Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients

TL;DR

This paper presents a novel deep learning and radiomics framework for predicting lung fibrosis in PASC patients using chest CT scans, achieving high accuracy and providing interpretability for clinical use.

Contribution

Introduces the first multi-center deep learning and radiomics approach for PASC-related lung fibrosis prediction with interpretability features.

Findings

Achieved 82.2% accuracy in fibrosis classification

Achieved 85.5% AUC in classification tasks

Demonstrated clinical relevance through Grad-CAM and radiomics analysis

Abstract

While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remains substantial uncertainty regarding both its duration and optimal management strategies. PASC manifests as a diverse array of persistent or newly emerging symptoms--ranging from fatigue, dyspnea, and neurologic impairments (e.g., brain fog), to cardiovascular, pulmonary, and musculoskeletal abnormalities--that extend beyond the acute infection phase. This heterogeneous presentation poses substantial challenges for clinical assessment, diagnosis, and treatment planning. In this paper, we focus on imaging findings that may suggest fibrotic damage in the lungs, a critical manifestation characterized by scarring of lung tissue, which can potentially affect long-term respiratory function in patients with PASC. This study introduces a novel multi-center chest CT analysis framework that combines deep learning and radiomics for fibrosis prediction. Our approach leverages convolutional neural networks (CNNs) and interpretable feature extraction, achieving 82.2% accuracy and 85.5% AUC in classification tasks. We demonstrate the effectiveness of Grad-CAM visualization and radiomics-based feature analysis in providing clinically relevant insights for PASC-related lung fibrosis prediction. Our findings highlight the potential of deep learning-driven computational methods for early detection and risk assessment of PASC-related lung fibrosis--presented for the first time in the literature.