Order Article Reprints

Park H-S, Lee K-s, Seo B-K, Kim E-S, Cho K-R, Woo O-H, Song S-E, Lee J-Y, Cha J. Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients. Cancers. 2021; 13(23):6013. https://doi.org/10.3390/cancers13236013

Park, Hyun-Soo, Kwang-sig Lee, Bo-Kyoung Seo, Eun-Sil Kim, Kyu-Ran Cho, Ok-Hee Woo, Sung-Eun Song, Ji-Young Lee, and Jaehyung Cha. 2021. "Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients" Cancers 13, no. 23: 6013. https://doi.org/10.3390/cancers13236013

Park, H.-S., Lee, K.-s., Seo, B.-K., Kim, E.-S., Cho, K.-R., Woo, O.-H., Song, S.-E., Lee, J.-Y., & Cha, J. (2021). Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients. Cancers, 13(23), 6013. https://doi.org/10.3390/cancers13236013