CT Images, Deep Learning, Gastric Cancer, Neoadjuvant Chemotherapy, Tumor Regression Grade
Conditions
Brief summary
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.
Interventions
OTHERdevelop and visualized a radio-clinical signatures from pretreatment oversampled CT images
develop and visualized a radio-clinical signatures from pretreatment oversampled CT images to predict the neoadjuvant chemotherapy response and prognosis
Sponsors
Zhejiang Cancer Hospital
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Tumor regression grade | 3 months | Tumor regression grade |
Countries
China
Outcome results
None listed