메뉴 건너뛰기




Volumn 90, Issue 1070, 2017, Pages

Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CALIBRATION; CLINICAL PRACTICE; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE RECONSTRUCTION; INFORMATION; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; QUANTITATIVE ANALYSIS; QUANTITATIVE RADIOMICS; RADIOGRAPHY; REVIEW; SOFTWARE; STANDARDIZATION; TISSUE CHARACTERIZATION; ULTRASOUND; DIAGNOSTIC IMAGING; IMAGE PROCESSING; NEOPLASM; PROCEDURES;

EID: 85011697926     PISSN: 00071285     EISSN: None     Source Type: Journal    
DOI: 10.1259/bjr.20160665     Document Type: Review
Times cited : (290)

References (95)
  • 1
    • 57849117384 scopus 로고    scopus 로고
    • New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
    • Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45: 228-47. doi: https://doi.org/ 10.1016/j.ejca.2008.10.026
    • (2009) Eur J Cancer , vol.45 , pp. 228-247
    • Eisenhauer, E.A.1    Therasse, P.2    Bogaerts, J.3    Schwartz, L.H.4    Sargent, D.5    Ford, R.6
  • 2
    • 66149139452 scopus 로고    scopus 로고
    • From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors
    • Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50(Suppl 1): 122S-50S. doi: https://doi.org/10.2967/ jnumed.108.057307
    • (2009) J Nucl Med , vol.50 , pp. 122S-150S
    • Wahl, R.L.1    Jacene, H.2    Kasamon, Y.3    Lodge, M.A.4
  • 3
    • 84982105864 scopus 로고    scopus 로고
    • Machine learning approaches in medical image analysis: From detection to diagnosis
    • de Bruijne M. Machine learning approaches in medical image analysis: from detection to diagnosis. Med Image Anal 2016; 33: 94-7. doi: https://doi.org/10.1016/j. media.2016.06.032
    • (2016) Med Image Anal , vol.33 , pp. 94-97
    • De Bruijne, M.1
  • 4
    • 20144388671 scopus 로고    scopus 로고
    • A computeraided diagnosis (CAD) system in lung cancer screening with computed tomography
    • Abe Y, Hanai K, Nakano M, Ohkubo Y, Hasizume T, Kakizaki T, et al. A computeraided diagnosis (CAD) system in lung cancer screening with computed tomography. Anticancer Res 2005; 25: 483-8.
    • (2005) Anticancer Res , vol.25 , pp. 483-488
    • Abe, Y.1    Hanai, K.2    Nakano, M.3    Ohkubo, Y.4    Hasizume, T.5    Kakizaki, T.6
  • 5
    • 84937522759 scopus 로고    scopus 로고
    • Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: Importance of radiologists role and successful observer study
    • Li F. Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologists role and successful observer study. Radiol Phys Technol 2015; 8: 161-73. doi: https://doi. org/10.1007/s12194-015-0319-0
    • (2015) Radiol Phys Technol , vol.8 , pp. 161-173
    • Li, F.1
  • 6
    • 84857037061 scopus 로고    scopus 로고
    • Radiomics: Extracting more information from medical images using advanced feature analysis
    • Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-6. doi: https://doi.org/10.1016/j. ejca.2011.11.036
    • (2012) Eur J Cancer , vol.48 , pp. 441-446
    • Lambin, P.1    Rios-Velazquez, E.2    Leijenaar, R.3    Carvalho, S.4    Van Stiphout, R.G.5    Granton, P.6
  • 7
    • 84908211707 scopus 로고    scopus 로고
    • Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer
    • Fried DV, Tucker SL, Zhou S, Liao Z, Mawlawi O, Ibbott G, et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2014; 90: 834-42. doi: https://doi.org/ 10.1016/j.ijrobp.2014.07.020
    • (2014) Int J Radiat Oncol Biol Phys , vol.90 , pp. 834-842
    • Fried, D.V.1    Tucker, S.L.2    Zhou, S.3    Liao, Z.4    Mawlawi, O.5    Ibbott, G.6
  • 8
    • 84911973434 scopus 로고    scopus 로고
    • Test-retest reproducibility analysis of lung CT image features
    • Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 2014; 27: 805-23. doi: https:// doi.org/10.1007/s10278-014-9716-x
    • (2014) J Digit Imaging , vol.27 , pp. 805-823
    • Balagurunathan, Y.1    Kumar, V.2    Gu, Y.3    Kim, J.4    Wang, H.5    Liu, Y.6
  • 9
    • 84943774122 scopus 로고    scopus 로고
