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

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: T-NK Cell Lymphoma

Study type: Observational

Funder types: Other

Identifiers

NCT06747299

RuijinH 2024-349

Details and patient eligibility

About

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.

Full 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.

Enrollment: 200 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

200 participants in 1 patient group

patients diagnosis of T-NK cell lymphoma