Clinical Research Directory

Deep Learning Time-Series Prediction of Long-Term Growth Patterns of Pulmonary Ground-Glass Nodules Using Serial CT

Sponsor: Peking University People's Hospital

Summary

Pulmonary ground-glass nodules (GGNs) are commonly found on chest CT scans. Some stay stable for years, while others slowly or rapidly turn into lung cancer. Doctors currently follow these nodules with repeated CT scans, but it is difficult to tell ahead of time which nodules will progress, how fast they will progress, and which ones can be safely monitored rather than immediately treated. This observational study aims to develop and validate an artificial intelligence (AI) model that uses each patient's series of CT scans over time to predict the long-term growth behavior of a GGN. The research team will collect three retrospective single-center cohorts from Peking University People's Hospital (a development cohort and two internal test cohorts, one from surgically resected patients and one from non-operated patients followed by serial CT) as well as a prospective multi-center validation cohort enrolled after the AI model is locked. For every patient, each GGN is automatically segmented in three dimensions on every CT scan. A deep learning model extracts imaging features at each timepoint and feeds the sequence of features, together with the actual times between scans, into a time-aware sequence model. The model is trained to predict (i) whether the nodule will show radiological progression at 1, 3, and 5 years after baseline, and (ii) which of four long-term growth patterns the nodule will follow: stable, slow progression, slow-then-rapid progression, or rapid progression. In patients who were ultimately resected, the histopathological diagnosis serves as a secondary reference standard. This is an observational study. No experimental treatment is given. All CT scans and clinical visits are part of routine clinical care.

Official title: Development and Multi-Cohort Validation of a Deep Learning Spatiotemporal Model for Predicting Long-Term Progression of Pulmonary Ground-Glass Nodules Using Serial Thoracic CT

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