    • Measuring computed tomography scanner variability of radiomics features
    • Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015; 50: 757-65. doi: https://doi.org/10.1097/ rli.0000000000000180
    • (2015) Invest Radiol , vol.50 , pp. 757-765
    • Mackin, D.1    Fave, X.2    Zhang, L.3    Fried, D.4    Yang, J.5    Taylor, B.6
  • 10
    • 84962314053 scopus 로고    scopus 로고
    • Reproducibility of radiomics for deciphering tumor phenotype with imaging
    • Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016; 6: 23428. doi: https://doi.org/ 10.1038/srep23428
    • (2016) Sci Rep , vol.6 , pp. 23428
    • Zhao, B.1    Tan, Y.2    Tsai, W.Y.3    Qi, J.4    Xie, C.5    Lu, L.6
  • 11
    • 67650085792 scopus 로고    scopus 로고
    • Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer
    • Zhao B, James LP, Moskowitz CS, Guo P, Ginsberg MS, Lefkowitz RA, et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 2009; 252: 263-72. doi: https://doi.org/10.1148/ radiol.2522081593
    • (2009) Radiology , vol.252 , pp. 263-272
    • Zhao, B.1    James, L.P.2    Moskowitz, C.S.3    Guo, P.4    Ginsberg, M.S.5    Lefkowitz, R.A.6
  • 12
    • 84946918741 scopus 로고    scopus 로고
    • Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?
    • Fave X, Mackin D, Yang J, Zhang J, Fried D, Balter P, et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 2015; 42: 6784-97. doi: https://doi. org/10.1118/1.4934826
    • (2015) Med Phys , vol.42 , pp. 6784-6797
    • Fave, X.1    Mackin, D.2    Yang, J.3    Zhang, J.4    Fried, D.5    Balter, P.6
  • 13
    • 84949801178 scopus 로고    scopus 로고
    • Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer
    • Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol 2015; 8: 524-34. doi: https://doi.org/10.1016/j.tranon.2015.11.013
    • (2015) Transl Oncol , vol.8 , pp. 524-534
    • Oliver, J.A.1    Budzevich, M.2    Zhang, G.G.3    Dilling, T.J.4    Latifi, K.5    Moros, E.G.6
  • 14
    • 84884562832 scopus 로고    scopus 로고
    • Stability of FDG-PET radiomics features: An integrated analysis of test-retest and interobserver variability
    • Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET radiomics features: an integrated analysis of test-retest and interobserver variability. Acta Oncol 2013; 52: 1391-7. doi: https://doi.org/10.3109/ 0284186X.2013.812798
    • (2013) Acta Oncol , vol.52 , pp. 1391-1397
    • Leijenaar, R.T.1    Carvalho, S.2    Velazquez, E.R.3    Van Elmpt, W.J.4    Parmar, C.5    Hoekstra, O.S.6
  • 15
    • 84860776116 scopus 로고    scopus 로고
    • Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET
    • Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 2012; 53: 693-700. doi: https://doi.org/10.2967/jnumed.111.099127
    • (2012) J Nucl Med , vol.53 , pp. 693-700
    • Tixier, F.1    Hatt, M.2    Le Rest, C.C.3    Le Pogam, A.4    Corcos, L.5    Visvikis, D.6
  • 16
    • 77956565862 scopus 로고    scopus 로고
    • Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters
    • Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 2010; 49: 1012-6. doi: https://doi.org/ 10.3109/0284186x.2010.498437
    • (2010) Acta Oncol , vol.49 , pp. 1012-1016
    • Galavis, P.E.1    Hollensen, C.2    Jallow, N.3    Paliwal, B.4    Jeraj, R.5
  • 17
    • 84975698783 scopus 로고    scopus 로고
    • Repeatability of radiomic features in non-smallcell lung cancer [(18)F]FDG-PET/CT studies: Impact of reconstruction and delineation
    • van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-smallcell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol 2016; 18: 788-95. doi: https://doi.org/10.1007/s11307-016-0940-2
    • (2016) Mol Imaging Biol , vol.18 , pp. 788-795
    • Van Velden, F.H.1    Kramer, G.M.2    Frings, V.3    Nissen, I.A.4    Mulder, E.R.5    De Langen, A.J.6
  • 18
    • 84946570299 scopus 로고    scopus 로고
    • Impact of image reconstruction settings on texture features in 18F-FDG PET
    • Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med 2015; 56: 1667-73. doi: https://doi.org/10.2967/ jnumed.115.156927
