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.
Study Type
OBSERVATIONAL
Enrollment
437
Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China
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.
Time frame: Half a year
The time for the predictive model to perform HGP classification
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.
Time frame: Half a year
Progression-free survival of patients with different HGP classifications
The time from surgery to tumor progression in patients with colorectal cancer liver metastasis of different HGP types
Time frame: Through study completion, an average of 1 year
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