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A radiomics and deep learning-based model for predicting prognosis of gastric cancer

A radiomics and deep learning-based model for predicting prognosis of gastric cancer

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200056583
Enrollment
Unknown
Registered
2022-02-08
Start date
2019-01-01
Completion date
Unknown
Last updated
2024-10-21

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

Conditions

gastirc neoplasm

Interventions

case series:none

Sponsors

Zhongshan Hospital, Xiamen University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
37 Years to 84 Years

Inclusion criteria

Inclusion criteria: 1. All patients were pathologically diagnosed as gastric adenocarcinoma; 2. The patient's heart, lung, liver, renal insufficiency and other important organ lesions; 3. The patient had no contraindications to enhanced CT scan, such as allergy to iodine contrast medium; 4. There was no neoadjuvant therapy such as radiotherapy and chemotherapy before operation.

Exclusion criteria

Exclusion criteria: 1. Image quality does not meet the requirements (such as large artifacts, more split-layer phenomenon, poor stomach filling); 2. Lesion diameter < 1cm, affecting the selection of interested area and lesion segmentation; 3. Incomplete clinicopathological data.

Design outcomes

Primary

MeasureTime frame
Effective atomic number;—

Countries

China

Contacts

Public ContactZeng Qiang

Zhongshan Hospital, Xiamen University School of Medicine

qianglf@xmu.edu.cn+86 18950085272

Outcome results

None listed

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