    • (2015) J Nucl Med , vol.56 , pp. 1667-1673
    • Yan, J.1    Chu-Shern, J.L.2    Loi, H.Y.3    Khor, L.K.4    Sinha, A.K.5    Quek, S.T.6
  • 19
    • 84919326536 scopus 로고    scopus 로고
    • Comparison of texture features derived from static and respiratorygated PET images in non-small cell lung cancer
    • Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJ, Berbeco R. Comparison of texture features derived from static and respiratorygated PET images in non-small cell lung cancer. PLoS One 2014; 9: e115510. doi: https://doi.org/10.1371/journal. pone.0115510
    • (2014) PLoS One , vol.9 , pp. e115510
    • Yip, S.1    McCall, K.2    Aristophanous, M.3    Chen, A.B.4    Aerts, H.J.5    Berbeco, R.6
  • 20
    • 84994895346 scopus 로고    scopus 로고
    • The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG PET imaging of lung cancer
    • Grootjans W, Tixier F, van der Vos CS, Vriens D, Le Rest CC, Bussink J, et al. The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG PET imaging of lung cancer. J Nucl Med 2016; 57: 1692-8.
    • (2016) J Nucl Med , vol.57 , pp. 1692-1698
    • Grootjans, W.1    Tixier, F.2    Van Der Vos, C.S.3    Vriens, D.4    Le Rest, C.C.5    Bussink, J.6
  • 21
    • 84938961706 scopus 로고    scopus 로고
    • The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer
    • Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ. The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer. Eur Radiol 2015; 25: 2805-12. doi: https://doi.org/10.1007/s00330-015-3681-8
    • (2015) Eur Radiol , vol.25 , pp. 2805-2812
    • Doumou, G.1    Siddique, M.2    Tsoumpas, C.3    Goh, V.4    Cook, G.J.5
  • 22
    • 84938942773 scopus 로고    scopus 로고
    • The effect of SUV discretization in quantitative FDG-PET radiomics: The need for standardized methodology in tumor texture analysis
    • Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 2015; 5: 11075. doi: https://doi.org/ 10.1038/srep11075
    • (2015) Sci Rep , vol.5 , pp. 11075
    • Leijenaar, R.T.1    Nalbantov, G.2    Carvalho, S.3    Van Elmpt, W.J.4    Troost, E.G.5    Boellaard, R.6
  • 23
    • 84975260856 scopus 로고    scopus 로고
    • Robustness of radiomic features in [11C]choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: Impact of segmentation and discretization
    • Lu L, Lv W, Jiang J, Ma J, Feng Q, Rahmim A, et al. Robustness of radiomic features in [11C]choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization. Mol Imaging Biol 2016; 18: 935-45.
    • (2016) Mol Imaging Biol , vol.18 , pp. 935-945
    • Lu, L.1    Lv, W.2    Jiang, J.3    Ma, J.4    Feng, Q.5    Rahmim, A.6
  • 24
    • 84925535468 scopus 로고    scopus 로고
    • FDG PET/CT: EANM procedure guidelines for tumour imaging: Version 2.0
    • European Association of Nuclear Medicine (EANM)
    • Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al; European Association of Nuclear Medicine (EANM). FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015; 42: 328-54. doi: https://doi.org/10.1007/s00259-014-2961-x
    • (2015) Eur J Nucl Med Mol Imaging , vol.42 , pp. 328-354
    • Boellaard, R.1    Delgado-Bolton, R.2    Oyen, W.J.3    Giammarile, F.4    Tatsch, K.5    Eschner, W.6
  • 25
    • 84941420890 scopus 로고    scopus 로고
    • Haralick texture analysis of prostate MRI: Utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
    • Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 2015; 25: 2840-50. doi: https://doi. org/10.1007/s00330-015-3701-8
    • (2015) Eur Radiol , vol.25 , pp. 2840-2850
    • Wibmer, A.1    Hricak, H.2    Gondo, T.3    Matsumoto, K.4    Veeraraghavan, H.5    Fehr, D.6
  • 26
    • 84976866378 scopus 로고    scopus 로고
    • Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer
    • Gnep K, Fargeas A, Gutiérrez-Carvajal RE, Commandeur F, Mathieu R, Ospina JD, et al. Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging 2017; 45; 103-17. doi: https://doi. org/10.1002/jmri.25335.
