Cargando…
Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predi...
Autores principales: | Chen, Jian, Hao, Li, Qian, Xiaojun, Lin, Lin, Pan, Yueyin, Han, Xinghua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352856/ https://www.ncbi.nlm.nih.gov/pubmed/35935976 http://dx.doi.org/10.3389/fimmu.2022.948601 |
Ejemplares similares
-
An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer
por: Fu, Changfang, et al.
Publicado: (2021) -
Pretreatment systemic inflammation response index is predictive of pathological complete response in patients with breast cancer receiving neoadjuvant chemotherapy
por: Dong, Jie, et al.
Publicado: (2021) -
An autophagic gene‐based signature to predict the survival of patients with low‐grade gliomas
por: Chen, Jian, et al.
Publicado: (2021) -
Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning
por: Wec, Anna Z., et al.
Publicado: (2021) -
Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI
por: Lu, Nannan, et al.
Publicado: (2021)