Xu Changyu, Zhang Liwei, Zhang Qiming, Wang Tianqi, Wu Yuqing, Yao Jinlai, Dong Xiaoqiu
Department of Medical Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Front Oncol. 2024 Oct 24;14:1439825. doi: 10.3389/fonc.2024.1439825. eCollection 2024.
The incidence of papillary thyroid caracinoma (PTC) is increasing year by year. Logistic regression model and Chi-squared automatic interaction (CHAID) decision tree based on multimodal ultrasound were established, and the diagnostic efficiency of the two models in PTC was compared.
The findings, features and data of routine ultrasound, shear wave elastography (SWE) and contrast-enhanced ultrasonography (CEUS) were prospectively collected in 203 patients. Including: echogenicity, aspect ratio, maximum diameter of tumor, boundary, morphology, focal hyperecho, blood flow grading, maximum elasticity (E), minimum elastcity (E), mean elasticity (E), enhancement degree, enhanced characteristics, distribution of contrast agent, contrast medium arrival time. According to the pathological results, they were divided into PTC group and non-PTC group. CHAID decision tree model and binary Logistic regression model were established, receiver operator characteristic (ROC) curves of the two models were drawn, and diagnostic effectiveness was evaluated by comparing area under curve (AUC).
Logistic regression showed that hypoechoic or very hypoechoic, aspect ratio ≥1, microcalcification and high SWE value were risk factors for PTC (OR 8.604, 2.154, 2.297, 1.067, respectively, P < 0.05). The CHAID decision tree showed echo, aspect ratio, E, contrast agent distribution and infusion time combined to diagnose PTC. ROC curve showed that the AUC of PTC predicted by Logistic regression model and CHAID decision tree model was 0.878 and 0.883, respectively, with no statistical significance (z=0.325, P=0.7456).
Both Logistic regression model and CHAID decision tree model can play a good role in the diagnosis of PTC based on multi-modal ultrasound, but the diagnostic efficiency of both models is comparable. In conclusion, these two models provide new insights and ideas for PTC diagnosis.
甲状腺乳头状癌（PTC）的发病率逐年上升。建立基于多模态超声的逻辑回归模型和卡方自动交互检测（CHAID）决策树，并比较两种模型对PTC的诊断效能。
前瞻性收集203例患者的常规超声、剪切波弹性成像（SWE）及超声造影（CEUS）的表现、特征及数据。包括：回声、纵横比、肿瘤最大径、边界、形态、局灶性高回声、血流分级、最大弹性值（E）、最小弹性值（E）、平均弹性值（E）、增强程度、增强特征、造影剂分布、造影剂到达时间。根据病理结果分为PTC组和非PTC组。建立CHAID决策树模型和二元逻辑回归模型，绘制两种模型的受试者操作特征（ROC）曲线，通过比较曲线下面积（AUC）评估诊断效能。
逻辑回归显示低回声或极低回声、纵横比≥1、微钙化及SWE值高是PTC的危险因素（OR分别为8.604、2.154、2.297、1.067，P<0.05）。CHAID决策树显示回声、纵横比、E、造影剂分布及灌注时间联合诊断PTC。ROC曲线显示逻辑回归模型和CHAID决策树模型预测PTC的AUC分别为0.878和0.883，差异无统计学意义（z=0.325，P=0.7456）。
逻辑回归模型和CHAID决策树模型基于多模态超声对PTC诊断均能发挥较好作用，但两种模型诊断效能相当。总之，这两种模型为PTC诊断提供了新的思路和方法。
