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

SIMPLE SUMMARY: Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Hyun-Soo, Lee, Kwang-sig, Seo, Bo-Kyoung, Kim, Eun-Sil, Cho, Kyu-Ran, Woo, Ok-Hee, Song, Sung-Eun, Lee, Ji-Young, Cha, Jaehyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656976/
https://www.ncbi.nlm.nih.gov/pubmed/34885124
http://dx.doi.org/10.3390/cancers13236013

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656976/
https://www.ncbi.nlm.nih.gov/pubmed/34885124
http://dx.doi.org/10.3390/cancers13236013

Ejemplares similares