From Cross-Sectional CT to Dynamic Insights: Pseudotime-Based Modeling of Lung Nodule Progression

Luoting Zhuang, Linh M. Tran, Ashley E. Prosper, William Hsu

Keywords: Pseudotime inference, disease trajectory, lung nodules, diffusion maps, unsupervised learning, medical imaging biomarker

TL;DR: This paper is the first to explore and demonstrate the potential of using pseudotime inference to reconstruct pulmonary nodule progression from cross-sectional CT scans.

Abstract: Early detection of lung cancer relies on a comprehensive understanding of the progression of pulmonary nodules. Existing longitudinal modeling approaches are constrained due to the limited availability of longitudinal datasets and the failure to capture the inter-nodular relationship. In this study, we present the first application of pseudotime inference, adapted from single-cell RNA sequencing studies, to reconstruct progression trajectories of nodules from cross-sectional CT images. We collected 13,626 nodule snapshots from two screening cohorts and reserved a longitudinal test set for evaluation. We compared a graph-based pseudotime method, diffusion pseudotime, and an unsupervised deep learning framework combining a variational autoencoder and a neural ordinary differential equation. Both approaches demonstrate longitudinal consistency, with malignant nodules showing a higher correlation between pseudotime and actual time. Pseudotime aligns with clinically relevant features such as irregular margins and solid consistency. Furthermore, pseudotime and delta pseudotime effectively stratify nodules into distinct malignancy risk groups and remain significant independent predictors of malignancy after adjusting for established semantic biomarkers. Our study highlights pseudotime inference as a promising tool for dynamic modeling of lesion progression using static imaging data.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Radiology

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Submission Number: 258