Wu Y, Rao K, Han C, Liu Z, Xu X (2020) Machine learning algorithms for the prediction of central lymph node metastasis in patients with papillary thyroid cancer. Front Endocrinol. https://doi.org/10.3389/fendo.2020.577537
Wiltshire JJ, Drake TM, Uttley L, Balasubramanian SP (2016) Systematic review of trends in the incidence rates of thyroid cancer. Thyroid 26:1541–1552. https://doi.org/10.1089/thy.2016.0100
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660
Article CAS PubMed Google Scholar
Yu J, Deng Y, Liu T, Zhou J, Jia X, Xiao T, Zhou S, Li J, Guo Y, Wang Y, Zhou J, Chang C (2020) Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics. Nat Commun 11:4807. https://doi.org/10.1038/s41467-020-18497-3
Article CAS PubMed PubMed Central Google Scholar
Wang Y, Guan Q, Xiang J (2018) Nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma: a retrospective cohort study of 8668 patients. Int J Surg 55:98–102. https://doi.org/10.1016/j.ijsu.2018.05.023
Xiang D, Hong Y, Zhang B, Huang P, Li G, Wang P, Li Z (2014) Contrast-enhanced ultrasound (CEUS) facilitated US in detecting lateral neck lymph node metastasis of thyroid cancer patients: diagnosis value and enhancement patterns of malignant lymph nodes. Eur Radiol 24:2513–2519. https://doi.org/10.1007/s00330-014-3288-5
Li J, Wu X, Mao N, Zheng G, Zhang H, Mou Y, Jia C, Mi J, Song X (2021) Computed tomography-based radiomics model to predict central cervical lymph node metastases in papillary thyroid carcinoma: a multicenter study. Front Endocrinol (Lausanne) 12:741698. https://doi.org/10.3389/fendo.2021.741698
Stulak JM, Grant CS, Farley DR, Thompson GB, van Heerden JA, Hay ID, Reading CC, Charboneau JW (2006) Value of preoperative ultrasonography in the surgical management of initial and reoperative papillary thyroid cancer. Arch Surg 141:489–494. https://doi.org/10.1001/archsurg.141.5.489
Kan Y, Dong D, Zhang Y, Jiang W, Zhao N, Han L, Fang M, Zang Y, Hu C, Tian J, Li C, Luo Y (2019) Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer. J Magn Reson Imaging 49:304–310. https://doi.org/10.1002/jmri.26209
Park VY, Han K, Kim HJ, Lee E, Youk JH, Kim E-K, Moon HJ, Yoon JH, Kwak JY (2020) Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma. PLoS ONE 15:e0227315. https://doi.org/10.1371/journal.pone.0227315
Article CAS PubMed PubMed Central Google Scholar
Zhu J, Zheng J, Li L, Huang R, Ren H, Wang D, Dai Z, Su X (2021) Application of machine learning algorithms to predict central lymph node metastasis in T1–T2, non-invasive, and clinically node negative papillary thyroid carcinoma. Front Med (Lausanne) 8:635771. https://doi.org/10.3389/fmed.2021.635771
Yu Y, Yu Z, Li M, Wang Y, Yan C, Fan J, Xu F, Meng H, Kong J, Li S, Ling R, Wang T (2022) Model development to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma by machine learning. Ann Transl Med 10:892. https://doi.org/10.21037/atm-22-3594
Article CAS PubMed PubMed Central Google Scholar
Yao J, Lei Z, Yue W, Feng B, Li W, Ou D, Feng N, Lu Y, Xu J, Chen W, Yang C, Wang L, Wang L, Liu J, Wei P, Xu H, Xu D (2022) DeepThy-net: a multimodal deep learning method for predicting cervical lymph node metastasis in papillary thyroid cancer. Adv Intell Syst 4:2200100. https://doi.org/10.1002/aisy.202200100
Wang C, Yu P, Zhang H, Han X, Song Z, Zheng G, Wang G, Zheng H, Mao N, Song X (2023) Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT. Eur Radiol 33:6828–6840. https://doi.org/10.1007/s00330-023-09700-2
Ding X, Liu Y, Zhao J, Wang R, Li C, Luo Q, Shen C (2023) A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images. Comput Med Imaging Graph 109:102298. https://doi.org/10.1016/j.compmedimag.2023.102298
Gürsoy Çoruh A, Uzun Ç, Kul M, Akkaya Z, Halil Elhan A, Gökcan K (2020) The impact of arterial phase on the detection of cervical lymph node metastasis from papillary thyroid carcinoma: a quantitative evaluation on multiphasic computed tomography. J Comput Assist Tomogr 44:262–268. https://doi.org/10.1097/RCT.0000000000001005
Shirley LA, Jones NB, Phay JE (2017) The role of central neck lymph node dissection in the management of papillary thyroid cancer. Front Oncol 7:122. https://doi.org/10.3389/fonc.2017.00122
Article PubMed PubMed Central Google Scholar
Wang J, Dong C, Zhang Y-Z, Wang L, Yuan X, He M, Xu S, Zhou Q, Jiang J (2023) A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients. Eur Radiol 33:9347–9356. https://doi.org/10.1007/s00330-023-09909-1
Tian X, Song Q, Xie F, Ren L, Zhang Y, Tang J, Zhang Y, Jin Z, Zhu Y, Zhang M, Luo Y (2020) Papillary thyroid carcinoma: an ultrasound-based nomogram improves the prediction of lymph node metastases in the central compartment. Eur Radiol 30:5881–5893. https://doi.org/10.1007/s00330-020-06906-6
Comments (0)