Cargando…
Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method
BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. METHODS: A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = ...
Autores principales: | Qu, Yu‐Hong, Zhu, Hai‐Tao, Cao, Kun, Li, Xiao‐Ting, Ye, Meng, Sun, Ying‐Shi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons Australia, Ltd
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7049483/ https://www.ncbi.nlm.nih.gov/pubmed/31944571 http://dx.doi.org/10.1111/1759-7714.13309 |
Ejemplares similares
-
Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer:A multicenter study
por: Li, Bao, et al.
Publicado: (2022) -
Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer
por: Li, Fengling, et al.
Publicado: (2021) -
Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
por: Joo, Sunghoon, et al.
Publicado: (2021) -
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning
por: Aswolinskiy, Witali, et al.
Publicado: (2023) -
Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
por: Peng, Yunsong, et al.
Publicado: (2022)