Robustness of radiomic features in magnetic resonance imaging: review and a phantom study

作     者：Renee Cattell Shenglan Chen Chuan Huang 

作者机构：Department of Biomedical EngineeringStony Brook UniversityStony BrookNY 11794USA Department of RadiologyStony Brook MedicineStony BrookNY 11794USA Department of PsychiatryStony Brook MedicineStony BrookNY 11794USA 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算（英文）)

年 卷 期：2019年第2卷第1期

页      面：176-191页

核心收录：

学科分类：0502[文学-外国语言文学] 1303[艺术学-戏剧与影视学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1301[艺术学-艺术学理论] 08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0503[文学-新闻传播学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is in part funded by Walk-for-Beauty Foundation and Carol M.Baldwin Breast Cancer Research Foundation 

主　　题：Radiomics Robustness Magnetic resonance imaging Imaging biomarker Phantom study 

摘      要：Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single *** imaging biomarkers can aid in the generation of prediction models aimed to further personalized ***,the generalizability of the model is dependent on the robustness of these *** purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance ***,a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions(signal to noise ratio,region of interest delineation,voxel size change and normalization methods)using intraclass correlation *** features extracted in this phantom study include first order,shape,gray level cooccurrence matrix and gray level run length *** features are found to be non-robust to changing *** robustness assessment prior to feature selection,especially in the case of combining multi-institutional data,may be *** investigation is needed in this area of research.