Predicting HER2 overexpression in prostate cancer using machine learning: implications for personalized therapy.

ABSTRACT:

Background

Human Epidermal Growth Factor Receptor 2 (HER2), a component of the epidermal growth factor receptor family, is thought to be related to advanced prostate cancer (PCa) when overexpressed. Currently, most research on HER2 is limited to molecular pathology, with relatively few studies focused on imaging aspects.

Objectives

To develop a predictive model by extracting high-throughput radiomics features from magnetic resonance imaging and combining them with clinical characteristics for predicting HER2 overexpression.

Materials and methods

A total of 201 patients who underwent radical prostatectomy and HER2 immunohistochemistry were retrospectively enrolled. These patients were randomly divided into a training set (n=160) and a test set (n=41). Multimodal radiom

SUBMITTER: Huang X 

PROVIDER: S-EPMC12834805 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature