Research on Early Recurrence of Locally Advanced Gastric Cancer Based on CT Radiomics Prediction

N/AEnrolling By InvitationNCT07683195

Liu Yang900 enrolled

Overview

This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images

Study Type

OBSERVATIONAL

Enrollment

900

Conditions

Gastric Cancer

Eligibility

Sex: ALLMin age: 18 YearsMax age: 85 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. pathology diagnosis of LAGC (pT2NxM0-pT4NxM0); 2. radical gastrectomy with D2 lymph node dissection (\>15 lymph nodes); 3. available clinicopathological data; 4. patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery. Exclusion Criteria: 1. preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy); 2. previous malignancies; 3. unsatisfactory gastric distention or inability to identify the primary tumor; 4. image artifacts.

Locations (1)

QianfoshanH

Jinan, Shandong, China

Outcomes

Primary Outcomes

Accuracy of early recurrence models

In this study, clinical data and contrast-enhanced CT imaging data of 550 patients with locally advanced gastric cancer from our hospital were collected. Machine learning and deep learning algorithms were applied to assess the early recurrence of patients within one year after surgery. The performance of the artificial intelligence model was evaluated from two dimensions: diagnostic accuracy and stability, and quantitative analysis of its performance was conducted using indicators including the area under the curve (AUC) and the precision-recall curve (PR curve).

Time frame: Immediately evaluated after the early recurrence model was built

Data from ClinicalTrials.gov

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