Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Imaging
Published in: BMC Medical Imaging 1/2023

Open Access 01-12-2023 | Ultrasound | Research

Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Authors: Xiang Yan, Xurong Mou, Yanan Yang, Jing Ren, Xingxu Zhou, Yifei Huang, Hongmei Yuan

Published in: BMC Medical Imaging | Issue 1/2023

Login to get access

Abstract

Objectives

To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).

Method

A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

Results

A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924–0.982) and 0.966 (95% CI, 0.901–0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model.

Conclusion

Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.CrossRefPubMed
2.
Megwalu UC, Moon PK. Thyroid Cancer Incidence and Mortality Trends in the United States: 2000–2018. Thyroid. 2022;32(5):560–70.PubMedCrossRef
3.
Deng Y, Zhang J, Wang J, Wang J, Zhang J, Guan L et al. Risk factors and prediction models of lymph node metastasis in papillary thyroid carcinoma based on clinical and imaging characteristics. Postgrad Med. 2022:1–7.
4.
Ito Y, Higashiyama T, Takamura Y, Kobayashi K, Miya A, Miyauchi A. Prognosis of patients with papillary thyroid carcinoma showing postoperative recurrence to the central neck. World J Surg. 2011;35(4):767–72.PubMedCrossRef
5.
6.
Lundgren CI, Hall P, Dickman PW, Zedenius J. Clinically significant prognostic factors for differentiated thyroid carcinoma: a population-based, nested case-control study. Cancer. 2006;106(3):524–31.PubMedCrossRef
7.
Parvathareddy SK, Siraj AK, Ahmed SO, DeVera F, Al-Sobhi SS, Al-Dayel F, et al. Risk factors for Central Lymph Node Metastases and Benefit of Prophylactic Central Lymph Node Dissection in Middle Eastern patients with cN0 papillary thyroid carcinoma. Front Oncol. 2021;11:819824.PubMedCrossRef
8.
Zhao W, You L, Hou X, Chen S, Ren X, Chen G, et al. The Effect of Prophylactic Central Neck dissection on Locoregional recurrence in papillary thyroid Cancer after total thyroidectomy: a systematic review and Meta-analysis: pCND for the Locoregional recurrence of papillary thyroid Cancer. Ann Surg Oncol. 2017;24(8):2189–98.PubMedCrossRef
9.
Liu C, Xiao C, Chen J, Li X, Feng Z, Gao Q, et al. Risk factor analysis for predicting cervical lymph node metastasis in papillary thyroid carcinoma: a study of 966 patients. BMC Cancer. 2019;19(1):622.PubMedPubMedCentralCrossRef
10.
Li F, Pan D, He Y, Wu Y, Peng J, Li J, et al. Using ultrasound features and radiomics analysis to predict lymph node metastasis in patients with thyroid cancer. BMC Surg. 2020;20(1):315.PubMedPubMedCentralCrossRef
11.
Jiang LH, Yin KX, Wen QL, Chen C, Ge MH, Tan Z. Predictive risk-scoring Model for Central Lymph Node Metastasis and Predictors of recurrence in papillary thyroid carcinoma. Sci Rep. 2020;10(1):710.PubMedPubMedCentralCrossRef
12.
Lee DW, Ji YB, Sung ES, Park JS, Lee YJ, Park DW, et al. Roles of ultrasonography and computed tomography in the surgical management of cervical lymph node metastases in papillary thyroid carcinoma. Eur J Surg Oncol. 2013;39(2):191–6.PubMedCrossRef
13.
Khokhar MT, Day KM, Sangal RB, Ahmedli NN, Pisharodi LR, Beland MD, et al. Preoperative High-Resolution Ultrasound for the Assessment of Malignant Central Compartment Lymph nodes in papillary thyroid Cancer. Thyroid. 2015;25(12):1351–4.PubMedCrossRef
14.
Shim MJ, Roh JL, Gong G, Choi KJ, Lee JH, Cho SH, et al. Preoperative detection and predictors of level V lymph node metastasis in patients with papillary thyroid carcinoma. Br J Surg. 2013;100(4):497–503.PubMedCrossRef
15.
