Cargando…
Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
OBJECTIVE: This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images. BACKGROUND: nCRT followed by total mesorectal excision (...
Autores principales: | Lou, Xiaoying, Zhou, Niyun, Feng, Lili, Li, Zhenhui, Fang, Yuqi, Fan, Xinjuan, Ling, Yihong, Liu, Hailing, Zou, Xuan, Wang, Jing, Huang, Junzhou, Yun, Jingping, Yao, Jianhua, Huang, Yan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214314/ https://www.ncbi.nlm.nih.gov/pubmed/35756653 http://dx.doi.org/10.3389/fonc.2022.807264 |
Ejemplares similares
-
Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study
por: Shao, Lizhi, et al.
Publicado: (2020) -
Clinical significance of adjuvant chemotherapy for pathological
complete response rectal cancer patients with acellular mucin pools after
neoadjuvant chemoradiotherapy
por: Chen, Mian, et al.
Publicado: (2023) -
Predictive Factors for Pathologic Complete Response Following Neoadjuvant Chemoradiotherapy for Rectal Cancer
por: Zhang, Qi, et al.
Publicado: (2021) -
Association between adjuvant chemotherapy and survival in patients with rectal cancer and pathological complete response after neoadjuvant chemoradiotherapy and resection
por: He, Fang, et al.
Publicado: (2020) -
Clinical parameters predicting pathologic complete response following neoadjuvant chemoradiotherapy for rectal cancer
por: Zeng, Wei-Gen, et al.
Publicado: (2015)