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Abstract #1304

Decoding heterogeneity in solitary HCC: OATP-dependent HBP-MRI radiomics predicts microvascular invasion & prognosis.

Yu can1 and Zhou Yang2
1The Affiliated Cancer Hospital of Harbin Medical University, harbin, China, 2The Affiliated Cancer Hospital of Harbin Medical University, Harbin, China

Synopsis

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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Keywords