Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients.

ABSTRACT:

Objective

To investigate the ability of ultrasomics to noninvasively predict epidermal growth factor receptor (EGFR) expression status in patients with hepatocellular carcinoma (HCC).

Methods

198 HCC patients were comprised in the study (n = 138 in the training dataset and n = 60 in the test dataset). EGFR expression was detected by immunohistochemistry. Ultrasomics features from gray-scale ultrasound images were extracted. Intra-class correlation coefficient (ICC) screening, variance filtering, mutual information method, and extreme gradient boosting (XGboost) embedding method were applied for selecting the best features. Random forest (RF), XGBoost, support vector machine (SVM), decision tree (DT), and logistic regression (LR) 5 machine learning algorithms we

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11609927 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature