Predicting PIK3CA mutation in breast cancer with machine learning based multimodal image radiomics.

ABSTRACT:

Background

Gene status may help determine molecular targeted therapy for patients with breast cancer (BC). The present study aimed to explore machine learning (ML) approaches for the early prediction of PIK3CA mutation based on mammography (MMG) and ultrasound (US) radiomics.

Methods

We analysed 186 primary BC patients (130 in the training set and 56 in the test set) who underwent PIK3CA gene testing and with pretreatment MMG and US images from January 2020 to December 2023 at our institution. PIK3CA mutation results were used as the reference standard. The MMG and US radiomics features were extracted using 3D slicer and PyRadiomics software. Three machine learning (ML) algorithms, namely, logistic regression (LR), adaptive boosting (AdaBoost), and Naive Bayes (NB) were appl

SUBMITTER: Yao J 

PROVIDER: S-EPMC12642033 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature