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
An EGFR gene mutation status prediction method, with high-performance thanks to an intelligent prediction model entrained by highly accurate annotated data is proposed. The outcome of this project will facilitate rapid decision-making when applying a TKI as an initial treatment.
Authors
- Nizar El Bouardi
- M. Serraj (ORCID: https://orcid.org/0000-0002-0750-3669)
- Anass Benfares (ORCID: https://orcid.org/0000-0002-2061-1058)
- Badreeddine Alami (ORCID: https://orcid.org/0000-0002-8014-683X)
- Mamoun Qjidaa
- Sara Boukansa (ORCID: https://orcid.org/0000-0003-4297-3380)
- Hassan Qjidaa (ORCID: https://orcid.org/0000-0003-4505-5243)
- Moulay Youssef Alaoui Lamrani
- Hind El Fatimi
- El Affar Anass
- Abdelali yahya Mourabiti
- Cherkaoui Abdeljabbar
- B. Amara
- Ouazzani Jamil Mohammed
- Smahi Mohammed
- Mustapha Maâroufi
Institutions
- Abdelmalek Essaâdi University (MA)
- Sustainable Energy Systems (United Kingdom) (GB)
- Sidi Mohamed Ben Abdellah University (MA)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2024-09-21
- DOI
- https://doi.org/10.1016/j.ejro.2024.100601
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Université Hassan II de Casablanca