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

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Conditions studied: Pulmonary Nodules, Lung Neoplasms, Adenocarcinoma of Lung

In brief

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.

Key facts

Study ID

NCT07647692

Run by

Peking University People's Hospital

People needed

4750

Starts

2026-06-01

Expected to finish

2031-06-01

Last updated by the study team

2026-06-15

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Age ≥ 18 years.
• Persistent pulmonary ground-glass nodule (pGGN or mGGN, 5-30 mm) on thin-slice chest CT (slice thickness ≤ 1.5 mm).
• Baseline and follow-up thin-slice chest CTs of sufficient quality for 3D segmentation and registration.
• Minimum interval between any two consecutive CTs > 1 month.
• Complete baseline clinical data available (age, sex, smoking history, family history of malignancy, relevant comorbidities).
• Cohort-specific inclusion
• Group 1 (Development): surgical resection of the target GGN at PKUPH between Jan 2007 - Jun 2025, with ≥ 2 pre-operative thin-slice CTs available.
• Group 2 (Surgical internal test): surgical resection at PKUPH between Jul 2025 - Jan 2026, with ≥ 2 pre-operative thin-slice CTs available.
• Group 3 (Non-surgical internal test): non-operative management at PKUPH between Jan 2020 - Dec 2025, with ≥ 3 thin-slice CTs of the target GGN available.
• Group 4 (Prospective external validation): prospective enrollment after model lock at participating centers, baseline CT plus ≥ 2 planned routine follow-up thin-slice CTs.

You may not qualify if…

• Coexisting severe pulmonary disease that obscures evaluation of the target GGN (e.g., active pulmonary tuberculosis, severe interstitial lung disease).
• Prior history of any other thoracic malignancy, or active extrathoracic malignancy under treatment within 5 years, that would confound interpretation of the target GGN.
• CT image quality insufficient for registration and feature extraction (severe motion artifact, slice thickness > 1.5 mm at any required timepoint, or extensive metallic artifact projecting over the target GGN).
• Pure solid nodule with no ground-glass component.
• Target GGN already received treatment (resection, ablation, or radiotherapy) prior to the baseline CT used in this study.

Where it is running

• Peking University People's Hospital — Beijing, Beijing Municipality, China

Full record on ClinicalTrials.gov

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