Enhanced preoperative prediction for microvascular invasion in hepatocellular carcinoma through an optimized MR Radiomics combination strategy and machine learning predictor.

Background: Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a crucial step toward personalized treatment, improved treatment outcomes, and enhanced patient survival. However, the disadvantage of existing prediction models of MVI in HCC based on enhanced magnetic resonance imaging (MRI) is that they require combining non-imaging information from enhanced MRI, or determining the perioperative region is highly subjective. These disadvantages are not conducive to the clinical application of predictive models, which hinders clinical decision-making and management for these vulnerable populations. Methods: To address the problem of combining non-imaging information from enhanced MRI with the highly subjective determination of the perioperative region, we propose an enhanced preoperative prediction model for MVI in HCC using an optimized MR Radiomics combination strategy and a machine learning predictor. First, the HCC was manually segmented from 125 × 512 × 512 × Results: The proposed MVI preoperative prediction model (RF + LASSO + SPECTRAL-10) achieves a mean accuracy of 0.7520 ± 0.0867, a mean precision of 0.7354 ± 0.1863, a mean recall of 0.6955 ± 0.2203, a mean Discussion: The proposed best preoperative prediction model can effectively predict MVI in HCC, potentially serving as a strong decision-making tool for these vulnerable populations.