Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

被引:467
作者
Berenguer, Roberto [1 ]
del Rosario Pastor-Juan, Maria [3 ]
Canales-Vazquez, Jesus [4 ]
Castro-Garcia, Miguel [4 ]
Villas, Maria Victoria [2 ]
Mansilla Legorburo, Francisco [5 ]
Sabater, Sebastia [2 ]
机构
[1] CHUA, Dept Med Phys, C Hnos Falco 37, Albacete 02006, Spain
[2] CHUA, Dept Radiat Oncol, C Hnos Falco 37, Albacete 02006, Spain
[3] CHUA, Dept Radiol, C Hnos Falco 37, Albacete 02006, Spain
[4] Univ Castilla La Mancha, Renewable Energy Res Inst, Albacete, Spain
[5] Mansilla Diagnost Por Imagen, Albacete, Spain
关键词
TEXTURE ANALYSIS; PET RADIOMICS; LUNG-CANCER; TEST-RETEST; REPRODUCIBILITY; IMAGES; STABILITY;
D O I
10.1148/radiol.2018172361
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
Purpose: To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods: Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results: A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion: Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy. (c) RSNA, 2018
引用
收藏
页码:407 / 415
页数:9
相关论文
共 36 条
  • [11] Characterization of PET/CT images using texture analysis: the past, the presenta... any future?
    Hatt, Mathieu
    Tixier, Florent
    Pierce, Larry
    Kinahan, Paul E.
    Le Rest, Catherine Cheze
    Visvikis, Dimitris
    [J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2017, 44 (01) : 151 - 165
  • [12] NSCLC tumor shrinkage prediction using quantitative image features
    Hunter, Luke A.
    Chen, Yi Pei
    Zhang, Lifei
    Matney, Jason E.
    Choi, Hiesun
    Kry, Stephen F.
    Martel, Mary K.
    Stingo, Francesco
    Liao, Zhongxing
    Gomez, Daniel
    Yang, Jinzhong
    Court, Laurence E.
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2016, 49 : 29 - 36
  • [13] High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images
    Hunter, Luke A.
    Krafft, Shane
    Stingo, Francesco
    Choi, Haesun
    Martel, Mary K.
    Kry, Stephen F.
    Court, Laurence E.
    [J]. MEDICAL PHYSICS, 2013, 40 (12)
  • [14] The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions
    Kessler, Larry G.
    Barnhart, Huiman X.
    Buckler, Andrew J.
    Choudhury, Kingshuk Roy
    Kondratovich, Marina V.
    Toledano, Alicia
    Guimaraes, Alexander R.
    Filice, Ross
    Zhang, Zheng
    Sullivan, Daniel C.
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2015, 24 (01) : 9 - 26
  • [15] Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability
    Kim, Hyungjin
    Park, Chang Min
    Lee, Myunghee
    Park, Sang Joon
    Song, Yong Sub
    Lee, Jong Hyuk
    Hwang, Eui Jin
    Goo, Jin Mo
    [J]. PLOS ONE, 2016, 11 (10):
  • [16] Radiomics: the process and the challenges
    Kumar, Virendra
    Gu, Yuhua
    Basu, Satrajit
    Berglund, Anders
    Eschrich, Steven A.
    Schabath, Matthew B.
    Forster, Kenneth
    Aerts, Hugo J. W. L.
    Dekker, Andre
    Fenstermacher, David
    Goldgof, Dmitry B.
    Hall, Lawrence O.
    Lambin, Philippe
    Balagurunathan, Yoganand
    Gatenby, Robert A.
    Gillies, Robert J.
    [J]. MAGNETIC RESONANCE IMAGING, 2012, 30 (09) : 1234 - 1248
  • [17] Radiomics: Extracting more information from medical images using advanced feature analysis
    Lambin, Philippe
    Rios-Velazquez, Emmanuel
    Leijenaar, Ralph
    Carvalho, Sara
    van Stiphout, Ruud G. P. M.
    Granton, Patrick
    Zegers, Catharina M. L.
    Gillies, Robert
    Boellard, Ronald
    Dekker, Andre
    Aerts, Hugo J. W. L.
    [J]. EUROPEAN JOURNAL OF CANCER, 2012, 48 (04) : 441 - 446
  • [18] 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers
    Larue, Ruben T. H. M.
    Van De Voorde, Lien
    van Timmeren, Janna E.
    Leijenaar, Ralph T. H.
    Berbee, Maaike
    Sosef, Meindert N.
    Schreurs, Wendy M. J.
    van Elmpt, Wouter
    Lambin, Philippe
    [J]. RADIOTHERAPY AND ONCOLOGY, 2017, 125 (01) : 147 - 153
  • [19] Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
    Leijenaar, Ralph T. H.
    Carvalho, Sara
    Velazquez, Emmanuel Rios
    Van Elmpt, Wouter J. C.
    Parmar, Chintan
    Hoekstra, Otto S.
    Hoekstra, Corneline J.
    Boellaard, Ronald
    Dekker, Andre L. A. J.
    Gillies, Robert J.
    Aerts, Hugo J. W. L.
    Lambin, Philippe
    [J]. ACTA ONCOLOGICA, 2013, 52 (07) : 1391 - 1397
  • [20] Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings
    Lu, Lin
    Ehmke, Ross C.
    Schwartz, Lawrence H.
    Zhao, Binsheng
    [J]. PLOS ONE, 2016, 11 (12):