Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes.

MLA

Tian, Dong, et al. “Machine Learning-Based Radiomic Computed Tomography Phenotyping of Thymic Epithelial Tumors: Predicting Pathological and Survival Outcomes.” The Journal of Thoracic and Cardiovascular Surgery, vol. 165, no. 2, Feb. 2023, p. 502. EBSCOhost, https://doi.org/10.1016/j.jtcvs.2022.05.046.

APA

Tian, D., Yan, H.-J., Shiiya, H., Sato, M., Shinozaki-Ushiku, A., & Nakajima, J. (2023). Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes. The Journal of Thoracic and Cardiovascular Surgery, 165(2), 502. https://doi.org/10.1016/j.jtcvs.2022.05.046

Chicago

Tian, Dong, Hao-Ji Yan, Haruhiko Shiiya, Masaaki Sato, Aya Shinozaki-Ushiku, and Jun Nakajima. 2023. “Machine Learning-Based Radiomic Computed Tomography Phenotyping of Thymic Epithelial Tumors: Predicting Pathological and Survival Outcomes.” The Journal of Thoracic and Cardiovascular Surgery 165 (2): 502. doi:10.1016/j.jtcvs.2022.05.046.