肺结节病是一种多系统肉芽肿性间质性肺病(ILD),表现和预后可变。肺结节病的早期准确检测可以防止进展为肺纤维化,一种严重的、可能危及生命的疾病。然而,缺乏金标准的诊断测试和特定的影像学检查结果给诊断肺结节病带来了挑战.胸部计算机断层扫描(CT)成像是常用的,但需要专家,胸部训练的放射科医师区分肺结节病和肺恶性肿瘤,感染,和其他ILD。在这项工作中,我们开发了多渠道,CT和影像组学引导的集成网络(RadCT-CNNViT)对肺结节病的视觉可解释性与使用胸部CT图像进行肺癌(LCa)分类。我们利用CT和手工制作的影像组学功能作为输入通道,以及3D卷积神经网络(CNN)和视觉变换器(ViT)集成网络,用于在分类头之前进行特征提取和融合。3DCNN子网络捕获病变的局部空间信息,而ViT子网捕获远程,功能之间的全局依赖关系。通过多通道输入和特征融合,我们的模型实现了最高的性能和准确性,灵敏度,特异性,精度,F1分数,和联合AUC分别为0.93±0.04、0.94±0.04、0.93±0.08、0.95±0.05、0.94±0.04和0.97,在一项针对肺结节病(n=126)和LCa(n=93)病例的5倍交叉验证研究中。详细的消融研究显示了CNN+ViT与单独的CNN或ViT相比的影响,和CT+影像组学输入,与单独的CT或影像组学相比,也在这项工作中提出。总的来说,这项工作中开发的AI模型为从胸部CT分类肺结节病患者以便及时诊断和治疗提供了有希望的潜力.
Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.