Clinical trial · Observational
Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NKTCL
Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NK/T-Cell Lymphoma
NCT07409168CI-TRIAL-00109615not yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Natural Killer/T-cell Lymphoma | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (1)
- 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.
Primary outcomes (1)
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 1\. Age ≥ 18 years. * 2\. Pathologically confirmed extranodal natural killer/T-cell lymphoma (NKTCL) according to the World Health Organization (WHO) classification. * 3\. Patients who are planned to receive first-line asparaginase-based chemotherapy or chemoradiotherapy. * 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. * 5\. Ability to understand the study and provide written informed consent (ICF). Exclusion Criteria: * 1\. History of other malignant tumors. * 2\. Patients with psychiatric disorders or those unable to provide informed consent.
References
Publications (0)
Data not yet available
No reference posted for this study.