[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100641818":3},{"organization":4,"armGroups":7,"interventions":26,"overallOfficials":32,"centralContacts":36,"locations":41,"responsibleParty":57,"collaborators":10,"id":60,"slug":61,"hasResults":62,"nctId":63,"briefTitle":64,"officialTitle":65,"acronym":66,"eligibilityCriteria":67,"healthyVolunteers":62,"sex":68,"minAge":69,"maxAge":10,"enrollmentInfo":70,"targetDuration":73,"studyType":74,"phases":10,"briefSummary":75,"conditions":76,"keywords":80,"overallStatus":89,"whyStopped":10,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":99},{"fullName":5,"class":6},"Peking University People's Hospital","OTHER",[8,14,18,22],{"label":9,"type":10,"description":11,"interventionNames":12},"Retrospective Surgical Development Cohort (Training)",null,"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.",[13],"Diagnostic Test: Serial Thin-Slice Chest CT",{"label":15,"type":10,"description":16,"interventionNames":17},"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.",[13],{"label":19,"type":10,"description":20,"interventionNames":21},"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.",[13],{"label":23,"type":10,"description":24,"interventionNames":25},"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.",[13],[27],{"type":28,"name":29,"description":30,"armGroupLabels":31,"otherNames":10},"DIAGNOSTIC_TEST","Serial Thin-Slice Chest CT","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.",[23,19,9,15],[33],{"name":34,"affiliation":5,"role":35},"Hao Li","PRINCIPAL_INVESTIGATOR",[37],{"name":34,"role":38,"phone":39,"phoneExt":10,"email":40},"CONTACT","13051713494","haolipkuph@pku.edu.cn",[42],{"facility":5,"status":10,"city":43,"state":44,"zip":45,"country":46,"countryCode":47,"cosmosGeoPoint":48,"geoPoint":53,"contacts":54},"Beijing","Beijing Municipality","100044","China","CN",{"type":49,"coordinates":50},"Point",[51,52],116.39723,39.9075,{"lat":52,"lon":51},[55],{"name":34,"role":38,"phone":39,"phoneExt":10,"email":56},"haolipkuph@pkuph.edu.cn",{"type":35,"investigatorFullName":58,"investigatorTitle":59,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"HaoLi","Associate Chief Physician, Department of Thoracic Surgery","100641818","deep-learning-time-series-prediction-of-long-term-growth-patterns-of-pulmonary-ground-glass-nodules-using-serial-ct-100641818",false,"NCT07647692","Deep Learning Time-Series Prediction of Long-Term Growth Patterns of Pulmonary Ground-Glass Nodules Using Serial CT","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","GGN-Trajectory","Inclusion Criteria:\n\n* Age ≥ 18 years.\n* Persistent pulmonary ground-glass nodule (pGGN or mGGN, 5-30 mm) on thin-slice chest CT (slice thickness ≤ 1.5 mm).\n* Baseline and follow-up thin-slice chest CTs of sufficient quality for 3D segmentation and registration.\n* Minimum interval between any two consecutive CTs \\> 1 month.\n* Complete baseline clinical data available (age, sex, smoking history, family history of malignancy, relevant comorbidities).\n\nCohort-specific inclusion\n\n* Group 1 (Development): surgical resection of the target GGN at PKUPH between Jan 2007 - Jun 2025, with ≥ 2 pre-operative thin-slice CTs available.\n* Group 2 (Surgical internal test): surgical resection at PKUPH between Jul 2025 - Jan 2026, with ≥ 2 pre-operative thin-slice CTs available.\n* 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.\n* Group 4 (Prospective external validation): prospective enrollment after model lock at participating centers, baseline CT plus ≥ 2 planned routine follow-up thin-slice CTs.\n\nExclusion Criteria:\n\n* Coexisting severe pulmonary disease that obscures evaluation of the target GGN (e.g., active pulmonary tuberculosis, severe interstitial lung disease).\n* Prior history of any other thoracic malignancy, or active extrathoracic malignancy under treatment within 5 years, that would confound interpretation of the target GGN.\n* 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).\n* Pure solid nodule with no ground-glass component.\n* Target GGN already received treatment (resection, ablation, or radiotherapy) prior to the baseline CT used in this study.","ALL","18 Years",{"count":71,"type":72},4750,"ESTIMATED","5 Years","OBSERVATIONAL","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.\n\nThis 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.\n\nFor 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.\n\nThis is an observational study. No experimental treatment is given. All CT scans and clinical visits are part of routine clinical care.",[77,78,79],"Pulmonary Nodules","Lung Neoplasms","Adenocarcinoma of Lung",[81,82,83,84,85,86,87,88],"Ground Glass Opacity","Ground-Glass Nodule","Longitudinal CT","Time-Series Analysis","Growth Trajectory","Volume Doubling Time","Deep Learning","Radiomics","NOT_YET_RECRUITING","2026-06-10",{"date":92,"type":93},"2026-06-15","ACTUAL",{"date":95,"type":72},"2026-06-01",{"date":97,"type":72},"2031-06-01",{"name":5,"class":6},1]