Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 2:15 AM
Study NCT ID:
NCT07409168
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-13
First Post:
2026-02-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NKTCL
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 100}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-02-15', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-06', 'studyFirstSubmitDate': '2026-02-06', 'studyFirstSubmitQcDate': '2026-02-06', 'lastUpdatePostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Predictive accuracy of first-line treatment response (CR vs non-CR) according to Lugano 2014 criteria', 'timeFrame': 'From baseline to disease response and follow-up assessments, up to 3 years.', 'description': 'The primary outcome is the predictive performance of the multimodal deep learning model for first-line treatment response in patients with extranodal natural killer/T-cell lymphoma (NKTCL). Treatment response is assessed according to the Lugano 2014 criteria. Model performance will be evaluated by receiver operating characteristic (ROC) analysis and quantified using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value by comparing model predictions with observed clinical response.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Natural Killer/T-cell Lymphoma']}, 'descriptionModule': {'briefSummary': 'This is a multicenter prospective study to develop and validate a multimodal, deep learning-based model for predicting treatment response in patients with extranodal natural killer/T-cell lymphoma (NKTCL) receiving first-line asparaginase-based therapy.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population includes adult patients with pathologically confirmed extranodal natural killer/T-cell lymphoma (NKTCL) receiving first-line asparaginase-based chemotherapy or chemoradiotherapy, with available pretreatment MRI or H\\&E-stained whole-slide pathology images. Patients will be prospectively followed for treatment response and survival outcomes.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* 1\\. Age ≥ 18 years.\n* 2\\. Pathologically confirmed extranodal natural killer/T-cell lymphoma (NKTCL) according to the World Health Organization (WHO) classification.\n* 3\\. Patients who are planned to receive first-line asparaginase-based chemotherapy or chemoradiotherapy.\n* 4\\. Patients who have either contrast-enhanced MRI of the nasopharynx obtained as part of routine clinical care or pretreatment whole-slide images (WSI) of tumor tissue from hematoxylin and eosin (H\\&E)-stained sections available for analysis.\n* 5\\. Ability to understand the study and provide written informed consent (ICF).\n\nExclusion Criteria:\n\n* 1\\. History of other malignant tumors.\n* 2\\. Patients with psychiatric disorders or those unable to provide informed consent.'}, 'identificationModule': {'nctId': 'NCT07409168', 'briefTitle': 'Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NKTCL', 'organization': {'class': 'OTHER', 'fullName': 'Sun Yat-sen University'}, 'officialTitle': 'Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NK/T-Cell Lymphoma', 'orgStudyIdInfo': {'id': 'B2025-444'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'First-line Asparaginase-based Treatment Cohort', 'description': 'Participants in this cohort are patients with extranodal natural killer/T-cell lymphoma (NKTCL) who are planned to receive standard first-line asparaginase-based chemotherapy according to institutional practice. Pretreatment clinical data, contrast-enhanced magnetic resonance imaging (MRI) of the nasopharynx and neck, and digital pathology images from hematoxylin and eosin (H\\&E)-stained tumor sections will be collected. Patients will be followed for progression-free survival and overall survival according to routine follow-up schedule.'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Qingqing Cai, MD. PhD.', 'role': 'CONTACT', 'email': 'caiqq@sysucc.org.cn', 'phone': '0208734282'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sun Yat-sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'chief phycisian', 'investigatorFullName': 'Qingqing Cai', 'investigatorAffiliation': 'Sun Yat-sen University'}}}}