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RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer
Journal article   Open access   Peer reviewed

RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer

Satvik Tripathi, Ethan Jacob Moyer, Alisha Isabelle Augustin, Alex Zavalny, Suhani Dheer, Rithvik Sukumaran, Daniel Schwartz, Brandon Gorski, Farouk Dako and Edward Kim
Informatics in medicine unlocked, v 33, 101062
2022
DOI: https://doi.org/10.1016/j.imu.2022.101062
url
https://doi.org/10.1016/j.imu.2022.101062View
Published, Version of Record (VoR)CC BY-NC-ND V4.0,  Open

Abstract

Deep learning EfficientNet Gene prediction Radiogenomics Lung Cancer
In this paper, we present our methodology that can be used for predicting gene mutation in patients with non-small cell lung cancer (NSCLC). There are three major types of gene mutations that a NSCLC patient’s gene structure can change to: epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and Anaplastic lymphoma kinase (ALK). We worked with the clinical and genomics data for each of the 130 patients as well their corresponding PET/CT scans. We preprocessed all of the data and then built a novel pipeline to integrate both the image and tabular data. We built a novel pipeline that used a fusion of Convolutional Neural Networks and Dense Neural Networks. Also, using a search approach, we pick an ensemble of deep learning models to classify the separate gene mutations. These models include EfficientNets, SENet, and ResNeXt WSL, among others. Our model achieved a high area under curve (AUC) score of 94% in predicting gene mutation.

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