Pang Yu-Yan, Tang Zhong-Qing, Song Chang, Qu Ning, Chen Jing-Yu, Xiong Dan-Dan, Feng Zhen-Bo, Chen Gang
Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, PR China.
Department of Pathology, Wuzhou Gongren Hospital, The Seventh Affiliated Hospital of Guangxi Medical University, No. 1, Nansanxiang Gaodi Road, Wuzhou, Guangxi Zhuang Autonomous Region, 543000, PR China.
Data Brief. 2025 Apr 28;60:111583. doi: 10.1016/j.dib.2025.111583. eCollection 2025 Jun.
The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our study constructed a multimodal diagnostic model for papillary thyroid carcinoma (PTC) by integrating pathomics and ultrasound radiomics characteristics using artificial intelligence machine learning methods, aiming to improve the efficiency of pathologists. A retrospective analysis was conducted on 222 cases with postoperative diagnoses of PTC and 163 cases with postoperative diagnoses of benign thyroid nodules. Scanning was used to obtain cytopathology digitised images, cut image blocks and outline the corresponding ultrasound imaging lesions to extract pathomics and ultrasound radiomics features. Three methods, as eXtreme gradient boosting (XGBoost), support vector machine (SVM), and random forest (RF) algorithms, were applied to construct PTC cytopathological diagnostic models. The efficacy of the models was evaluated and validated with the area under receiver operating characteristic curve (AUC), and the performance of single-modal, multimodal, and artificial diagnostic models was compared.
利用机器学习整合和分析多模态信息在提高肿瘤诊断精度方面具有广阔前景。我们的研究使用人工智能机器学习方法，通过整合病理组学和超声放射组学特征，构建了甲状腺乳头状癌（PTC）的多模态诊断模型，旨在提高病理学家的工作效率。对222例术后诊断为PTC的病例和163例术后诊断为良性甲状腺结节的病例进行了回顾性分析。采用扫描获取细胞病理学数字化图像、切割图像块并勾勒出相应的超声成像病变，以提取病理组学和超声放射组学特征。应用极端梯度提升（XGBoost）、支持向量机（SVM）和随机森林（RF）算法这三种方法构建PTC细胞病理学诊断模型。通过受试者操作特征曲线下面积（AUC）评估和验证模型的效能，并比较单模态、多模态和人工诊断模型的性能。
