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NCT Number: NCT07409168

Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NKTCL

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.

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Key information

Conditions

Natural Killer/T-cell Lymphoma

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Predictive accuracy of first-line treatment response (CR vs non-CR) according to Lugano 2014 criteria

    Time frame: From baseline to disease response and follow-up assessments, up to 3 years.

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Qingqing Cai, MD. PhD.

CONTACT

[email protected]

0208734282

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Official study title

Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NK/T-Cell Lymphoma

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Feb 13, 2026
Registry last updated
Apr 28, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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