Morita S, Mizoguchi K, Suzuki M, Iizuka K. The accuracy of (18)[F]-fluoro-2-deoxy-d-glucose-positron emission tomography/computed tomography, ultrasonography, and enhanced computed tomography alone in the preoperative diagnosis of cervical lymph node metastasis in patients with papillary thyroid carcinoma. World J Surg. 2010;34(11):2564–9.PubMedCrossRef
16.
Zhou SC, Liu TT, Zhou J, Huang YX, Guo Y, Yu JH, et al. An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary thyroid carcinoma. Front Oncol. 2020;10:1591.PubMedPubMedCentralCrossRef
17.
Tong Y, Zhang J, Wei Y, Yu J, Zhan W, Xia H, et al. Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study. BMC Med Imaging. 2022;22(1):82.PubMedPubMedCentralCrossRef
18.
Zhao H, Li H. Meta-analysis of ultrasound for cervical lymph nodes in papillary thyroid cancer: diagnosis of central and lateral compartment nodal metastases. Eur J Radiol. 2019;112:14–21.PubMedCrossRef
19.
Gao X, Luo W, He L, Cheng J, Yang L. Predictors and a prediction model for Central Cervical Lymph Node Metastasis in Papillary thyroid carcinoma (cN0). Front Endocrinol (Lausanne). 2021;12:789310.PubMedCrossRef
20.
Liu C, Xiao C, Chen J, Li X, Feng Z, Gao Q et al. Risk factor analysis for predicting cervical lymph node metastasis in papillary thyroid carcinoma: a study of 966 patients. 2019;19(1):622.
21.
Duron L, Savatovsky J, Fournier L, Lecler A. Can we use radiomics in ultrasound imaging? Impact of preprocessing on feature repeatability. Diagn Interv Imaging. 2021;102(11):659–67.PubMedCrossRef
22.
Zwanenburg A, Vallieres M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, et al. The image Biomarker Standardization Initiative: standardized quantitative Radiomics for High-Throughput Image-based phenotyping. Radiology. 2020;295(2):328–38.PubMedCrossRef
23.
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–73.PubMedCrossRef
24.
Ito Y, Miyauchi A, Kihara M, Fukushima M, Higashiyama T, Miya A. Overall survival of papillary thyroid carcinoma patients: a Single-Institution Long-Term Follow-Up of 5897 patients. World J Surg. 2018;42(3):615–22.PubMedPubMedCentralCrossRef
25.
Randolph GW, Duh QY, Heller KS, LiVolsi VA, Mandel SJ, Steward DL, et al. The prognostic significance of nodal metastases from papillary thyroid carcinoma can be stratified based on the size and number of metastatic lymph nodes, as well as the presence of extranodal extension. Thyroid. 2012;22(11):1144–52.PubMedCrossRef
26.
Machens A, Hinze R, Thomusch O, Dralle H. Pattern of nodal metastasis for primary and reoperative thyroid cancer. World J Surg. 2002;26(1):22–8.PubMedCrossRef
27.
Kim E, Park JS, Son KR, Kim JH, Jeon SJ, Na DG. Preoperative diagnosis of cervical metastatic lymph nodes in papillary thyroid carcinoma: comparison of ultrasound, computed tomography, and combined ultrasound with computed tomography. Thyroid. 2008;18(4):411–8.PubMedCrossRef
28.
Iqbal MA, Wang X, Guoliang Z, Moazzam NF, Shahid AD, Qian X, et al. A comparison of the efficiency of diagnostic ultrasound and magnetic resonance imaging of cervical lymph nodes in papillary thyroid carcinoma. J Xray Sci Technol. 2021;29(6):1033–44.PubMed
29.
Guo L, Ma YQ, Yao Y, Wu M, Deng ZH, Zhu FW, et al. Role of ultrasonographic features and quantified BRAFV600E mutation in lymph node metastasis in chinese patients with papillary thyroid carcinoma. Sci Rep. 2019;9(1):75.PubMedPubMedCentralCrossRef
30.
Chen L, Wu YH, Lee CH, Chen HA, Loh EW, Tam KW. Prophylactic Central Neck dissection for papillary thyroid carcinoma with clinically Uninvolved Central Neck Lymph Nodes: a systematic review and Meta-analysis. World J Surg. 2018;42(9):2846–57.PubMedCrossRef
31.
