Ignite Creation Date:
2025-12-24 @ 11:45 PM
Ignite Modification Date:
2026-01-03 @ 4:34 AM
Study NCT ID:
NCT05925751
Status:
UNKNOWN
Last Update Posted:
2023-06-29
First Post:
2023-05-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002289', 'term': 'Carcinoma, Non-Small-Cell Lung'}, {'id': 'D000095384', 'term': 'Pathologic Complete Response'}], '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'}, {'id': 'D018450', 'term': 'Disease Progression'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 100}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-06', 'completionDateStruct': {'date': '2023-10-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-06-27', 'studyFirstSubmitDate': '2023-05-12', 'studyFirstSubmitQcDate': '2023-06-27', 'lastUpdatePostDateStruct': {'date': '2023-06-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-06-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-10-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Specificity', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The specificity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}, {'measure': 'Positive predictive value', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The positive predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}, {'measure': 'Negative predictive value', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The negative predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}, {'measure': 'Accuracy', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The accuracy of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}], 'primaryOutcomes': [{'measure': 'Area under the receiver operating characteristic curve', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}], 'secondaryOutcomes': [{'measure': 'Sensitivity', 'timeFrame': '2023.5.1-2023.10.31', 'description': 'The sensitivity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Non-small Cell Lung Cancer', 'Neoadjuvant Chemoimmunotherapy', 'Complete Pathological Response']}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '20 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Resected Stage I-III NSCLC following neoadjuvant chemoimmunotherapy', 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ranging from 20-75 years;\n2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;\n3. Obtained written informed consent.\n\nExclusion Criteria:\n\n1. Missing image data;\n2. Pathological N3 disease.'}, 'identificationModule': {'nctId': 'NCT05925751', 'briefTitle': 'Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Pulmonary Hospital, Shanghai, China'}, 'officialTitle': 'An Integration of a Computed Tomography/Positron Emission Tomography/Whole Slide Image (CT/PET/WSI) Based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer: A Multicenter Study', 'orgStudyIdInfo': {'id': 'DLCPR'}}, 'armsInterventionsModule': {'interventions': [{'name': 'CT/PET/WSI-based Deep Learning Signature', 'type': 'DIAGNOSTIC_TEST', 'description': 'CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Zunyi', 'state': 'Guizhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Yongxiang Song, Dr', 'role': 'CONTACT', 'email': 'zhong961008@163.com', 'phone': '15505177258'}], 'facility': 'Affiliated Hospital of Zunyi Medical University', 'geoPoint': {'lat': 27.68667, 'lon': 106.90722}}, {'city': 'Nanchang', 'state': 'Jiangxi', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Bentong Yu, Dr', 'role': 'CONTACT', 'email': '1151697503@qq.com', 'phone': '021-65115006'}], 'facility': 'The First Affiliated Hospital of Nanchang University', 'geoPoint': {'lat': 28.68396, 'lon': 115.85306}}, {'city': 'Ningbo', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Minglei Yang, Dr', 'role': 'CONTACT', 'email': 'almondjj@163.com', 'phone': '021-65115006'}], 'facility': 'Ningbo HwaMei Hospital', 'geoPoint': {'lat': 29.87819, 'lon': 121.54945}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Pulmonary Hospital, Shanghai, China', 'class': 'OTHER'}, 'collaborators': [{'name': 'Ningbo No.2 Hospital', 'class': 'OTHER'}, {'name': 'Zunyi Medical College', 'class': 'OTHER'}, {'name': 'The First Affiliated Hospital of Nanchang University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Chang Chen', 'investigatorAffiliation': 'Shanghai Pulmonary Hospital, Shanghai, China'}}}}