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

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

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.

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.

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.