PuSH - Publikationsserver des Helmholtz Zentrums München: Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks.

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Pisula, J.I.* ; Datta, R.R.* ; Valdez, L.B.* ; Avemarg, J.R.* ; Jung, J.O.* ; Plum, P.* ; Löser, H.* ; Lohneis, P.* ; Meuschke, M.* ; Dos Santos, D.P.* ; Gebauer, F.* ; Quaas, A.* ; Walch, A.K. ; Bruns, C.J.* ; Lawonn, K.* ; Popp, F.C.* ; Bozek, K.*

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

Br. J. Cancer 128, 1369-1376 (2023)

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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 separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. RESULTS: We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. CONCLUSIONS: We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

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