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

IF 3.3 Q2 ONCOLOGY
JMIR Cancer Pub Date : 2025-07-11 DOI:10.2196/73069
Xin Li, Wen-Yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-Ze Hu, Yang Yang, Yi-Hang Yang, Jing-Yao Liu, Chun-Hui Yuan, Zheng Liu
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引用次数: 0

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.

滤泡性甲状腺肿瘤恶性风险的大小特异性预测因子:机器学习分析。
背景:在进行诊断性手术之前,外科医生经常面临区分良性和恶性滤泡性甲状腺肿瘤(ftn),特别是小肿瘤的挑战。目的:本研究旨在确定FTNs术前恶性风险的大小特异性预测因素。方法:2012 - 2023年在北京大学第三医院进行回顾性队列研究。术后病理诊断为滤泡性甲状腺腺瘤(FTA)或滤泡性甲状腺癌(FTC)的患者纳入研究。根据样条回归得到的肿瘤直径截断值将ftn分为小、大两类,该截断值标志着恶性风险的转折点。我们使用机器学习方法,从22个变量中确定了5个最重要的预测因素,包括人口统计、超声检查和激素。我们还计算了这些预测因子在小型和大型ftn中的95% CI的比值比(OR)。结果:我们总共纳入了1494个ftn,包括1266个FTAs和228个FTCs。最大直径小于3.0 cm的FTNs为小肿瘤(n=715),直径较大的FTNs为大肿瘤(n=779)。在小尺寸组中,伴有大钙化的肿瘤(OR 2.90, 95% CI 1.50-5.60)、伴有外周钙化的肿瘤(OR 4.50, 95% CI 1.50-13.00)和年轻患者的肿瘤(OR 1.33, 95% CI 1.05-1.69)表现出更高的恶性肿瘤风险。在大肿瘤组中,表现为结节中结节的肿瘤(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(OR 1.49, 95% CI 1.20-1.85;大尺寸ftn的OR为1.61,95% CI为1.37-1.96),平均直径较大(小尺寸ftn的OR为1.40,95% CI为1.10-1.70;OR为1.50 (95% CI为1.20-1.70)与ftn的恶性风险相关。结论:本研究确定了ftn恶性肿瘤风险的大小特异性预测因子,强调了基于肿瘤大小分层预测的重要性。
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
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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