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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Background:

Dissection of the lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLNs) in papillary thyroid cancer (PTC) remains controversial.

Objective:

This study aimed to determine the capability of ultrasonography (US)-based radiomics for presurgical prediction of metastasis in LN-prRLNs in PTC.

Methods:

Patients were retrospectively enrolled and pathologically confirmed as LN-prRLN metastasis with PTC after surgery. Radiomic analysis based on preoperative US images with manual segmentation of targets was used to develop a radiomics model. US features described in ACR TI-RADS were collected to construct a clinical model. The Radiomics model, a combined model integrating radiomics and clinical model, were also developed for the presurgical prediction of metastasis in LN-prRLNs.

Results:

A total of 570 patients, including 488 patients with non-LN-prRLN metastasis and 82 with LN-prRLN metastasis, were assessed. The 15 top-performing features finally remained significant for constructing the radiomics model. The combined model showed that US measured tumor size (OR: 1.036, = 0.044), US suspected lateral lymph node metastasis (OR: 2.247, = 0.009), multifocality (OR: 1.920, = 0.021), Delphian lymph node metastasis (DLNM) (OR: 2.300, = 0.039), VIa compartment metastasis (OR: 5.357, = 0.000), the radiomics score (OR: 1.003, = 0.001) were significant risk factors for predicting LN-prRLN metastasis. The combined model achieved a higher AUC of 0.849 than that of the clinical model (AUC: 0.826) and radiomics model (AUC: 0.759).

Conclusion:

The US-based radiomics combined model can more effectively predict LN-prRLN metastasis in PTCs patients preoperatively. This approach had the potential to assist surgeons in decision-making regarding LN-prRLN dissection.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023-10-31
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