Order Article Reprints

Benfares A, Mourabiti Ay, Alami B, Boukansa S, Benomar I, El Bouardi N, Alaoui Lamrani MY, El Fatimi H, Amara B, Serraj M, et al. Nomogram Based on the Most Relevant Clinical, CT, and Radiomic Features, and a Machine Learning Model to Predict EGFR Mutation Status in Non-Small Cell Lung Cancer. Journal of Respiration. 2025; 5(3):11. https://doi.org/10.3390/jor5030011

Benfares, Anass, Abdelali yahya Mourabiti, Badreddine Alami, Sara Boukansa, Ikram Benomar, Nizar El Bouardi, Moulay Youssef Alaoui Lamrani, Hind El Fatimi, Bouchra Amara, Mounia Serraj, and et al. 2025. "Nomogram Based on the Most Relevant Clinical, CT, and Radiomic Features, and a Machine Learning Model to Predict EGFR Mutation Status in Non-Small Cell Lung Cancer" Journal of Respiration 5, no. 3: 11. https://doi.org/10.3390/jor5030011

Benfares, A., Mourabiti, A. y., Alami, B., Boukansa, S., Benomar, I., El Bouardi, N., Alaoui Lamrani, M. Y., El Fatimi, H., Amara, B., Serraj, M., Smahi, M., Cherkaoui, A., Qjidaa, M., Lakhssassi, A., Ouazzani Jamil, M., Maaroufi, M., & Qjidaa, H. (2025). Nomogram Based on the Most Relevant Clinical, CT, and Radiomic Features, and a Machine Learning Model to Predict EGFR Mutation Status in Non-Small Cell Lung Cancer. Journal of Respiration, 5(3), 11. https://doi.org/10.3390/jor5030011