    • (2017) J Magn Reson Imaging , vol.45 , pp. 103-117
    • Gnep, K.1    Fargeas, A.2    Gutiérrez-Carvajal, R.E.3    Commandeur, F.4    Mathieu, R.5    Ospina, J.D.6
  • 27
    • 84975299241 scopus 로고    scopus 로고
    • Spatial precision in magnetic resonance imaging-guided radiation therapy: The role of geometric distortion
    • Weygand J, Fuller CD, Ibbott GS, Mohamed ASR, Ding Y, Yang J, et al. Spatial precision in magnetic resonance imaging-guided radiation therapy: the role of geometric distortion. Int J Radiat Oncol Biol Phys 2016; 95: 1304-16. doi: https://doi.org/10.1016/j. ijrobp.2016.02.059
    • (2016) Int J Radiat Oncol Biol Phys , vol.95 , pp. 1304-1316
    • Weygand, J.1    Fuller, C.D.2    Ibbott, G.S.3    Mohamed, A.S.R.4    Ding, Y.5    Yang, J.6
  • 28
    • 84952787244 scopus 로고    scopus 로고
    • Application of wavelet techniques for cancer diagnosis using ultrasound images: A review
    • Sudarshan VK, Mookiah MR, Acharya UR, Chandran V, Molinari F, Fujita H, et al. Application of wavelet techniques for cancer diagnosis using ultrasound images: a review. Comput Biol Med 2016; 69: 97-111. doi: https://doi.org/10.1016/j. compbiomed.2015.12.006
    • (2016) Comput Biol Med , vol.69 , pp. 97-111
    • Sudarshan, V.K.1    Mookiah, M.R.2    Acharya, U.R.3    Chandran, V.4    Molinari, F.5    Fujita, H.6
  • 29
    • 84994868781 scopus 로고    scopus 로고
    • Automatic differential diagnosis of melanocytic skin tumors using ultrasound data
    • Andrekute K, Linkeviciute G, Raišutis R, Valiukeviciene S, Makštiene J. Automatic differential diagnosis of melanocytic skin tumors using ultrasound data. Ultrasound Med Biol 2016; 42: 2834-43. doi: https://doi. org/10.1016/j.ultrasmedbio.2016.07.026
    • (2016) Ultrasound Med Biol , vol.42 , pp. 2834-2843
    • Andrekute, K.1    Linkeviciute, G.2    Raišutis, R.3    Valiukeviciene, S.4    Makštiene, J.5
  • 30
    • 84992436564 scopus 로고    scopus 로고
    • Quantitative analysis of echogenicity for patients with thyroid nodules
    • Wu MH, Chen CN, Chen KY, Ho MC, Tai HC, Wang YH, et al. Quantitative analysis of echogenicity for patients with thyroid nodules. Sci Rep 2016; 6: 35632. doi: https://doi. org/10.1038/srep35632
    • (2016) Sci Rep , vol.6 , pp. 35632
    • Wu, M.H.1    Chen, C.N.2    Chen, K.Y.3    Ho, M.C.4    Tai, H.C.5    Wang, Y.H.6
  • 31
    • 84942753070 scopus 로고    scopus 로고
    • A model using texture features to differentiate the nature of thyroid nodules on sonography
    • Song G, Xue F, Zhang C. A model using texture features to differentiate the nature of thyroid nodules on sonography. J Ultrasound Med 2015; 34: 1753-60. doi: https://doi.org/ 10.7863/ultra.15.14.10045
    • (2015) J Ultrasound Med , vol.34 , pp. 1753-1760
    • Song, G.1    Xue, F.2    Zhang, C.3
  • 32
    • 84931266806 scopus 로고    scopus 로고
    • Computer-aided diagnosis for distinguishing between triplenegative breast cancer and fibroadenomas based on ultrasound texture features
    • Moon WK, Huang YS, Lo CM, Huang CS, Bae MS, Kim WH, et al. Computer-aided diagnosis for distinguishing between triplenegative breast cancer and fibroadenomas based on ultrasound texture features. Med Phys 2015; 42: 3024-35. doi: https://doi.org/ 10.1118/1.4921123
    • (2015) Med Phys , vol.42 , pp. 3024-3035
    • Moon, W.K.1    Huang, Y.S.2    Lo, C.M.3    Huang, C.S.4    Bae, M.S.5    Kim, W.H.6
  • 33
    • 84921754422 scopus 로고    scopus 로고
    • Classification of breast tumors using sonographic texture analysis
