Ignite Creation Date:
2025-12-24 @ 7:02 PM
Ignite Modification Date:
2025-12-25 @ 4:35 PM
Study NCT ID:
NCT07256457
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-01
First Post:
2025-11-20
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Postoperative Pulmonary Function Assessment Based on Deep Learning Study
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}, {'id': 'D002289', 'term': 'Carcinoma, Non-Small-Cell Lung'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D002283', 'term': 'Carcinoma, Bronchogenic'}, {'id': 'D001984', 'term': 'Bronchial Neoplasms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D015412', 'term': 'Mastectomy, Segmental'}, {'id': 'D038481', 'term': 'Anterior Temporal Lobectomy'}], 'ancestors': [{'id': 'D008408', 'term': 'Mastectomy'}, {'id': 'D013514', 'term': 'Surgical Procedures, Operative'}, {'id': 'D019635', 'term': 'Neurosurgical Procedures'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'OTHER', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 192}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-07-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2029-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-20', 'studyFirstSubmitDate': '2025-11-20', 'studyFirstSubmitQcDate': '2025-11-20', 'lastUpdatePostDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2029-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Develop and validate a composite PF score', 'timeFrame': '1, 3, 6, 12, 18, and 24 months (post-operation)', 'description': 'Develop and validate a composite PF score by applying ML algorithms to integrate pulmonary function test results (FEV₁, FVC, DLCO), 3D-CT volumetric data (segmental volumes, vascular density) (Figure 1), and basic demographic variables (age, BMI). The primary objective is to predict changes in postoperative pulmonary function at 1, 3, 6, 12, 18, and 24 months.'}], 'secondaryOutcomes': [{'measure': 'To compare the time trajectory of the PF score', 'timeFrame': 'at 1, 3, 6, 12, 18, and 24 months (post-operation)', 'description': 'Compare the time trajectory of the PF score among patients undergoing different treatment approaches-segmentectomy, lobectomy, and robotic/navigation-guided bronchoscopic ablation-to determine which method offers a superior advantage in long-term postoperative pulmonary function preservation.'}, {'measure': 'To Correlate the PF score with overall survival, cancer-specific survival, and postoperative complications', 'timeFrame': 'at 1, 3, 6, 12, 18, and 24 months (post-operation)', 'description': 'Correlate the PF score with overall survival, cancer-specific survival, and postoperative complications, assessing its utility as a clinical endpoint.'}, {'measure': 'To identify key predictive factors-including tumor location, histology, and baseline comorbidities', 'timeFrame': 'at 1, 3, 6, 12, 18, and 24 months (post-operation)', 'description': 'Identify key predictive factors-including tumor location, histology, and baseline comorbidities-that may influence postoperative functional decline or survival. Additionally, conduct a preliminary investigation into the application of robotic/navigation-guided bronchoscopic ablation in high-risk patients.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Non-small cell lung cancer', 'Pulmonary function score', 'Machine learning', '3D-CT imaging', 'Robotic bronchoscopy', 'Navigational bronchoscopy', 'Segmentectomy', 'Lobectomy', 'Postoperative outcomes'], 'conditions': ['Lung Cancer (NSCLC)']}, 'descriptionModule': {'briefSummary': "Improvements in low-dose CT screening have led to an increase in early-stage NSCLC diagnoses, with surgical resection-usually lobectomy or segmentectomy-remaining the primary curative option. In Hong Kong, however, patients frequently present with comorbidities such as chronic respiratory disease or cardiovascular issues, making the preservation of healthy lung tissue crucial for their long-term quality of life. Traditional surgical resection, such as lobectomy or segmentectomy, has certain limitations in terms of functional preservation. In contrast, robotic/navigational bronchoscopic ablation has emerged in recent years as a novel minimally invasive endoscopic treatment strategy. This approach has been implemented in select centers and demonstrates potential advantages, including faster postoperative recovery, reduced trauma, and improved preservation of pulmonary function. By leveraging advanced navigation systems, bronchoscopic ablation enables precise localization and ablation of pulmonary nodules, avoiding the extensive resection of healthy lung tissue required in traditional surgery. These benefits hold promise for enhancing patients' long-term quality of life and survival rates.\n\nMoreover, conventional pulmonary function tests like FEV₁ and diffusing capacity of the lung for carbon monoxide provide only a global assessment of respiratory capacity, which may not fully capture the regional changes in pulmonary function that occur following segmentectomy or lobectomy. Likewise, basic CT volumetry overlooks finer anatomical details such as segmental airway distribution, microvascular networks, and local alveolar compliance. Furthermore, there is currently a paucity of direct comparative studies between robotic/navigational bronchoscopic ablation and traditional surgical resection regarding postoperative pulmonary function and long-term outcomes. Supported by Research Grants Council, our work since 2019 has validated the feasibility and safety of this technique, leading to widespread recognition and numerous publications. However, most existing research is retrospective or derived from single-center data, with a primary focus on short-term safety and technical feasibility.