Version 1
: Received: 2 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (07:31:48 CEST)
How to cite:
Qiu, J.; Mitra, J.; Ghose, S.; Dumas, C.; Yang, J.; Sarachan, B.; Judson, M. A. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Preprints2024, 2024050171. https://doi.org/10.20944/preprints202405.0171.v1
Qiu, J.; Mitra, J.; Ghose, S.; Dumas, C.; Yang, J.; Sarachan, B.; Judson, M. A. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Preprints 2024, 2024050171. https://doi.org/10.20944/preprints202405.0171.v1
Qiu, J.; Mitra, J.; Ghose, S.; Dumas, C.; Yang, J.; Sarachan, B.; Judson, M. A. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Preprints2024, 2024050171. https://doi.org/10.20944/preprints202405.0171.v1
APA Style
Qiu, J., Mitra, J., Ghose, S., Dumas, C., Yang, J., Sarachan, B., & Judson, M. A. (2024). A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Preprints. https://doi.org/10.20944/preprints202405.0171.v1
Chicago/Turabian Style
Qiu, J., Brion Sarachan and Marc A. Judson. 2024 "A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis" Preprints. https://doi.org/10.20944/preprints202405.0171.v1
Abstract
Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. 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 pose 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 developed 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 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 achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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