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The Clinical Value of Deep Learning-Based Imaging Analysis in Predicting Pathology and Prognosis for Colorectal Cancer

The Clinical Value of Deep Learning-Based Imaging Analysis in Predicting Pathology and Prognosis for Colorectal Cancer

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500106546
Enrollment
Unknown
Registered
2025-07-25
Start date
2025-07-28
Completion date
Unknown
Last updated
2025-08-18

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

Conditions

rectal cancers

Interventions

Stratified into high-risk and low-risk groups based on prognosis:NA

Sponsors

The Second Affiliated Hospital of Soochow University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age > 18 years; 2. Preoperative MRI performed within 2 weeks before surgery; 3. Histopathologically confirmed rectal adenocarcinoma (via surgical specimen).

Exclusion criteria

Exclusion criteria: 1. Prior rectal surgery or pelvic radiotherapy/chemotherapy before MRI examination; 2. Concurrent malignant tumors or history of anticancer therapy; 3. Tumors too small for accurate identification.

Design outcomes

Primary

MeasureTime frame
Overall Survival;Area Under the Receiver Operating Characteristic Curve;Concordance Index (C-statistic C-score);—

Countries

China

Contacts

Public ContactYueyue Zhang

The Second Affiliated Hospital of Soochow University

yhc1879407129@163.com+86 139 2379 3046

Outcome results

None listed

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