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CT-Based Radiomics Machine Learning Model for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer: A Multi-Cohort Real-World Study

CT-Based Radiomics Machine Learning Model for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer: A Multi-Cohort Real-World Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108648
Enrollment
Unknown
Registered
2025-09-03
Start date
2025-09-10
Completion date
Unknown
Last updated
2025-09-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastric cancer

Interventions

Gold Standard:Pathological examination
Index test:A fusion machine learning model based on radiomics and clinical features before neoadjuvant immunotherapy

Sponsors

The First Medical Center of Chinese PLA General Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Pathological diagnosis of gastric adenocarcinoma; 2. Adapt to neoadjuvant immunotherapy; 3. No distant metastasis occurred; 4. Complete clinicopathological information can be collected.

Exclusion criteria

Exclusion criteria: 1. Received other previous treatments, such as radiotherapy, targeted therapy, or chemotherapy alone; 2. Missing baseline enhanced CT intravenous images before neoadjuvant immunotherapy or poor image quality.

Design outcomes

Primary

MeasureTime frame
Area Under the Curve;Sensitivity;Specificity;

Secondary

MeasureTime frame
F1 score;

Countries

China

Contacts

Public ContactWei Bo

The First Medical Center of Chinese PLA General Hospital

weibo@vip.163.com+86 139 1003 8055

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026