APA (7th ed.) Citation

S, U., ASM, N., SS, A., Z, S., AA, A., & SS, I. (2026). Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma. Frontiers in artificial intelligence, 8, 1738174. https://doi.org/10.3389/frai.2025.1738174

Chicago Style (17th ed.) Citation

S, Ujjahan, Noman ASM, Al-Johani SS, Shinwari Z, Alaiya AA, and Islam SS. "Deep Learning and Machine Learning Integration of Radiomics and Transcriptomics Predicts Response-adapted Radiotherapy Outcome and Radiosensitivity in Resectable Locally Advanced Laryngeal Carcinoma." Frontiers in Artificial Intelligence 8 (2026): 1738174. https://doi.org/10.3389/frai.2025.1738174.

MLA (9th ed.) Citation

S, Ujjahan, et al. "Deep Learning and Machine Learning Integration of Radiomics and Transcriptomics Predicts Response-adapted Radiotherapy Outcome and Radiosensitivity in Resectable Locally Advanced Laryngeal Carcinoma." Frontiers in Artificial Intelligence, vol. 8, 2026, p. 1738174, https://doi.org/10.3389/frai.2025.1738174.