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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Natural Killer/T-cell Lymphoma

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Qingqing Cai, MD. PhD.; Phone Number: 0208734282; Email: caiqq@sysucc.org.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

First-line Asparaginase-based Treatment Cohort; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive accuracy of first-line treatment response (CR vs non-CR) according to Lugano 2014 criteria	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.	From baseline to disease response and follow-up assessments, up to 3 years.

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Estimated): August 15, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: February 6, 2026
• First Submitted That Met QC Criteria: February 6, 2026
• First Posted (Actual): February 13, 2026

Study Record Updates

• Last Update Posted (Actual): April 28, 2026
• Last Update Submitted That Met QC Criteria: April 26, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: B2025-444

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No