Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

ABSTRACT:

Background and objective

Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.

Methods

In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Dise

SUBMITTER: Fanizzi A 

PROVIDER: S-EPMC11581389 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature