A Transfer Learning Radiomics Model for Predicting Response to Initial Transarterial Embolization in Patients with Gastroenteropancreatic Neuroendocrine Tumor Liver Metastases

First Affiliated Hospital, Sun Yat-Sen University257 enrolled

Overview

To develop and validate a CT-based transfer learning radiomics model for predicting response to initial TAE in GEP-NETLM patients and compare its performance with traditional radiomics and clinical models.

Study Type

OBSERVATIONAL

Enrollment

257

Conditions

Neuroendocrine Tumors, NET

Eligibility

Sex: ALLMin age: 18 YearsMax age: 78 Years

Medical Language ↔ Plain English

Inclusion Criteria: * (a) Clinical diagnosed as gastroenteropancreatic neuroendocrine tumor liver metastases; (b) Received initial transarterial embolization therapy; (c) Underwent multi-phase ceCT scans pre-TAE (≤1 month) and post-TAE (4-6 weeks). Exclusion Criteria: * (a) Neuroendocrine carcinoma (NEC) or other malignancies. (b) Received other liver metastasis treatments. (c) Lack of Multi-phase ceCT scans records. (d) CT images with artifacts or no visible lesions.

Outcomes

Primary Outcomes

The efficacy of TAE

Complete multiphase contrast-enhanced CT images were collected preoperatively and postoperatively. The efficacy of TAE was assessed based on RECIST 1.1 criteria. Patients were classified as objective responders (those achieving complete response \[CR\] or partial response \[PR\]) or non-responders (those with stable disease \[SD\] or progressive disease \[PD\]) through evaluation of target lesions.

Time frame: Within one month prior and 4-6 weeks following the initial TAE.

Data from ClinicalTrials.gov

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