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The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma: a Clinical Study

Not yet recruiting
Conditions
T-NK Cell Lymphoma
Registration Number
NCT06747299
Lead Sponsor
Ruijin Hospital
Brief Summary

Based on the PET/CT imaging data of patients with T-NK cell lymphoma, machine learning and deep learning methods are used to extract imaging features, establish a T-NK cell lymphoma prediction model, and provide more scientific and accurate prognosis prediction for the clinic.

Detailed Description

This study adopts a multicenter retrospective cohort study design,we provided PET/CT of 200 patients with T-NK cell lymphoma as an external validation set for model validation.

Recruitment & Eligibility

Status
NOT_YET_RECRUITING
Sex
All
Target Recruitment
200
Inclusion Criteria
  1. Pathological histology confirmed as T-NK Cell Lymphoma; 2.18F-FDG PET/CT examination before treatment; 3. Using modern best practice treatment options; 4. Complete clinicopathological and follow-up data were obtained.
Exclusion Criteria
  1. The patient had previously received antitumor therapy;
  2. The patient had a history of other tumors;
  3. Incomplete clinical information or imaging data;
  4. Concomitant other malignant tumors.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Evaluation the value of Artificial Intelligence-based 18F-FDG PET/CT of T-NK Cell LymphomaWithin 1 week of enrollment and after 3 months treatment

The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma

Secondary Outcome Measures
NameTimeMethod
Progress free survival3 years

Progress free survival

Overall survival3 years

Overall survival

Trial Locations

Locations (1)

Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine

🇨🇳

Shanghai, Shanghai, China

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