Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed

Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

Version 1 : Received: 20 December 2021 / Approved: 21 December 2021 / Online: 21 December 2021 (12:28:44 CET)

A peer-reviewed article of this Preprint also exists.

Hong, R.; Liu, W.; Fenyö, D. Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning. BioMedInformatics 2022, 2, 101-105. Hong, R.; Liu, W.; Fenyö, D. Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning. BioMedInformatics 2022, 2, 101-105.

Journal reference: BioMedInformatics 2021, 2, 6
DOI: 10.3390/biomedinformatics2010006

Abstract

Studies have shown that STK11 mutation plays a critical role in affecting the lung adenocarcinoma (LUAD) tumor immune environment. By training an Inception-Resnet-v2 deep convolutional neural network model, we were able to classify STK11-mutated and wild type LUAD tumor histopathology images with a promising accuracy (per slide AUROC=0.795). Dimensional reduction of the activation maps before the output layer of the test set images revealed that fewer immune cells were accumulated around cancer cells in STK11-mutation cases. Our study demonstrated that deep convolutional network model can automatically identify STK11 mutations based on histopathology slides and confirmed that the immune cell density was the main feature used by the model to distinguish STK11-mutated cases.

Keywords

keyword; histopathology; deep learning; machine learning; cancer; lung adenocarcinoma; immune; computational pathology

Subject

MEDICINE & PHARMACOLOGY, Pathology & Pathobiology

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