Clinical trial NCT06760494: Microvascular Invasion Artificial Intelligence Prediction Via Contrast-enhanced Ultrasound With Explainability
Official title: Prediction of Microvascular Invasion in HCC Using Spatiotemporal Radiomics of Contrast-enhanced Ultrasound: a Deep Learning Model With Transcriptomics Correlation
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: HCC - Hepatocellular Carcinoma
Microvascular Invasion (MVI)
Keywords: hepatocellular carcinoma
microvascular invasion
contrast-enhanced ultrasound
deep learning
Lead sponsor: Chinese PLA General Hospital
Sponsor class: OTHER
Collaborators: name: Chinese Academy of Sciences
First posted: 2025-01-06
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients who underwent surgical treatment for HCC

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

1. Age \>18 years old.
2. The HCC diagnosis and the presence of MVI were confirmed by surgical pathology.
3. Complete and clear CEUS videos obtained within two weeks preoperatively.

Exclusion Criteria:

1. Unqualified CEUS images.
2. Missing surgical pathological diagnosis.
3. Lesions underwent local treatments.
4. Non-HCC diagnosis

Primary outcomes:
measure: area under operating characteristic curves (AUC)
timeFrame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
Secondary outcomes:
measure: ACC (accuracy)
timeFrame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
measure: Specificity
timeFrame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
measure: Sensitivity
timeFrame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: the First Affiliated Hospital of Sun Yat-sen University
state: Guangdong
city: Beijing
country: China
facility: Chinese PLA General Hospital
Study officials:
affiliation: Chinese PLA General Hospital
name: Chuan Pang
role: PRINCIPAL_INVESTIGATOR
References: