Performance of Ct-Based Deep Learning in Diagnostic Assessment of Suspicious Lateral Lymph Nodes in Papillary Thyroid Cancer: A Prospective Diagnostic Study

Authors

Guibin Zheng
Haicheng Zhang
Fusheng Lin
Mark Zafereo
Neil Gross
Peng Sun
Yang Liu
Haiqing Sun
Guochang Wu
Shujian Wei
Jia Wu
Ning Mao
Guojun Li
Guoyang Wu
Haitao Zheng
Xicheng Song

Publication Date

11-1-2023

Journal

International Journal of Surgery

DOI

10.1097/JS9.0000000000000660

PMID

37578434

PMCID

PMC10651261

PubMedCentral® Posted Date

11-1-2023

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer is challenging. Strategies for using deep learning to diagnosis of lateral LN metastasis require additional development and testing. This study aimed to build a deep learning-based model to distinguish benign lateral LNs from metastatic lateral LNs in papillary thyroid cancer and test the model's diagnostic performance in a real-world clinical setting.

Methods: This was a prospective diagnostic study. An ensemble model integrating a three-dimensional residual network algorithm with clinical risk factors available before surgery was developed based on computed tomography images of lateral LNs in an internal dataset and validated in two external datasets. The diagnostic performance of the ensemble model was tested and compared with the results of fine-needle aspiration (FNA) (used as the standard reference method) and the diagnoses made by two senior radiologists in 113 suspicious lateral LNs in patients enrolled prospectively.

Results: The area under the receiver operating characteristic curve of the ensemble model for diagnosing suspicious lateral LNs was 0.829 (95% CI: 0.732-0.927). The sensitivity and specificity of the ensemble model were 0.839 (95% CI: 0.762-0.916) and 0.769 (95% CI: 0.607-0.931), respectively. The diagnostic accuracy of the ensemble model was 82.3%. With FNA results as the criterion standard, the ensemble model had excellent diagnostic performance ( P =0.115), similar to that of the two senior radiologists ( P =1.000 and P =0.392, respectively).

Conclusion: A three-dimensional residual network-based ensemble model was successfully developed for the diagnostic assessment of suspicious lateral LNs and achieved diagnostic performance similar to that of FNA and senior radiologists. The model appears promising for clinical application.

Keywords

Humans, Thyroid Cancer, Papillary, Thyroid Neoplasms, Prospective Studies, Deep Learning, Lymphatic Metastasis, Lymph Nodes, Tomography, X-Ray Computed, Retrospective Studies

Published Open-Access

yes

Recommended Citation

Zheng, Guibin; Zhang, Haicheng; Lin, Fusheng; et al., "Performance of Ct-Based Deep Learning in Diagnostic Assessment of Suspicious Lateral Lymph Nodes in Papillary Thyroid Cancer: A Prospective Diagnostic Study" (2023). Faculty, Staff and Student Publications. 3558.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/3558