DLCS for Predicting Neoadjuvant Chemotherapy Response

Zhejiang Cancer Hospital1,100 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,100

Conditions

Gastric Cancer CT Images Deep Learning Neoadjuvant Chemotherapy Tumor Regression Grade

Interventions

develop and visualized a radio-clinical signatures from pretreatment oversampled CT imagesOTHER

develop and visualized a radio-clinical signatures from pretreatment oversampled CT images to predict the neoadjuvant chemotherapy response and prognosis

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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.

Locations (1)

Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)

Hangzhou, Zhejiang, China

RECRUITING

Outcomes

Primary Outcomes

Tumor regression grade

Time frame: 3 months

Data from ClinicalTrials.gov

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