Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks.

ABSTRACT:

Background

Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression.

Methods

Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN's generalisation capabilities with an independent dataset with 653 samples from a

SUBMITTER: Pisula JI 

PROVIDER: S-EPMC10050393 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature