An artificial intelligence (AI) model to predict MVI of HCC using contrast-enhanced ultrasound was constructed. This model also has biological explainability. The investigators named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability). The goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.
The presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical prognostic indicator, but its preoperative diagnosis remains challenging. Contrast-enhanced ultrasound (CEUS), with its dynamic microvascular imaging capability, holds promise in prediction of MVI. The investigators constructed an artificial intelligence (AI) model to predict MVI using contrast-enhanced ultrasound. This model also has biological explainability. We named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability). The goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China. The performance of MAPUSE is to be tested in two prospective testing cohorts from two centers in southern and northern China. Before surgery, patient CEUS videos will be collected and analysed by MAPUSE model to generate an MVI risk score. According to the postoperative pathological diagnosis of MVI (golden criterion), the result of MAPUSE will be evaluated. Parameters include area under curve (AUC), accuracy (ACC), sensitivity, specificity and F1-score.
Study Type
OBSERVATIONAL
Enrollment
250
Using the MAPUSE model to predict MVI status before surgical resection for HCC patients
the First Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
Chinese PLA General Hospital
Beijing, China
area under operating characteristic curves (AUC)
the area under operating characteristic curves (AUC) to evaluate the performance of MAPUSE model in predicting MVI in HCC patients
Time frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
ACC (accuracy)
The ratio of the number of samples correctly predicted by the model to the total number of samples
Time frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
Specificity
Proportion of all patients without MVI who are predicted negative by MAPUSE
Time frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
Sensitivity
The proportion of patients with MVI that MAPUSE correctly identifies
Time frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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