External Validation of a Predictive Model for Thyroid Cancer Risk with Decision Curve Analysis.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Juan Jesús Fernández Alba, Florentino Carral, Carmen Ayala Ortega, Jose Diego Santotoribio, María Castillo Lara, Carmen González Macías
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Abstract

Background/Objectives: Thyroid cancer ranks among the most prevalent endocrine neoplasms, with a significant rise in incidence observed in recent decades, particularly in papillary thyroid carcinoma (PTC). This increase is largely attributed to the enhanced detection of subclinical cancers through advanced imaging techniques and fine-needle aspiration biopsies. The present study aims to externally validate a predictive model previously developed by our group, designed to assess the risk of a thyroid nodule being malignant. Methods: By utilizing clinical, analytical, ultrasound, and histological data from patients treated at the Puerto Real University Hospital, this study seeks to evaluate the performance of the predictive model in a distinct dataset and perform a decision curve analysis to ascertain its clinical utility. Results: A total of 455 patients with thyroid nodular pathology were studied. Benign nodular pathology was diagnosed in 357 patients (78.46%), while 98 patients (21.54%) presented with a malignant tumor. The most frequent histological type of malignant tumor was papillary cancer (71.4%), followed by follicular cancer (6.1%). Malignant nodules were predominantly solid (95.9%), hypoechogenic (72.4%), with irregular or microlobed borders (36.7%), and associated with suspicious lymph nodes (24.5%). The decision curve analysis confirmed the model's accuracy and its potential impact on clinical decision-making. Conclusions: The external validation of our predictive model demonstrates its robustness and generalizability across different populations and clinical settings. The integration of advanced diagnostic tools, such as AI and ML models, improves the accuracy in distinguishing between benign and malignant nodules, thereby optimizing treatment strategies and minimizing invasive procedures. This approach not only facilitates the early detection of cancer but also helps to avoid unnecessary surgeries and biopsies, ultimately reducing patient morbidity and healthcare costs.

决策曲线分析对甲状腺癌风险预测模型的外部验证。
背景/目的:甲状腺癌是最常见的内分泌肿瘤之一,近几十年来发病率显著上升,尤其是甲状腺乳头状癌(PTC)。这一增长主要归因于通过先进的成像技术和细针穿刺活检加强了对亚临床癌症的检测。本研究旨在从外部验证我们小组先前开发的预测模型,该模型旨在评估甲状腺结节恶性风险。方法:通过利用在Puerto Real大学医院接受治疗的患者的临床、分析、超声和组织学数据,本研究旨在评估预测模型在不同数据集中的性能,并进行决策曲线分析以确定其临床实用性。结果:共对455例甲状腺结节性病理患者进行了研究。良性结节357例(78.46%),恶性肿瘤98例(21.54%)。恶性肿瘤最常见的组织学类型是乳头状癌(71.4%),其次是滤泡癌(6.1%)。恶性结节以实性为主(95.9%),低回声(72.4%),边界不规则或微叶状(36.7%),伴有可疑淋巴结(24.5%)。决策曲线分析证实了模型的准确性及其对临床决策的潜在影响。结论:我们的预测模型的外部验证证明了其在不同人群和临床环境中的稳健性和通用性。人工智能和机器学习模型等先进诊断工具的整合,提高了良恶性结节区分的准确性,从而优化治疗策略,最大限度地减少了侵入性手术。这种方法不仅有助于早期发现癌症,还有助于避免不必要的手术和活检,最终降低患者的发病率和医疗成本。
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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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