Ignite Creation Date:
2025-12-25 @ 12:21 AM
Ignite Modification Date:
2025-12-25 @ 10:24 PM
Study NCT ID:
NCT06285058
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-13
First Post:
2024-02-22
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002289', 'term': 'Carcinoma, Non-Small-Cell Lung'}], 'ancestors': [{'id': 'D002283', 'term': 'Carcinoma, Bronchogenic'}, {'id': 'D001984', 'term': 'Bronchial Neoplasms'}, {'id': 'D008175', 'term': 'Lung Neoplasms'}, {'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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2024-03', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-02', 'completionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-03-11', 'studyFirstSubmitDate': '2024-02-22', 'studyFirstSubmitQcDate': '2024-02-22', 'lastUpdatePostDateStruct': {'date': '2024-03-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-02-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of predicting model', 'timeFrame': 'Baseline treatment', 'description': 'several metrics were calculated, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Deep Learning Model', 'Pathological Complete Response', 'Non-small Cell Lung Cancer', 'Neoadjuvant Chemoimmunotherapy']}, 'referencesModule': {'references': [{'pmid': '41127561', 'type': 'DERIVED', 'citation': 'Ye G, Wei Z, Han C, Wu G, Wong C, Liang Y, Chen X, Zhou W, Gao J, Liang C, Liao Y, Hendriks LEL, Wee L, De Ruysscher D, Dekker A, Zhou H, Qi Y, Liu Z, Shi Z. AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. EClinicalMedicine. 2025 Oct 7;89:103551. doi: 10.1016/j.eclinm.2025.103551. eCollection 2025 Nov.'}]}, 'descriptionModule': {'briefSummary': 'This study presents the development and validation of an artificial intelligence (AI) prediction system that utilizes pre-neoadjuvant immunotherapy plain scans and enhanced multimodal CT scans to extract deep learning features. The aim is to predict the occurrence of pathological complete response in non-small cell lung cancer patients undergoing neoadjuvant immunochemotherapyy.', 'detailedDescription': 'This study retrospectively obtained non-contrast enhanced and contrast enhanced CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy. at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contract enhanced and contract enhanced CT scans to construct the predictive models (LUNAI-nCT model and LUNAI-eCT model), respectively. After feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to generate saliency heatmaps.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n1. Patients' with non-small cell lung cancer, diagnosed through biopsy pathology and clinically classified as stage IB to III;\n2. Patients who receive at least two cycles of neoadjuvant immunotherapy combined with chemotherapy induction therapy;\n3. According to the IASLC guidelines, postoperative pathological evaluation was performed on the treatment response of the tumor primary lesion and lymph nodes.\n\nExclusion Criteria:\n\n1. Missing or inadequate quality of CT;\n2. Time interval between CT and start of treatment is greater than 1 month;\n3. Incomplete clinicopathologic data."}, 'identificationModule': {'nctId': 'NCT06285058', 'briefTitle': 'Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy', 'organization': {'class': 'OTHER', 'fullName': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology'}, 'officialTitle': 'A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy', 'orgStudyIdInfo': {'id': 'LUNAI'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training dataset', 'description': 'patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital 1 (Tongji Medical College Affiliated Union Hospital)', 'interventionNames': ['Diagnostic Test: No interventions']}, {'label': 'test dataset', 'description': 'patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital (Zhengzhou University First Affiliated Hospital, Yichang Central Hospital, Anyang Cancer Hospital)'}], 'interventions': [{'name': 'No interventions', 'type': 'DIAGNOSTIC_TEST', 'description': 'The high-throughput extraction of large amounts of quantitative image features from medical images', 'armGroupLabels': ['Training dataset']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}