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Multicenter Study
. 2023 Feb 21:14:964074.
doi: 10.3389/fendo.2023.964074. eCollection 2023.

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study

Luchen Chang  1 , Yanqiu Zhang  1 , Jialin Zhu  1 , Linfei Hu  2 , Xiaoqing Wang  1 , Haozhi Zhang  2 , Qing Gu  3 , Xiaoyu Chen  1 , Sheng Zhang  1 , Ming Gao  2   4 , Xi Wei  1
Affiliations
Multicenter Study

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study

Luchen Chang et al. Front Endocrinol (Lausanne). .

Abstract

Objective: Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM.

Materials and methods: In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients.

Results: Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability.

Conclusion: Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

Keywords: central lymph node metastasis; deep learning; nomogram; papillary thyroid carcinoma; ultrasound.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of the thyroid cancer lymph node metastasis nomogram.
Figure 2
Figure 2
Thyroid cancer lymph node metastasis nomogram for predicting CLNM in PTC patients.
Figure 3
Figure 3
Performance of the thyroid cancer lymph node metastasis nomogram, thyroid cancer lymph node metastasis AI system, clinical model and US-reported CLN status in PTC patients. (A) ROC curve for predicting CLNM in the training cohort. (B) ROC curve for predicting CLNM in the internal validation cohort. (C) ROC curve for predicting CLNM in the external validation cohort.
Figure 4
Figure 4
Calibration curves of the thyroid cancer lymph node metastasis nomogram, thyroid cancer lymph node metastasis AI system, clinical model and US-reported CLN status in PTC patients. (A) Calibration curves for predicting CLNM in the training cohort. (B) Calibration curves for predicting CLNM in the internal validation cohort. (C) Calibration curves for predicting CLNM in the external validation cohort.
Figure 5
Figure 5
Decision curve analysis of the thyroid cancer lymph node metastasis nomogram, thyroid cancer lymph node metastasis AI system, clinical model and US-reported CLN status in PTC patients. (A) Decision curve analysis for predicting CLNM in the training cohort. (B) Decision curve analysis for predicting CLNM in the internal validation cohort. (C) Decision curve analysis for predicting CLNM in the external validation cohort.
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References

    1. Byrd JK, Yawn RJ, Wilhoit CST, Sora ND, Meyers L, Fernandes J, et al. . Well differentiated thyroid carcinoma: current treatment. Curr Treat options Oncol (2012) 13(1):47–57. doi: 10.1007/s11864-011-0173-1 - DOI - PubMed
    1. Lim H, Devesa SS, Sosa JA, Check D, Kitahara CM. Trends in thyroid cancer incidence and mortality in the united states, 1974-2013. JAMA (2017) 317(13):1338–48. doi: 10.1001/jama.2017.2719 - DOI - PMC - PubMed
    1. Morris LGT, Sikora AG, Tosteson TD, Davies L. The increasing incidence of thyroid cancer: The influence of access to care. Thyroid: Off J Am Thyroid Assoc (2013) 23(7):885–91. doi: 10.1089/thy.2013.0045 - DOI - PMC - PubMed
    1. Yu QA, Ma DK, Liu KP, Wang P, Xie CM, Wu YH, et al. . Clinicopathologic risk factors for right paraesophageal lymph node metastasis in patients with papillary thyroid carcinoma. J endocrinol Invest (2018) 41(11):1333–8. doi: 10.1007/s40618-018-0874-4 - DOI - PubMed
    1. Pereira JA, Jimeno J, Miquel J, Iglesias M, Munné A, Sancho JJ, et al. . Nodal yield, morbidity, and recurrence after central neck dissection for papillary thyroid carcinoma. Surgery (2005) 138(6):1095–100, discussion 1100-1. doi: 10.1016/j.surg.2005.09.013 - DOI - PubMed

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