Author:
Yao Jiawen,Shi Yu,Cao Kai,Lu Le,Lu Jianping,Song Qike,Jin Gang,Xiao Jing,Hou Yang,Zhang Ling
Funder
National Natural Science Foundation of China
Subject
Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Reference76 articles.
1. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach;Aerts;Nat. Commun.,2014
2. Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis;Attiyeh;Ann. Surg. Oncol.,2018
3. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features;Bakas;Sci. Data,2017
4. Validation of the 6th edition AJCC pancreatic cancer staging system: report from the national cancer database;Bilimoria;Cancer Interdiscip. Int. J. Am. Cancer Soc.,2007
5. Pancreatic adenocarcinoma: quantitative CT features are correlated with fibrous stromal fraction and help predict outcome after resection;Cai;Eur. Radiol.,2020
Cited by
27 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献