APA (7th ed.) Citation

J, Y., J, L., X, K., P, Y., W, W., Z, W., . . . X, W. (2026). Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer. Frontiers in oncology, 16, 1770037. https://doi.org/10.3389/fonc.2026.1770037

Chicago Style (17th ed.) Citation

J, Yue, et al. "Development and Validation of a Pathomics-driven Machine Learning Model for Individualized Prediction of Neoadjuvant Chemotherapy Response and Early Recurrence in HR-positive, HER2-negative Breast Cancer." Frontiers in Oncology 16 (2026): 1770037. https://doi.org/10.3389/fonc.2026.1770037.

MLA (9th ed.) Citation

J, Yue, et al. "Development and Validation of a Pathomics-driven Machine Learning Model for Individualized Prediction of Neoadjuvant Chemotherapy Response and Early Recurrence in HR-positive, HER2-negative Breast Cancer." Frontiers in Oncology, vol. 16, 2026, p. 1770037, https://doi.org/10.3389/fonc.2026.1770037.