Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer

Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer

Purpose: To propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer. Materia...

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Bibliographic Details
Main Authors: Anass Benfares, Abdelali yahya Mourabiti, Badreddine Alami, Sara Boukansa, Nizar El Bouardi, Moulay Youssef Alaoui Lamrani, Hind El Fatimi, Bouchra Amara, Mounia Serraj, Smahi Mohammed, Cherkaoui Abdeljabbar, El affar Anass, Mamoun Qjidaa, Mustapha Maaroufi, Ouazzani Jamil Mohammed, Qjidaa Hassan
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:European Journal of Radiology Open
Subjects:
EGFR gene mutation
Convolutional Neuron Network
Lung tumor
Deep transfer learning
Stacking model
CT image
Online Access:http://www.sciencedirect.com/science/article/pii/S235204772400056X

Internet

http://www.sciencedirect.com/science/article/pii/S235204772400056X

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