Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Intelligent Medical Imaging of Jiangxi Key Laboratory, Nanchang, China.
Thorac Cancer. 2024 Nov;15(31):2235-2247. doi: 10.1111/1759-7714.15454. Epub 2024 Sep 20.
This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort.
Based on chest-enhanced computed tomography (CT), the region of interest was delineated, and the maximum cross section of the lesion was selected as the input image. Five convolutional neural networks (CNNs) and Vision Transformer (ViT) were used to construct a 2D DTL model. The 2D model constructed by the maximum section (n) and the upper and lower layers (n - 1, n + 1) of the lesion was used for feature extraction, and the features were selected. The remaining features were pre-fused to construct a 2.5D model. The whole lesion image was selected for input and constructing a 3D model.
In the 2D model, the area under curve (AUC) of Resnet50 was 0.950 in the training cohort and 0.907 in the internal validation cohort. In the 2.5D model, the AUCs of Vgg11 in the internal validation cohort and external validation cohort 1 were 0.937 and 0.965, respectively. The AUCs of Inception_v3 in the training cohort and external validation cohort 2 were 0.981 and 0.950, respectively. The AUC values of 3D_Resnet50 in the four cohorts were 0.987, 0.937, 0.938, and 0.905.
The DTL model based on multiple different dimensions can be used as a highly sensitive and specific tool for the non-invasive differential diagnosis of thymomas and thymic cysts to assist clinicians in decision-making.
本研究旨在评估不同类型和维度的深度迁移学习（DTL）网络在回顾性队列中区分胸腺瘤和胸腺囊肿的可行性和性能。
基于胸部增强 CT，划定感兴趣区域，并选择病变最大横截面作为输入图像。使用 5 个卷积神经网络（CNN）和 Vision Transformer（ViT）构建 2D DTL 模型。构建病变最大截面（n）和上下层（n-1、n+1）的 2D 模型用于特征提取，并选择特征。剩余特征进行预融合，构建 2.5D 模型。选择整个病变图像作为输入，构建 3D 模型。
在 2D 模型中，Resnet50 在训练队列中的 AUC 为 0.950，内部验证队列中的 AUC 为 0.907。在 2.5D 模型中，Vgg11 在内部验证队列和外部验证队列 1 的 AUC 分别为 0.937 和 0.965。Inception_v3 在训练队列和外部验证队列 2 的 AUC 分别为 0.981 和 0.950。3D_Resnet50 在四个队列中的 AUC 值分别为 0.987、0.937、0.938 和 0.905。
基于多种不同维度的 DTL 模型可作为一种高度敏感和特异的非侵入性鉴别诊断胸腺瘤和胸腺囊肿的工具，以协助临床医生进行决策。
