Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmenta...

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Detalles Bibliográficos
Autores principales: Tan, Jing-wen, Wang, Lan, Chen, Yong, Xi, WenQi, Ji, Jun, Wang, Lingyun, Xu, Xin, Zou, Long-kuan, Feng, Jian-xing, Zhang, Jun, Zhang, Huan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2020
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646171/
https://www.ncbi.nlm.nih.gov/pubmed/33193886
http://dx.doi.org/10.7150/jca.46704

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646171/
https://www.ncbi.nlm.nih.gov/pubmed/33193886
http://dx.doi.org/10.7150/jca.46704

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