Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis - Scorecard - MDSpire
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Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis
Clinical Scorecard: AI-Driven Radiomics for Assessing Diagnosis and Prognosis of Tumor Cluster-Encapsulating Vessels in Hepatocellular Carcinoma: A Comprehensive Review and Meta-Analysis
At a Glance
Category
Detail
Condition
Hepatocellular carcinoma (HCC)
Key Mechanisms
Vessels encapsulating tumor clusters (VETCs) as a poor prognostic factor; AI-based imaging for noninvasive prediction.
Target Population
Patients with hepatocellular carcinoma.
Care Setting
Oncology and radiology departments utilizing imaging techniques.
Key Highlights
HCC accounts for approximately 90% of liver cancer cases.
VETC positivity is associated with higher recurrence rates and poorer survival.
AI technologies show potential for noninvasive prediction of VETC status.
Traditional diagnostic methods are invasive and subject to bias.
Existing studies show variability in AI model performance.
Guideline-Based Recommendations
Diagnosis
Utilize AI-based imaging techniques for predicting VETC status.
Management
Tailor treatment strategies based on VETC status.
Monitoring & Follow-up
Implement postoperative surveillance guided by VETC prediction.
Risks
Invasive methods may miss VETC-positive areas due to sampling bias.
Patient & Prescribing Data
Patients diagnosed with hepatocellular carcinoma.
Early identification of VETC status can influence management decisions.
Clinical Best Practices
Adhere to PRISMA-DTA guidelines for systematic reviews.
Integrate risk-of-bias appraisal and GRADE certainty ratings in evaluations.
