Cite
Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.
MLA
Park, Jiwoo, et al. “Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.” Diagnostics (2075-4418), vol. 13, no. 19, Oct. 2023, p. 3031. EBSCOhost, https://doi.org/10.3390/diagnostics13193031.
APA
Park, J., Kim, M. J., Yoon, J.-H., Han, K., Kim, E.-K., Sohn, J. H., Lee, Y. H., & Yoo, Y. (2023). Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features. Diagnostics (2075-4418), 13(19), 3031. https://doi.org/10.3390/diagnostics13193031
Chicago
Park, Jiwoo, Min Jung Kim, Jong-Hyun Yoon, Kyunghwa Han, Eun-Kyung Kim, Joo Hyuk Sohn, Young Han Lee, and Yangmo Yoo. 2023. “Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.” Diagnostics (2075-4418) 13 (19): 3031. doi:10.3390/diagnostics13193031.