Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

IF 3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Endocrine Pub Date : 2024-11-18 DOI:10.1007/s12020-024-04091-w

Tingting Qian, Yahan Zhou, Jincao Yao, Chen Ni, Sohaib Asif, Chen Chen, Lujiao Lv, Di Ou, Dong Xu

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引用次数: 0

Abstract

Background: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.

Methods: Ultrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.

Results: In the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.

Conclusions: This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.

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基于深度学习的动态视频超声分析预测甲状腺乳头状癌的颈淋巴结转移

背景：颈淋巴结转移（CLNM）是甲状腺癌最常见的转移形式。对于甲状腺乳头状癌（PTC）患者来说，术前准确诊断CLNM更为重要。本研究旨在开发一种深度学习（DL）模型，帮助临床医生更准确地判断PTC患者是否存在CLNM风险，进而协助他们做出治疗决策：方法：回顾性收集了浙江省肿瘤医院2020年1月至2022年6月期间388例717个甲状腺结节患者的超声动态视频。研究了五个深度学习（DL）模型，以检验其预测CLNM风险的有效性，并将其性能与使用二维超声静态图像预测的性能进行了比较：在测试数据集（n = 78）中，根据超声动态视频训练的 DenseNet121 模型在所有指标上都优于其他四个 DL 模型以及使用二维（2D）静态图像训练的 DL 模型。具体来说，使用 DenseNet121，三维模型和二维模型在所有指标上的比较如下：AUROC：0.903 对 0.828，灵敏度：0.877 对 0.871，特异性：0.865 对 0.659：这项研究表明，DenseNet 121 模型在区分 PTC 患者的 CLNM 和非 CLNM 方面具有最大的潜力。动态视频还提供了更多有关疾病状态的信息，事实证明，与静态图像相比，动态视频在识别 CLNM 方面更有效、更稳健。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Endocrine ENDOCRINOLOGY & METABOLISM-

CiteScore

6.50

自引率

5.40%

发文量

295

审稿时长

1.5 months

期刊介绍： Well-established as a major journal in today’s rapidly advancing experimental and clinical research areas, Endocrine publishes original articles devoted to basic (including molecular, cellular and physiological studies), translational and clinical research in all the different fields of endocrinology and metabolism. Articles will be accepted based on peer-reviews, priority, and editorial decision. Invited reviews, mini-reviews and viewpoints on relevant pathophysiological and clinical topics, as well as Editorials on articles appearing in the Journal, are published. Unsolicited Editorials will be evaluated by the editorial team. Outcomes of scientific meetings, as well as guidelines and position statements, may be submitted. The Journal also considers special feature articles in the field of endocrine genetics and epigenetics, as well as articles devoted to novel methods and techniques in endocrinology. Endocrine covers controversial, clinical endocrine issues. Meta-analyses on endocrine and metabolic topics are also accepted. Descriptions of single clinical cases and/or small patients studies are not published unless of exceptional interest. However, reports of novel imaging studies and endocrine side effects in single patients may be considered. Research letters and letters to the editor related or unrelated to recently published articles can be submitted. Endocrine covers leading topics in endocrinology such as neuroendocrinology, pituitary and hypothalamic peptides, thyroid physiological and clinical aspects, bone and mineral metabolism and osteoporosis, obesity, lipid and energy metabolism and food intake control, insulin, Type 1 and Type 2 diabetes, hormones of male and female reproduction, adrenal diseases pediatric and geriatric endocrinology, endocrine hypertension and endocrine oncology.