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

doi:10.1117/12.3005429

2 April 2024 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, Medical Imaging 2024: Image Processing; 129260J (2024) https://doi.org/10.1117/12.3005429
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianwei Qiu, Jhimli Mitra, Camille Dumas, Brion Sarachan, Soumya Ghose, and Marc Judson "Radiomics-guided 3D CNN-vision transformer (Rad-CNNViT) ensemble to diagnose pulmonary sarcoidosis from CT", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260J (2 April 2024); https://doi.org/10.1117/12.3005429

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