Measuring Computed Tomography Scanner Variability of Radiomics Features

被引:543
|
作者
Mackin, Dennis [1 ]
Fave, Xenia [1 ,2 ]
Zhang, Lifei [1 ]
Fried, David [1 ,2 ]
Yang, Jinzhong [1 ]
Taylor, Brian [3 ,4 ]
Rodriguez-Rivera, Edgardo [5 ]
Dodge, Cristina [6 ]
Jones, Aaron Kyle [7 ]
Court, Laurence [1 ,7 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Radiat Phys, Houston, TX 77030 USA
[2] Univ Texas Hlth Sci Ctr Houston, Grad Sch Biomed Sci, Houston, TX 77030 USA
[3] Michael E DeBakey VA Med Ctr, Res Serv Line & Diagnost & Therapeut Care Line, Houston, TX USA
[4] Baylor Coll Med, Dept Radiol, Houston, TX 77030 USA
[5] Houston Methodist Hosp, Radiat Oncol Dept, Houston, TX USA
[6] Texas Childrens Hosp, Dept Diagnost Imaging, Houston, TX 77030 USA
[7] Univ Texas MD Anderson Canc Ctr, Dept Imaging Phys, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
radiomics; image texture; image features; CT; computed tomography; phantom; TEXTURAL FEATURES; CT; IMAGES; REPRODUCIBILITY; CANCER;
D O I
10.1097/RLI.0000000000000180
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives The purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies. Materials and Methods We compared the radiomics features calculated for non-small cell lung cancer (NSCLC) tumors from 20 patients with those calculated for 17 scans of a specially designed radiomics phantom. The phantom comprised 10 cartridges, each filled with different materials to produce a wide range of radiomics feature values. The scans were acquired using General Electric, Philips, Siemens, and Toshiba scanners from 4 medical centers using their routine thoracic imaging protocol. The radiomics feature studied included the mean and standard deviations of the CT numbers as well as textures derived from the neighborhood gray-tone difference matrix. To quantify the significance of the interscanner variability, we introduced the metric feature noise. To look for patterns in the scans, we performed hierarchical clustering for each cartridge. Results The mean CT numbers for the 17 CT scans of the phantom cartridges spanned from -864 to 652 Hounsfield units compared with a span of -186 to 35 Hounsfield units for the CT scans of the NSCLC tumors, showing that the phantom's dynamic range includes that of the tumors. The interscanner variability of the feature values depended on both the cartridge material and the feature, and the variability was large relative to the interpatient variability in the NSCLC tumors for some features. The feature interscanner noise was greatest for busyness and least for texture strength. Hierarchical clustering produced different clusters of the phantom scans for each cartridge, although there was some consistent clustering by scanner manufacturer. Conclusions The variability in the values of radiomics features calculated on CT images from different CT scanners can be comparable to the variability in these features found in CT images of NSCLC tumors. These interscanner differences should be considered, and their effects should be minimized in future radiomics studies.
引用
收藏
页码:757 / 765
页数:9
相关论文
共 50 条
  • [41] Recognition of liver tumors by predicted hyperspectral features based on patient's computed tomography radiomics features
    Wang, Xuehu
    Wang, Tianqi
    Zheng, Yongchang
    Yin, Xiaoping
    PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY, 2023, 42
  • [42] Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease
    Au, Ryan C.
    Tan, Wan C.
    Bourbeau, Jean
    Hogg, James C.
    Kirby, Miranda
    PHYSICS IN MEDICINE AND BIOLOGY, 2021, 66 (24)
  • [43] Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging
    Shu-Ju Tu
    Wei-Yuan Chen
    Chen-Te Wu
    European Radiology, 2021, 31 : 7865 - 7875
  • [44] Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging
    Linsalata, Stefania
    Borgheresi, Rita
    Marfisi, Daniela
    Barca, Patrizio
    Sainato, Aldo
    Paiar, Fabiola
    Neri, Emanuele
    Traino, Antonio Claudio
    Giannelli, Marco
    BIOMED RESEARCH INTERNATIONAL, 2022, 2022
  • [45] Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke
    Wang, Chendong
    Li, Tao
    Jia, Zhenyu
    Qiu, Kai
    Jiang, Runhao
    Hang, Yu
    Ni, Heng
    Cao, Yuezhou
    Zhao, Linbo
    Li, Mingfang
    Jiao, Jincheng
    Shi, Haibin
    Zhang, Jiulou
    Liu, Sheng
    JOURNAL OF STROKE & CEREBROVASCULAR DISEASES, 2023, 32 (11)
  • [46] Computed tomography-based radiomics for identifying pulmonary cryptococcosis mimicking lung cancer
    Zhang, Yongchang
    Chu, Zhigang
    Yu, Jianqun
    Chen, Xiaoyi
    Liu, Jing
    Xu, Jingxu
    Huang, Chencui
    Peng, Liqing
    MEDICAL PHYSICS, 2022, 49 (09) : 5943 - 5952
  • [47] Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study
    Liu, Ziwei
    Luo, Chun
    Chen, Xinjie
    Feng, Yanqiu
    Feng, Jieying
    Zhang, Rong
    Ouyang, Fusheng
    Li, Xiaohong
    Tan, Zhilin
    Deng, Lingda
    Chen, Yifan
    Cai, Zhiping
    Zhang, Ximing
    Liu, Jiehong
    Liu, Wei
    Guo, Baoliang
    Hu, Qiugen
    INTERNATIONAL JOURNAL OF SURGERY, 2024, 110 (02) : 1039 - 1051
  • [48] Computed tomography-based radiomics signature as a pretreatment predictor of progression-free survival in locally advanced hypopharyngeal carcinoma with a different response to induction chemotherapy
    Liu, Xiaobin
    Sun, Chuanqi
    Long, Miaomiao
    Yang, Yining
    Lin, Peng
    Xia, Shuang
    Shen, Wen
    EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, 2022, 279 (07) : 3551 - 3562
  • [49] Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication
    Lee, Geewon
    Park, Hyunjin
    Sohn, Insuk
    Lee, Seung-Hak
    Song, So Hee
    Kim, Hyeseung
    Lee, Kyung Soo
    Shim, Young Mog
    Lee, Ho Yun
    ONCOLOGIST, 2018, 23 (07) : 806 - 813
  • [50] Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques
    Kolossvary, Marton
    Kellermayer, Miklos
    Merkely, Bela
    Maurovich-Horvat, Pal
    JOURNAL OF THORACIC IMAGING, 2018, 33 (01) : 26 - 34