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

NCT07647692 | Not Yet Recruiting

• 3 other identifiers: PKUPH-GGN-TRAJ-2026L2520642025PHB380
• Study Type: observational
• Target: 4,750
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Pulmonary Nodules; Lung Neoplasms; Adenocarcinoma of Lung

Keywords

Ground Glass Opacity; Ground-Glass Nodule; Longitudinal CT; Time-Series Analysis; Growth Trajectory; Volume Doubling Time; Deep Learning; Radiomics

Outcome Measures

Primary Outcomes (4)

• Radiological Progression of the Target Ground-Glass Nodule at 1 Year

  Radiological progression is defined as meeting either of the following on a follow-up thin-slice CT compared with the baseline CT, based on 3D automated segmentation with expert adjudication: (a) increase of the overall maximum diameter of the nodule by ≥ 2 mm; OR (b) the appearance of a new solid component, or the increase in maximum diameter of an existing solid component, by ≥ 2 mm. The solid component is defined as regions with an attenuation value greater than -300 HU. Each participant is classified as a progression event at the 1-year timepoint if either criterion is met on a CT performed within the ± 3-month window around 12 months after baseline. Applies to all four cohorts.

  12 months from baseline CT (± 3-month window)

• Radiological Progression of the Target Ground-Glass Nodule at 3 Years

  Same progression definition as Primary Outcome 1, assessed on a CT performed within the ± 6-month window around 36 months after baseline. Ascertained where the available follow-up duration permits. Applies to all four cohorts.

  36 months from baseline CT (± 6-month window)

• Radiological Progression of the Target Ground-Glass Nodule at 5 Years

  Same progression definition as Primary Outcome 1, assessed on a CT performed within the ± 6-month window around 60 months after baseline. Ascertained where the available follow-up duration permits. Applies to all four cohorts.

  60 months from baseline CT (± 6-month window)

• Long-Term Growth Trajectory Classification of the Target GGN

  Each participant's target GGN is assigned, based on the full serial CT record, to exactly one of four mutually exclusive trajectory classes: (1) Stable - no progression event during follow-up; (2) Slow progression - continuous, approximately constant slow growth or slow increase of the solid component; (3) Slow-then-rapid progression - stable or minimally changing for an early period (e.g., 1-3 years) followed by abrupt acceleration (e.g., marked shortening of volume doubling time or new prominent solid component); (4) Rapid progression - aggressive growth evident early in follow-up. Classification is performed by two senior chest radiologists reading independently on the 3D automated outputs, with a third senior radiologist adjudicating disagreements.

  Assessed across the full serial CT record, up to 60 months from baseline

Secondary Outcomes (1)

• Histopathological Diagnosis at Surgical Resection

  At the time of clinical surgical resection (varies by participant; up to 60 months from baseline)

Study Arms (4)

Retrospective Surgical Development Cohort (Training)

Anticipated n: 2,700. Enrollment period: January 2007 - June 2025. Adults aged 18 years or older who underwent surgical resection for a pulmonary ground-glass nodule (pGGN or mGGN, 5-30 mm) at Peking University People's Hospital between January 2007 and 30 June 2025, with at least two pre-operative thin-slice chest CT scans (slice thickness ≤ 1.5 mm) of the resected nodule available. Imaging and clinical data from this cohort are used exclusively for model development (training and hyperparameter tuning). Surgical histopathology is collected and used as the secondary reference standard.

Diagnostic Test: Serial Thin-Slice Chest CT

Retrospective Surgical Internal Test Cohort

Anticipated n: 350. Enrollment period: July 2025 - January 2026. Adults aged 18 years or older who underwent surgical resection for a pulmonary ground-glass nodule at Peking University People's Hospital between 1 July 2025 and 31 January 2026, with at least two pre-operative thin-slice chest CT scans of the resected nodule available. This cohort is held out from model development and used exclusively for internal testing on operated patients. Surgical histopathology is available as the secondary reference standard.

Diagnostic Test: Serial Thin-Slice Chest CT

Retrospective Non-Surgical Internal Test Cohort

Anticipated n: 1,200. Enrollment period: January 2020 - December 2025. Adults aged 18 years or older with a persistent pulmonary ground-glass nodule at Peking University People's Hospital between 1 January 2020 and 31 December 2025 who were managed non-operatively with serial CT surveillance, with at least three thin-slice chest CT scans of the target GGN available. This cohort is held out from model development and used exclusively for internal testing in the non-operated population. Histopathology is not available; the primary imaging endpoints are used as the reference standard.

Diagnostic Test: Serial Thin-Slice Chest CT

Prospective Multi-Center External Validation Cohort

Anticipated n: 500. Enrollment period: June 2026 onward. Adults aged 18 years or older newly identified with a persistent pulmonary ground-glass nodule (5-30 mm) at participating centers, prospectively enrolled after the AI model is locked. Both operated and non-operated patients are eligible; a baseline thin-slice chest CT is required, and at least two additional thin-slice follow-up CTs are obtained as part of routine clinical care. Imaging and clinical data from this cohort are used exclusively for external (in-time and multi-center) validation of the locked model and are not used for any model training or hyperparameter tuning. Histopathology, where available from clinical resection, is used as the secondary reference standard.

Diagnostic Test: Serial Thin-Slice Chest CT

Interventions

Serial Thin-Slice Chest CT DIAGNOSTIC_TEST

Routine-care thin-slice non-contrast chest CT (slice thickness ≤ 1.5 mm, lung-window reconstruction) acquired at baseline and at subsequent clinical follow-up timepoints (minimum inter-scan interval \> 1 month). Images are resampled to 1 × 1 × 1 mm and intensity-normalized before analysis. No additional imaging, radiation exposure, or procedures are performed for this study; all imaging is part of routine clinical care.

Prospective Multi-Center External Validation Cohort; Retrospective Non-Surgical Internal Test Cohort; Retrospective Surgical Development Cohort (Training); Retrospective Surgical Internal Test Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients (≥ 18 years) with a persistent pulmonary ground-glass nodule who underwent routine chest CT surveillance at the participating centers. Three retrospective cohorts (surgical development, surgical internal test, non-surgical internal test) are identified from the institutional PACS and clinical records of Peking University People's Hospital; one prospective multi-center cohort is enrolled consecutively after the AI model is locked.

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).
• 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.

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, 100044, China

Location

MeSH Terms

Conditions

Multiple Pulmonary Nodules; Lung Neoplasms; Adenocarcinoma of Lung

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Study Officials

• Hao Li

  Peking University People's Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Hao Li

CONTACT

13051713494; haolipkuph@pku.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chief Physician, Department of Thoracic Surgery

Study Record Dates

• First Submitted: June 10, 2026
• First Posted: June 15, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): June 1, 2031
• Last Updated: June 15, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)