APA (7th ed.) Citation

Luna, J. M., Chao, H., Shinohara, R. T., Ungar, L. H., Cengel, K. A., Pryma, D. A., . . . Diffenderfer, E. S. (2020). Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation. Clinical and translational radiation oncology, 22, 69-75. https://doi.org/10.1016/j.ctro.2020.03.007

Chicago Style (17th ed.) Citation

Luna, José Marcio, et al. "Machine Learning Highlights the Deficiency of Conventional Dosimetric Constraints for Prevention of High-grade Radiation Esophagitis in Non-small Cell Lung Cancer Treated with Chemoradiation." Clinical and Translational Radiation Oncology 22 (2020): 69-75. https://doi.org/10.1016/j.ctro.2020.03.007.

MLA (9th ed.) Citation

Luna, José Marcio, et al. "Machine Learning Highlights the Deficiency of Conventional Dosimetric Constraints for Prevention of High-grade Radiation Esophagitis in Non-small Cell Lung Cancer Treated with Chemoradiation." Clinical and Translational Radiation Oncology, vol. 22, 2020, pp. 69-75, https://doi.org/10.1016/j.ctro.2020.03.007.