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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-01-01

Study Completion Date

2027-12-31

Brief Summary

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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

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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.

Conditions

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T-NK Cell Lymphoma

Keywords

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T-NK cell Lymphoma PET/CT Diferential Diagnosis Efficacy Prediction Prognosis Prediction

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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patients diagnosis of T-NK cell lymphoma

No interventions assigned to this group

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

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GUO RUI

Deputy director

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine

Shanghai, Shanghai Municipality, China

Site Status

Countries

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China

Central Contacts

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Guo rui Deputy director

Role: CONTACT

Phone: 13361860108

Email: [email protected]

Other Identifiers

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RuijinH 2024-349

Identifier Type: -

Identifier Source: org_study_id