机器学习已越来越多地用于开发可改善医学诊断和预测的算法，并在改善甲状腺超声图像的分类方面显示出希望。这项概念验证研究旨在开发一种多模式机器学习模型来对滤泡性癌和腺瘤进行分类。这是一项对 2010 年至 2022 年间在单一机构患有滤泡性腺瘤或癌的患者进行的回顾性研究。人口统计学、影像学和围手术期收集了变量。感兴趣的区域在超声上注释并用于进行放射组学分析。然后使用影像特征和临床变量创建随机森林分类器来预测恶性肿瘤。进行留一法交叉验证，使用受试者工作特征曲线下的面积来评估分类器性能。包括具有完整影像和围手术期数据的滤泡性腺瘤 (n = 7) 和癌症 (n = 11) 患者。每幅图像总共提取了 910 个特征。 t 分布随机邻域嵌入方法将维度减少到 2 个主要表示分量。随机森林分类器的接受者操作特征曲线下面积分别为 0.76（仅临床）、0.29（仅图像）和 0.79（多模态数据）。我们的多模态机器学习模型在滤泡性癌和腺瘤的分类方面显示出有希望的结果。这种方法有可能应用于未来的研究中，以生成滤泡性甲状腺肿瘤术前分化的模型。版权所有 © 2023 Elsevier Inc. 保留所有权利。

Machine learning has been increasingly used to develop algorithms that can improve medical diagnostics and prognostication and has shown promise in improving the classification of thyroid ultrasound images. This proof-of-concept study aims to develop a multimodal machine-learning model to classify follicular carcinoma from adenoma.This is a retrospective study of patients with follicular adenoma or carcinoma at a single institution between 2010 and 2022. Demographics, imaging, and perioperative variables were collected. The region of interest was annotated on ultrasound and used to perform radiomics analysis. Imaging features and clinical variables were then used to create a random forest classifier to predict malignancy. Leave-one-out cross-validation was conducted to evaluate classifier performance using the area under the receiver operating characteristic curve.Patients with follicular adenomas (n = 7) and carcinomas (n = 11) with complete imaging and perioperative data were included. A total of 910 features were extracted from each image. The t-distributed stochastic neighbor embedding method reduced the dimension to 2 primary represented components. The random forest classifier achieved an area under the receiver operating characteristic curve of 0.76 (clinical only), 0.29 (image only), and 0.79 (multimodal data).Our multimodal machine learning model demonstrates promising results in classifying follicular carcinoma from adenoma. This approach can potentially be applied in future studies to generate models for preoperative differentiation of follicular thyroid neoplasms.Copyright © 2023 Elsevier Inc. All rights reserved.