Qiu Jianwei, Mitra Jhimli, Ghose Soumya, Dumas Camille, Yang Jun, Sarachan Brion, Judson Marc A
GE HealthCare, Niskayuna, NY 12309, USA.
Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA.
Diagnostics (Basel). 2024 May 18;14(10):1049. doi: 10.3390/diagnostics14101049.
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.
肺结节病是一种多系统肉芽肿性间质性肺病（ILD），其表现和预后各不相同。早期准确检测肺结节病可预防疾病进展为肺纤维化，这是该疾病的一种严重且可能危及生命的形式。然而，缺乏金标准诊断测试和特定的影像学表现给肺结节病的诊断带来了挑战。胸部计算机断层扫描（CT）成像常用，但需要专业的、经过胸部培训的放射科医生来区分肺结节病与肺癌、感染及其他ILD。在这项研究中，我们开发了一种具有视觉可解释性的多通道、CT和放射组学引导的集成网络（RadCT-CNNViT），用于使用胸部CT图像对肺结节病与肺癌（LCa）进行分类。我们利用CT和手工制作的放射组学特征作为输入通道，并在分类头之前使用三维卷积神经网络（CNN）和视觉Transformer（ViT）集成网络进行特征提取和融合。三维CNN子网络捕获病变的局部空间信息，而ViT子网络捕获特征之间的远程全局依赖性。通过多通道输入和特征融合，在一项对126例肺结节病和93例LCa病例的五折交叉验证研究中，我们的模型分别在准确率、敏感性、特异性、精确率、F1分数和综合AUC方面取得了最高性能，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。这项研究还展示了一项详细的消融研究，表明与单独的CNN或ViT相比，CNN+ViT的影响，以及与单独的CT或放射组学相比，CT+放射组学输入的影响。总体而言，这项研究中开发的人工智能模型为通过胸部CT对肺结节病患者进行及时诊断和治疗的分诊提供了有前景的潜力。
