Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography

Frontiers in Medicine

Kuo-Chen Wu¹  Yi-Jin Chen¹  Chao-Jen Chang¹  Chia-Hung Kao²  Kuo-Yang Yen³  Yung-Chi Lai⁴  Neng-Chuan Tseng⁵ 

[1] Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan;Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan;Department of Biomedical Imaging and Radiological Science, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan;Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan;Division of Nuclear Medicine, Tungs’ Taichung MetroHarbor Hospital, Taichung, Taiwan;Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan;Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan;

关键词: pulmonary nodules;    3D high-resolution representation learning;    fluorodeoxyglucose (FDG);    positron emission tomography-computed tomography (PET-CT);    operating characteristic curve (AUC);    artificial intelligence;   

DOI  :  10.3389/fmed.2022.773041

来源: DOAJ

【 摘 要 】

BackgroundThe investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT.MethodsIn total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model.ResultsAll pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively.ConclusionOur results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.

【 授权许可】

Unknown