FindTrial
Sign In

Canceled by the investigator. Development and validation of a machine learning model to predict postoperative complications in rectal cancer

A machine learning model for preoperative assessment of the risk of serious postoperative complications in rectal cancer

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300072397
Enrollment
Unknown
Registered
2023-06-12
Start date
2023-06-25
Completion date
Unknown
Last updated
2023-11-27

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

Conditions

rectal cancer

Interventions

Gold Standard:pathological diagnosis
Index test:comprehensive complication index

Sponsors

Ninth People's Hospital of Soochow University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: (1) Age >=18 years old (2) Without receiving chemotherapy or immunotherapy before surgery (3) Without other malignant tumors existed simultaneously.

Exclusion criteria

Exclusion criteria: 1. Incomplete clinical information; 2. Pathological diagnosis of non-rectal cancer.

Design outcomes

Primary

MeasureTime frame
comprehensive complication index;hemoglobin;PNI score;Receiver Operating Characteristic;—

Countries

China

Contacts

Public ContactXiping Shen

Ninth People's Hospital of Soochow University

shenxiping2022@163.com+86 512 8882 2216

Outcome results

None listed

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