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September 17, 2025

Radiomics Based on Magnetic Resonance Imaging in Differentiation of Hepatic micro-abscess from Hepatic micro-metastasis: A Multicenter Study

Puntos clave

The MRI-based radiomics model effectively differentiates hepatic micro-abscess from micro-metastasis.
Machine learning models demonstrated high efficacy, with statistical analysis confirming reliability in classification.
Chi-square and binary logistic regression analyses were used for evaluating the model's performance.
These findings are critical for selecting optimal treatment and improving patient prognosis.

Resumen

Motivation: The clinical and imaging characteristics of hepatic micro-abscesses and hepatic micro-metastases may exhibit certain similarities. Goal(s): To evaluate the feasibility of using magnetic resonance imaging (MRI)-based radiomics model to differentiate hepatic micro-abscess from hepatic micro-metastasis. Approach: MRI scans using 1.5 or 3.0 T systems from various manufacturers and the uAI Research Portal (United Imaging Intelligence, China) were used for image acquisition and post-processing. Chi-square test, Binary logistic regression analysis, and Delong test were used for statistical analysis. Results: Various machine learning models utilizing semantic features demonstrate high efficacy in distinguishing hepatic micro-abscesses from hepatic micro-metastases. Impact: Our findings regarding Differentiation of Hepatic micro-abscess from Hepatic micro-metastasis is essential in selecting the best treatment option and enhancing prognosis.

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