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):1–133.PubMedPubMedCentralCrossRef
32.
Lacout A, Chevenet C, Marcy PY. Mummified thyroid syndrome. AJR Am J Roentgenol. 2016;206(4):837–45.PubMedCrossRef
33.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.PubMedPubMedCentralCrossRef
34.
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.PubMedPubMedCentralCrossRef
35.
Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, et al. Development and validation of a preoperative magnetic resonance imaging Radiomics-Based signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in patients with early-stage breast Cancer. JAMA Netw Open. 2020;3(12):e2028086.PubMedPubMedCentralCrossRef
36.
Zhao B, Zhu HT, Li XT, Shi YJ, Cao K, Sun YS. Predicting Lymph Node Metastasis using computed Tomography Radiomics analysis in patients with resectable esophageal squamous cell carcinoma. J Comput Assist Tomogr. 2021;45(2):323–29.PubMedCrossRef
37.
Li T, Li H, Xue J, Miao J, Kang C. Shear wave elastography combined with gray-scale ultrasound for predicting central lymph node metastasis of papillary thyroid carcinoma. Surg Oncol. 2021;36:1–6.PubMedCrossRef
38.
Feng JW, Hong LZ, Wang F, Wu WX, Hu J, Liu SY, et al. A Nomogram based on clinical and Ultrasound characteristics to Predict Central Lymph Node Metastasis of Papillary thyroid carcinoma. Front Endocrinol (Lausanne). 2021;12:666315.PubMedCrossRef
39.
Zhou Y, Su GY, Hu H, Tao XW, Ge YQ, Si Y, et al. Radiomics from primary tumor on dual-energy CT derived Iodine Maps can predict cervical lymph node metastasis in papillary thyroid Cancer. Acad Radiol. 2022;29(Suppl 3):222–S31.CrossRef
40.
Li J, Wu X, Mao N, Zheng G, Zhang H, Mou Y, et al. Computed tomography-based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary thyroid carcinoma: a Multicenter Study. Front Endocrinol (Lausanne). 2021;12:741698.PubMedCrossRef
41.
Abbasian Ardakani A, Mohammadi A, Mirza-Aghazadeh-Attari M, Faeghi F, Vogl TJ, Acharya UR. Diagnosis of metastatic lymph nodes in patients with papillary thyroid Cancer: a comparative Multi-Center Study of Semantic features and deep learning-based models. J Ultrasound Med. 2023;42(6):1211–21.PubMedCrossRef
42.
Liu Y, Lai F, Lin B, Gu Y, Chen L, Chen G, et al. Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study. EClinicalMedicine. 2023;60:102007.PubMedPubMedCentralCrossRef
43.
Chang L, Zhang Y, Zhu J, Hu L, Wang X, Zhang H, et al. An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: a multicenter study. Front Endocrinol (Lausanne). 2023;14:964074.PubMedCrossRef
44.
Wang Z, Qu L, Chen Q, Zhou Y, Duan H, Li B, et al. Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer. BMC Cancer. 2023;23(1):128.PubMedPubMedCentralCrossRef
Metadata
Title
Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis
Authors
Xiang Yan
Xurong Mou
Yanan Yang
Jing Ren
Xingxu Zhou
Yifei Huang
Hongmei Yuan
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2023
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-023-01085-4

Other articles of this Issue 1/2023

BMC Medical Imaging 1/2023 Go to the issue

Research

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions

Research

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

Research

Enhanced CT combined with texture analysis for differential diagnosis of pleomorphic adenoma and adenolymphoma

Research

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

Research

Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image

Research

Detecting lesion-specific ischemia in patients with coronary artery disease with computed tomography fractional flow reserve measured at different sites