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An deep learning and radiomics Model Based on the whole region-subregion of CT images to accurately Predict postoperative prognosis of locally advanced gastric cancer

An deep learning and radiomics Model Based on the whole region-subregion of CT images to accurately Predict postoperative prognosis of locally advanced gastric cancer

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400082977
Enrollment
Unknown
Registered
2024-04-12
Start date
2023-09-01
Completion date
Unknown
Last updated
2024-04-15

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

Conditions

gastric cancer

Interventions

local advanced gastric cancer :None

Sponsors

Shanxi Provincial Cancer Hospital/Chinese Academy of Medical Sciences Cancer Hospital Shanxi Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: (1) The patient underwent radical gastrectomy and was pathologically confirmed as locally advanced gastric adenocarcinoma (pT3-4 and/or N+, M0). (2) No neoadjuvant treatment was performed before surgery; (3) Standard upper abdominal enhanced CT scan before treatment; (4) Complete clinical and pathological information.

Exclusion criteria

Exclusion criteria: (1)The patient underwent other treatments before CT examination; (2)CT image quality is poor or artifacts are heavy; (3) Lesions was not visible on CT images.

Design outcomes

Primary

MeasureTime frame
Overall survival;—

Secondary

MeasureTime frame
progression-free survival;—

Countries

China

Contacts

Public ContactXiaotang Yang

Shanxi Cancer Hospital/Cancer Hospital of the Chinese Academy of Medical Sciences, Shanxi Hospital

yangxt210@126.com+86 159 3515 1002

Outcome results

None listed

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