Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology.

MLA

Levy, Joshua J., et al. “Examining Longitudinal Markers of Bladder Cancer Recurrence through a Semiautonomous Machine Learning System for Quantifying Specimen Atypia from Urine Cytology.” Cancer Cytopathology, vol. 131, no. 9, Sept. 2023, pp. 561–73. EBSCOhost, https://doi.org/10.1002/cncy.22725.

APA

Levy, J. J., Chan, N., Marotti, J. D., Rodrigues, N. J., Ismail, A. A. O., Kerr, D. A., Gutmann, E. J., Glass, R. E., Dodge, C. P., Suriawinata, A. A., Christensen, B. C., Liu, X., & Vaickus, L. J. (2023). Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology. Cancer Cytopathology, 131(9), 561–573. https://doi.org/10.1002/cncy.22725

Chicago

Levy, Joshua J., Natt Chan, Jonathan D. Marotti, Nathalie J. Rodrigues, A. Aziz O. Ismail, Darcy A. Kerr, Edward J. Gutmann, et al. 2023. “Examining Longitudinal Markers of Bladder Cancer Recurrence through a Semiautonomous Machine Learning System for Quantifying Specimen Atypia from Urine Cytology.” Cancer Cytopathology 131 (9): 561–73. doi:10.1002/cncy.22725.