Dr. Marc Judson Profile

Dr. Marc Judson

at Albany Medical College

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Proceedings Article | 2 April 2024 Presentation + Paper

Radiomics-guided 3D CNN-vision transformer (Rad-CNNViT) ensemble to diagnose pulmonary sarcoidosis from CT

Jianwei Qiu, Jhimli Mitra, Camille Dumas, Brion Sarachan, Soumya Ghose, Marc Judson

Proceedings Volume 12926, 129260J (2024) https://doi.org/10.1117/12.3005429

KEYWORDS: Radiomics, Pulmonary disorders, Lung, Feature extraction, Diagnostics, Deep learning, Transformers, Image classification, Education and training

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Pulmonary sarcoidosis (PS) is an inflammatory interstitial lung disease, causing clusters of inflamed tissue called ‘granulomas’ within the lung. However, PS may mimic other conditions such as malignancy or infection, which often leads to a delayed diagnosis, leading to worsening respiratory function. Chest computed tomography (CT) is used to diagnose PS but with varying specificities, as the appearance of PS and other diffuse lung diseases on chest CT are myriad and considered difficult to diagnose by many radiologists. In this work we used a radiomics-guided ensemble of 3D CNN and Vision Transformers (ViT) features (Rad-CNNViT) to classify between PS and other interstitial lung diseases (o-ILDs). The input to the network was the 3D radiomics map that received the top feature score in a Random Forest (RF)-based feature selection approach. The input map was then fed into an ensemble network of CNN and ViT to capture local and global features respectively for diffuse lung disease classification. Training datasets of PS (n=61) and o-ILD chest CTs (n=154) were used for feature discovery using RF and train the Rad-CNNViT and a radiomics-based machine learning (Rad-ML) framework. On a separate test cohort of PS (n=65) and o-ILD (n=96), Rad-CNNViT ensemble network differentiated PS from o-ILDs with higher AUC=0.89 compared to a Rad-ML approach with AUC=0.77.

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