyroid nodules: efficacy of the standard deviation elasticity [J]. Korean J Intern Med, 2019, 34(4): 850-857.
|
| 47 |
Liu BJ, Zhang YF, Zhao CK, et al. Conventional ultrasound characteristics, TI-RADS category and shear wave speed measurement between follicular adenoma and follicular thyroid carcinoma [J]. Clin Hemorheol Microcirc, 2020, 75(3): 291-301.
|
| 48 |
Savala R, Dey P, Gupta N. Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid [J]. Diagn Cytopathol, 2018, 46(3): 244-249.
|
| 49 |
Shin I, Kim YJ, Han K, et al. Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland [J]. Ultrasonography, 2020, 39(3): 257-265.
|
| 50 |
Seo JK, Kim YJ, Kim KG, et al. Differentiation of the follicular neoplasm on the gray-scale us by image selection subsampling along with the marginal outline using convolutional neural network [J]. Biomed Res Int, 2017, 2017: 3098293.
|