人工智能在甲状腺结节超声中的应用,I-131 摄取的预测

A. V. Manaev, A. A. Trukhin, S. M. Zakharova, M. S. Sheremeta, E. A. Troshina
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引用次数: 0

摘要

背景:甲状腺结节是一个普遍存在的问题,根据超声波检查估计其发病率为19%至35%,根据尸检结果估计其发病率为8%至65%[1]。在某些病例中,可观察到普鲁默氏病,在 10%至 35%的巴塞杜氏病病例中可观察到结节性肿块,并伴有不同性质的碘蓄积[2, 3]。放射性碘治疗是治疗巴塞杜氏病和普卢默病的主要方法之一,其作用是排除结节中恶性肿瘤的可能性。此外,还要对碘的药代动力学进行研究,这是准备放射性碘治疗最耗时耗力的阶段。在临床实践中,根据 TI-RADS 系统进行超声波检查,然后(如有必要)根据贝塞斯达系统分层进行细针穿刺活检。然而,超声检查的判读本身具有主观性,而决策支持系统的使用可将细针穿刺活检的次数减少 27%,将漏检的恶性肿瘤数量减少 1.9%。此外,结节超声的定量特征描述可加强对 I-131 药代动力学的研究[4, 5]。目的:本研究旨在开发一种定量表征甲状腺结节肿块超声图像的方法,以预测结节肿块的恶性程度和 I-131 积累情况。材料与方法:研究对象包括俄罗斯科学基金会资助项目(编号 22-15-00135)中接受放射碘治疗的患者的 125 个有病理形态学检查结果的结节(65 个良性,60 个恶性)和 25 个良性结节(通过细胞学检查确定)。使用 GE Voluson E8(36% 的良性结节和 27% 的恶性结节)和 GE Logiq E(64% 的良性结节和 73% 的恶性结节)获得了甲状腺结节的纵向和横向投影。对使用 GE Logiq V2 设备获得的 25 个结节进行了药代动力学研究。24 小时后测定了 I-131 的蓄积指数。采用空间邻接矩阵、灰度线长度矩阵、灰度区大小矩阵和直方图来研究超声图像的特征。结果:根据最重要的特征并经过 KNN 相关性分析后建立的恶性肿瘤预测模型的诊断准确率为 72±3%,灵敏度为 73±5%,特异性为 73±5%。对 I-131 药代动力学的调查显示,24 小时后,最大直方图强度梯度(r=-0.48,p=0.08)和强度熵(r=-0.51,p=0.06)与 I-131 积累的斯皮尔曼相关系数模数最高。结论:本研究表明,将结节肿块超声图像的定量特征描述作为放射碘治疗前监测结节的工具是可行的。这样做的目的是为了随后辅助进行细针穿刺活检,并预测 24 小时后的 I-131 积累情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in ultrasound of thyroid nodules, prognosis of I-131 uptake
BACKGROUND: Thyroid nodules are a prevalent issue, with an estimated incidence of 19% to 35% based on ultrasound examination and 8% to 65% based on autopsy findings [1]. In some cases, Plummer’s disease is observed, and nodular masses may be observed in 10% to 35% of Graves’ disease cases, with iodine accumulation of a different nature [2, 3]. One of the principal treatments for Graves’ and Plummer’s diseases is radioiodine therapy, which serves to exclude the possibility of malignancy in nodules. Furthermore, the pharmacokinetics of iodine is investigated, which represents the most time-consuming and labor-intensive stage of preparation for radioiodine therapy. In clinical practice, ultrasound is performed in accordance with the TI-RADS system, followed (if necessary) by fine-needle aspiration puncture biopsy, stratified according to the Bethesda system. However, the interpretation of ultrasound examinations is inherently subjective, whereas the use of decision support systems can reduce the number of fine-needle aspiration puncture biopsies by 27% and the number of missed malignant neoplasms by 1.9%. Furthermore, the quantitative characterization of nodal ultrasound may enhance the investigation of the pharmacokinetics of I-131 [4, 5]. AIM: The study aimed to develop a method for quantitatively characterizing ultrasound images of thyroid nodular masses for predicting malignancy and I-131 accumulation by nodular masses. MATERIALS AND METHODS: The study included 125 nodules with pathomorphologic findings (65 benign, 60 malignant) and 25 benign nodules (established by cytologic examination) of patients who underwent radioiodotherapy as part of the Russian Science Foundation grant project No. 22-15-00135. Longitudinal and transverse projections of thyroid nodules were obtained using GE Voluson E8 (36% of all benign nodules and 27% of malignant nodules) and GE Logiq E (64% of benign and 73% of malignant nodules). A pharmacokinetics study was conducted on 25 nodes obtained on a GE Logiq V2 device. The accumulation index of I-131 was determined after 24 hours. A spatial adjacency matrix, gray level line length matrix, gray level zone size matrix, and histogram were employed to investigate features based on ultrasound images. RESULTS: The malignancy prediction model, developed on the basis of the most significant features and after KNN correlation analysis, exhibited a diagnostic accuracy value of 72±3%, a sensitivity of 73±5%, and a specificity of 73±5%. An investigation of I-131 pharmacokinetics revealed that the maximum histogram intensity gradient (r=–0.48, p=0.08) and intensity entropy (r=–0.51, p=0.06) exhibited the highest Spearman correlation coefficient modulus with I-131 accumulation after 24 hours. CONCLUSIONS: The present study demonstrates the feasibility of using quantitative characterization of ultrasound images of nodal masses as a tool to monitor nodules before radioiodotherapy. This is with a view to subsequent adjunctive fine-needle aspiration puncture biopsy and prediction of I-131 accumulation after 24 hours.
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