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

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07088393

Enrollment

437

Registered

2025-07-28

Start date

2025-07-09

Completion date

2025-12-31

Last updated

2025-07-28

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

Conditions

Liver Metastases of Colorectal Cancer

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.

Interventions

None listed

Study design

Observational model

CASE_ONLY

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

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

Exclusion criteria

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

Design outcomes

Primary

Measure	Time frame	Description
The accuracy rate of the predictive model for HGP classification	Half a year	We will build an AI prediction model for HGP prediction and verify the accuracy of the AI-assisted prediction model in classifying HGP.

Secondary

Measure	Time frame	Description
The time for the predictive model to perform HGP classification	Half a year	We will measure the time it takes for the AI-assisted predictive model to classify HGP and compare the difference in interpretation time between the model and pathologists.
Progression-free survival of patients with different HGP classifications	Through study completion, an average of 1 year	The time from surgery to tumor progression in patients with colorectal cancer liver metastasis of different HGP types

Countries

China

Outcome results