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CT-based convolutional neural network model predicts early recurrence in locally advanced gastric cancer

CT-based convolutional neural network model predicts early recurrence in locally advanced gastric cancer

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400087160
Enrollment
Unknown
Registered
2024-07-22
Start date
2024-08-01
Completion date
Unknown
Last updated
2024-07-29

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:Early recurrence, defined as occurred in the early postoperative recurrence within 1 year, according to the clinical imaging (abdominal ultrasound, CT and PET/CT), peritoneal effusion cy

Sponsors

Lishui Central Hospital of Zhejiang Province
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) Patients underwent pathology diagnosis of LAGC; (2) Radical gastrectomy with D2 lymph node dissection (>15 lymph nodes); (3) Abdominal enhanced CT was performed within 1 month before surgery; (4) Available clinicopathological data.

Exclusion criteria

Exclusion criteria: (1) Preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy; (2) Unsatisfactory gastric distention or inability to identify the primary tumour; (3) Image artifacts.

Design outcomes

Primary

MeasureTime frame
the area under the receiver operating characteristics (ROC) curve (AUC);

Secondary

MeasureTime frame
accuracy;sensitivity;specificity;

Countries

China

Contacts

Public ContactJi Jiansong

Lishui Central Hospital of Zhejiang Province

jijiansong@zju.edu.cn+86 578 228 5011

Outcome results

None listed

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