Suppr超能文献

滤泡性甲状腺肿瘤恶性风险的大小特异性预测指标:机器学习分析

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.

作者信息

Li Xin, Yang Wen-Yu, Zhang Fan, Shan Rui, Mei Fang, Song Shi-Bing, Sun Bang-Kai, Chen Jing, Hu Run-Ze, Yang Yang, Yang Yi-Hang, Liu Jing-Yao, Yuan Chun-Hui, Liu Zheng

机构信息

Department of General Surgery, Peking University Third Hospital, Beijing, China.

China Center for Health Development Studies, Peking University, Beijing, China.

出版信息

JMIR Cancer. 2025 Jul 11;11:e73069. doi: 10.2196/73069.

Abstract

BACKGROUND

Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed.

OBJECTIVE

This study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively.

METHODS

A retrospective cohort study was conducted at Peking University Third Hospital in Beijing, China, from 2012 to 2023. Patients with a postoperative pathological diagnosis of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included. FTNs were classified into small- and large-sized categories based on the cutoff value of the tumor diameter derived from spline regression, which indicated the turning point of malignancy risk. We identified the 5 most important predictors from 22 variables including demography, sonography, and hormones, using machine learning methods. We also calculated the odds ratios (OR) with 95% CI for these predictors in both small- and large-sized FTNs.

RESULTS

Altogether, we included 1494 FTNs, comprising 1266 FTAs and 228 FTCs. FTNs with a maximum diameter less than 3.0 cm were grouped as small-sized tumors (n=715), while those with larger diameters were categorized as large-sized tumors (n=779). In the small-sized group, tumors with macrocalcification (OR 2.90, 95% CI 1.50-5.60), those with peripheral calcification (OR 4.50, 95% CI 1.50-13.00), and those in younger patients (OR 1.33, 95% CI 1.05-1.69) showed a higher malignancy risk. In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95% CI 1.30-7.90) exhibited a higher malignancy risk. In both groups, lower thyroid-stimulating hormone levels (OR 1.49, 95% CI 1.20-1.85 for small-sized FTNs; OR 1.61, 95% CI 1.37-1.96 for large-sized FTNs) and a larger mean diameter (OR 1.40, 95% CI 1.10-1.70 for small-sized FTNs; OR 1.50 95% CI 1.20-1.70 for large-sized FTNs) were associated with the malignancy risk of FTNs.

CONCLUSIONS

This study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size.

摘要

背景

在进行诊断性手术之前,外科医生在区分良性和恶性甲状腺滤泡性肿瘤(FTN),尤其是小肿瘤方面常常面临挑战。

目的

本研究旨在术前确定FTN恶性风险的大小特异性预测因素。

方法

2012年至2023年在中国北京的北京大学第三医院进行了一项回顾性队列研究。纳入术后病理诊断为甲状腺滤泡性腺瘤(FTA)或甲状腺滤泡癌(FTC)的患者。根据样条回归得出的肿瘤直径临界值将FTN分为小尺寸和大尺寸类别,该临界值表明恶性风险的转折点。我们使用机器学习方法从包括人口统计学、超声检查和激素在内的22个变量中确定了5个最重要的预测因素。我们还计算了这些预测因素在小尺寸和大尺寸FTN中的比值比(OR)及95%置信区间(CI)。

结果

总共纳入了1494个FTN,包括1266个FTA和228个FTC。最大直径小于3.0 cm的FTN被归为小尺寸肿瘤(n = 715),而直径较大的则被归为大尺寸肿瘤(n = 779)。在小尺寸组中,有粗大钙化的肿瘤(OR 2.90,95% CI 1.50 - 5.60)、有周边钙化的肿瘤(OR 4.50,95% CI 1.50 - 13.00)以及年轻患者的肿瘤(OR 1.33,95% CI 1.05 - 1.69)显示出较高的恶性风险。在大尺寸组中,呈现结节内结节外观的肿瘤(OR 3.30,95% CI 1.30 - 7.90)表现出较高的恶性风险。在两组中,较低的促甲状腺激素水平(小尺寸FTN的OR 1.49,95% CI 1.20 - 1.85;大尺寸FTN的OR 1.61,95% CI 1.37 - 1.96)和较大的平均直径(小尺寸FTN的OR 1.40,95% CI 1.10 - 1.70;大尺寸FTN的OR 1.50,95% CI 1.20 - 1.70)与FTN的恶性风险相关。

结论

本研究确定了FTN恶性风险的大小特异性预测因素,强调了基于肿瘤大小进行分层预测的重要性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验