Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis

NCT07088393 | Active Not Recruiting

• 1 other identifier: 2023ZSLYEC-256
• Study Type: observational
• Target: 437
• Locations: 1 country
• Sites: 1

Brief Summary

This study selected cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection, retrieved the pathological HE sections of the metastatic lesions, and constructed a predictive model. AI software was applied to delineate different types of regions, achieving full automation of HGP prediction and constructing a predictive model. Statistical analysis was conducted on the classification of histopathological growth patterns (HGP) of liver metastasis and the survival prognosis of patients, and the differences in prognosis among different HGP classification methods were compared. This provides a new method for judging prognosis and treatment for clinical treatment of colorectal cancer liver metastasis patients.

Conditions

Liver Metastases of Colorectal Cancer

Outcome Measures

Primary Outcomes (1)

• The accuracy rate of the predictive model for HGP classification

  We will build an AI prediction model for HGP prediction and verify the accuracy of the AI-assisted prediction model in classifying HGP.

  Half a year

Secondary Outcomes (2)

• The time for the predictive model to perform HGP classification

  Half a year

• Progression-free survival of patients with different HGP classifications

  Through study completion, an average of 1 year

Study Arms (1)

Colorectal cancer liver metastasis cohort

A total of 437 cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection were selected, with a total of 1205 tumor lesions. Pathological HE sections were retrieved and a predictive model was constructed. Among them, 301 cases were in the training set and 106 cases were in the validation set. After constructing the model, it was used to prospectively interpret 30 lesions. The interpretation result of a senior pathologist with a high professional title was taken as the standard to evaluate the accuracy and interpretation time of the model.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients must meet all inclusion and exclusion criteria. In addition, the patient should be thoroughly informed about the study, including the study visit schedule and required evaluations and all regulatory requirements for informed consent. The written informed consent should be obtained from the patient prior toenrollment. The following criteria apply to all patients enrolled onto the study unless otherwise specified.

You may qualify if:

• Patients with colorectal cancer liver metastases who underwent resection of liver metastases;
• Confirmed by a pathologist as having liver metastases from colorectal cancer;

You may not qualify if:

• Cases of colorectal cancer liver metastasis that cannot be classified by histopathology.

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Sixth Affiliated Hospital, Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

Location

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: July 10, 2025
• First Posted: July 28, 2025
• Study Start: July 9, 2025
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: July 28, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)