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

## TL;DR

This paper introduces a new model using MRI radiomics and machine learning to better predict microvascular invasion in liver cancer before surgery, improving treatment decisions.

## Contribution

The novel contribution is an optimized MR Radiomics combination strategy and machine learning predictor for preoperative MVI prediction in HCC.

## Key findings

- The proposed model achieved a mean AUC of 0.7962 ± 0.1700 in predicting MVI.
- The model uses 125 × N selected and 125 × 10 fused radiomics features with random forest for prediction.
- It outperforms existing models by avoiding subjective region determination and non-imaging data.

## Abstract

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.

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 × N abdominal enhanced T1-weighted magnetic resonance imaging (T1WI) images during the arterial phase, generating 125 × 512 × 512 × N HCC mask images. Second, 125 × 1,692 MR Radiomics features of HCC are extracted from abdominal enhanced T1WI images based on the HCC mask images. Third, the 125 × N selected and 125 × 10 fused MR Radiomics features are determined using the proposed optimized MR Radiomics combination strategy with 5-fold cross-validation. Finally, the best preoperative prediction model is constructed using a random forest (RF) predictor with 125 × N selected and 125 × 10 fused MR Radiomics features.

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 F1-score of 0.6943 ± 0.1437, and a mean AUC of 0.7962 ± 0.1700.

The proposed best preoperative prediction model can effectively predict MVI in HCC, potentially serving as a strong decision-making tool for these vulnerable populations.