Keywords: Diagnosis/Prediction, Analysis/Processing
Motivation: HCC is deadly, and MVI impacts prognosis.
Goal(s): This study integrates multiple machine learning methods to predict MVI in HCC, revealing an ECM-OATP1B3 correlation and enhancing the biological interpretability of the radiomics model for prognostic assessment.
Approach: Machine learning methods to identify radiomic features and VOI regions. RNA sequencing explored biological differences between two groups. The model's efficacy was validated through immunohistochemistry and staining.
Results: The model identified optimal methods and margin regions, with AUC values of 0.80, 0.76, and 0.74. It stratified patients into two groups with significant survival differences. RNA sequencing revealed ECM-related genes and pathways. Immunohistochemistry validated the model's reliability.
Impact: This study integrates multiple machine learning methods to predict MVI in HCC, revealing an ECM-OATP1B3 correlation and enhancing the biological interpretability of the radiomics model for prognostic assessment.
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