APA (7th ed.) Citation

H, F., Y, Y., J, D., Z, C., F, L., Z, L., . . . D, Z. (2025). Prediction of pediatric Wilms tumor recurrence using interpretable machine learning models: Insights from a 20-year real-world study and the prognostic value of Ki-67. American journal of cancer research, 15(9), 3835. https://doi.org/10.62347/GLJC3405

Chicago Style (17th ed.) Citation

H, Fang, et al. "Prediction of Pediatric Wilms Tumor Recurrence Using Interpretable Machine Learning Models: Insights from a 20-year Real-world Study and the Prognostic Value of Ki-67." American Journal of Cancer Research 15, no. 9 (2025): 3835. https://doi.org/10.62347/GLJC3405.

MLA (9th ed.) Citation

H, Fang, et al. "Prediction of Pediatric Wilms Tumor Recurrence Using Interpretable Machine Learning Models: Insights from a 20-year Real-world Study and the Prognostic Value of Ki-67." American Journal of Cancer Research, vol. 15, no. 9, 2025, p. 3835, https://doi.org/10.62347/GLJC3405.