DLCS for Predicting Neoadjuvant Chemotherapy Response

University of Chinese Academy Sciences

Status: Enrolling

Conditions: Deep Learning; CT Images; Tumor Regression Grade; Gastric Cancer; Neoadjuvant Chemotherapy

Treatments: Other: develop and visualized a radio-clinical signatures from pretreatment oversampled CT images

Study type: Observational

Funder types: Other

Identifiers

NCT05617469

AICT-01

Details and patient eligibility

About

The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.

Enrollment: 1,100 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. patients with GC/EGJC confirmed by pathological examination; 2) patients who underwent D2 lymphadenectomy; 3) patients who received at least two cycles of preoperative chemotherapy; 4) patients with negative resection margins; and 5) patients with complete CT image data and clinical data.

Exclusion criteria

1. patients unable to undergo D2 radical gastrectomy after neoadjuvant therapy; and 2) patients with incomplete CT images and clinical data.