\n\nTo address these limitations, an integrative approach leveraging 3D-CT imaging and ML is proposed. Machine learning is a technology that uses algorithms to automatically learn from data and make predictions or decisions. Deep learning, a subfield of ML, utilizes multi-layer neural networks to effectively extract features and recognize patterns in complex, high-dimensional data. ML, and particularly DL, has demonstrated remarkable potential in various medical imaging applications, including lesion detection, tissue segmentation, and outcome prediction. By automatically learning complex patterns in high-dimensional data, ML and DL models can interpret subtle radiologic characteristics that may be missed by conventional analyses. In the context of 3D-CT imaging for NSCLC, DL architectures-such as convolutional neural networks-can extract detailed features from volumetric scans, enabling robust quantification of tumor size, shape, and location as well as refined assessment of lung parenchyma. When integrated with pulmonary function parameters and clinical data, these algorithms provide a powerful means to generate predictive models, identify at-risk patients earlier, and guide individualized treatment planning. Moreover, ML-driven approaches can adapt to evolving datasets over time, continuously refining and improving their performance. This scalability and adaptability are especially valuable in prospective studies, where large, multimodal datasets are collected to evaluate the long-term impact of different treatment strategies. Consequently, incorporating ML and DL in this research not only enhances the precision of outcome prediction but also contributes to a standardized framework for dynamic, personalized assessment of pulmonary function, guiding more informed clinical decision-making.\n\nThe primary aim of this study is to determine whether segmentectomy truly offers better functional preservation than lobectomy, whether robotic/navigation-guided bronchoscopic ablation indeed achieves superior pulmonary function preservation compared to traditional surgical resection, and under which specific patient conditions each approach may yield the greatest benefit. By undertaking a prospective, well-designed investigation, the research will fill a critical gap in evidence regarding long-term functional outcomes, providing clearer criteria for selecting the most appropriate resection type. Moreover, the introduction of a standardized, integrative assessment tool has the potential to optimize surgical decision-making and postoperative care, ultimately improving survival and quality of life for early-stage NSCLC patients in Hong Kong and potentially informing best practices in other healthcare contexts."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ≥18 years\n2. Histologically or cytologically confirmed stage IA NSCLC (T1N0M0, tumor ≤3 cm)\n3. Scheduled for surgical treatment (segmentectomy or lobectomy or robotic/navigation-guided bronchoscopic ablation)\n4. Ability to complete postoperative visits for up to 2 years\n5. Provided informed consent\n\nExclusion Criteria:\n\n1. Presence of lymph node involvement (N1 or higher) or distant metastases (M1)\n2. Concomitant primary malignancies that could confound outcome analysis\n3. Insufficient or missing key data points (e.g., tumor size, treatment type, survival status)\n4. Duplicate or overlapping records from different data sources\n5. Contraindications to anesthesia or sedation for bronchoscopy or surgery\n6. Inability or unwillingness to perform the PF tests\n7. Pregnant or breastfeeding women (for prospective phase)\n8. Inability or unwillingness to comply with study procedures'}, 'identificationModule': {'nctId': 'NCT07256457', 'briefTitle': 'Postoperative Pulmonary Function Assessment Based on Deep Learning Study', 'organization': {'class': 'OTHER', 'fullName': 'Chinese University of Hong Kong'}, 'officialTitle': 'Multimodal Machine Learning Integration of Pulmonary Function Parameters and 3D-CT Imaging for Postoperative Outcome Prediction in Early-Stage NSCLC', 'orgStudyIdInfo': {'id': 'Protocol version 1.0'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Segmentectomy Group', 'interventionNames': ['Procedure: Segmentectomy']}, {'type': 'EXPERIMENTAL', 'label': 'Lobectomy Group', 'interventionNames': ['Procedure: Lobectomy']}, {'type': 'EXPERIMENTAL', 'label': 'Bronchoscopic Ablation Group', 'interventionNames': ['Procedure: Bronchoscopic Ablation']}], 'interventions': [{'name': 'Segmentectomy', 'type': 'PROCEDURE', 'description': 'with 64 participants', 'armGroupLabels': ['Segmentectomy Group']}, {'name': 'Lobectomy', 'type': 'PROCEDURE', 'description': 'with 64 participants', 'armGroupLabels': ['Lobectomy Group']}, {'name': 'Bronchoscopic Ablation', 'type': 'PROCEDURE', 'description': 'with 64 participants', 'armGroupLabels': ['Bronchoscopic Ablation Group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Shatin', 'country': 'Hong Kong', 'contacts': [{'name': 'Calvin Sze Hang Ng', 'role': 'CONTACT', 'email': 'calvinng@surgery.cuhk.edu.hk', 'phone': '+852 3505 2618'}], 'facility': 'Prince of Wales Hospital', 'geoPoint': {'lat': 22.38333, 'lon': 114.18333}}], 'centralContacts': [{'name': 'Calvin Sze Hang Ng', 'role': 'CONTACT', 'email': 'calvinng@surgery.cuhk.edu.hk', 'phone': '+852 3505 2618'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese University of Hong Kong', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Calvin Sze Hang Ng', 'investigatorAffiliation': 'Chinese University of Hong Kong'}}}}