B-G.C.24: Multimodal Machine Learning for Lung Function Prediction in Interstitial Lung Disease
Keywords
Data Science Multimodal Deep Learning ILD
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This work evaluates the feasibility of predicting one-year follow-up forced vital capacity (FVC) in patients with interstitial lung disease (ILD) using multimodal machine learning models based on three-dimensional high-resolution computed tomography (HRCT) scans and baseline clinical variables. A curated subset of the OSIC cohort was used to compare tabular-only, image-only, and multimodal late-fusion approaches under consistent preprocessing, training, and evaluation protocols, with image-based models employing axial-coronal-sagittal convolutions.
Across all experiments, models relying solely on baseline clinical variables achieved the strongest and most stable predictive performance. Baseline FVC was the dominant predictor, explaining most of the variance in one-year lung function outcomes. In contrast, image-only models trained on full three-dimensional HRCT volumes showed significantly weaker performance, indicating limited prognostic value of baseline imaging for short-term functional decline. Multimodal late-fusion models improved upon image-only approaches but did not outperform tabular baselines, suggesting only modest incremental benefit from HRCT-derived features when strong clinical predictors are available.
These findings highlight a key distinction between diagnostic and prognostic imaging tasks. Although HRCT is essential for ILD diagnosis and disease characterization, its utility for predicting short-term pulmonary function changes from a single baseline scan appears limited under moderate data and computational constraints. Radiomics-based models performed competitively relative to end-to-end deep learning approaches, underscoring the relevance of feature-based methods in data-limited settings. Overall, this work emphasizes aligning prediction targets with the information content of available data and motivates future studies using larger, longitudinal, and more harmonized datasets, as well as more data-efficient modeling strategies.
Co-authors: Simone Buchholz, Volker Ahlers
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