    • Ardakani AA, Gharbali A, Mohammadi A. Classification of breast tumors using sonographic texture analysis. J Ultrasound Med 2015; 34: 225-31. doi: https://doi.org/ 10.7863/ultra.34.2.225
    • (2015) J Ultrasound Med , vol.34 , pp. 225-231
    • Ardakani, A.A.1    Gharbali, A.2    Mohammadi, A.3
  • 34
    • 84982162870 scopus 로고    scopus 로고
    • Quantitative ultrasound imaging of Achilles tendon integrity in symptomatic and asymptomatic individuals: Reliability and minimal detectable change
    • Nadeau MJ, Desrochers A, Lamontagne M, Lariviere C, Gagnon DH. Quantitative ultrasound imaging of Achilles tendon integrity in symptomatic and asymptomatic individuals: reliability and minimal detectable change. J Foot Ankle Res 2016; 9:30. doi: https://doi. org/10.1186/s13047-016-0164-3
    • (2016) J Foot Ankle Res , vol.9 , pp. 30
    • Nadeau, M.J.1    Desrochers, A.2    Lamontagne, M.3    Lariviere, C.4    Gagnon, D.H.5
  • 35
    • 84872015239 scopus 로고    scopus 로고
    • Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?
    • Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med 2013; 54: 19-26. doi: https:// doi.org/10.2967/jnumed.112.107375
    • (2013) J Nucl Med , vol.54 , pp. 19-26
    • Cook, G.J.1    Yip, C.2    Siddique, M.3    Goh, V.4    Chicklore, S.5    Roy, A.6
  • 36
    • 84964335379 scopus 로고    scopus 로고
    • Radiomic phenotype features predict pathological response in non-small cell lung cancer
    • Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 2015; 119: 480-6. doi: https://doi.org/10.1016/j.radonc.2016.04.004
    • (2015) Radiother Oncol , vol.119 , pp. 480-486
    • Coroller, T.P.1    Agrawal, V.2    Narayan, V.3    Hou, Y.4    Grossmann, P.5    Lee, S.W.6
  • 37
    • 84927569956 scopus 로고    scopus 로고
    • CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
    • Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114: 345-50. doi: https:// doi.org/10.1016/j.radonc.2015.02.015
    • (2015) Radiother Oncol , vol.114 , pp. 345-350
    • Coroller, T.P.1    Grossmann, P.2    Hou, Y.3    Rios Velazquez, E.4    Leijenaar, R.T.5    Hermann, G.6
  • 38
    • 84975256387 scopus 로고    scopus 로고
    • Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III
    • Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, et al. Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging 2016; 43: 1477-85. doi: https://doi.org/10.1007/ s00259-016-3325-5
    • (2016) Eur J Nucl Med Mol Imaging , vol.43 , pp. 1477-1485
    • Desseroit, M.C.1    Visvikis, D.2    Tixier, F.3    Majdoub, M.4    Perdrisot, R.5    Guillevin, R.6
  • 39
    • 84999633654 scopus 로고    scopus 로고
    • Radiomics signature: A potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer
    • Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 2016; 281: 947-57.
    • (2016) Radiology , vol.281 , pp. 947-957
    • Huang, Y.1    Liu, Z.2    He, L.3    Chen, X.4    Pan, D.5    Ma, Z.6
  • 40
    • 84971500987 scopus 로고    scopus 로고
    • The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer
    • Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 2016; 7: 31401-12. doi: https:// doi.org/10.18632/oncotarget.8919
    • (2016) Oncotarget , vol.7 , pp. 31401-31412
    • Liang, C.1    Huang, Y.2    He, L.3    Chen, X.4    Ma, Z.5    Dong, D.6
  • 42
    • 79952309367 scopus 로고    scopus 로고
    • A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging
    • Group
    • Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC; Group. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology 2011; 258: 906-14. doi: https://doi.org/ 10.1148/radiol.10100799
    • (2011) Radiology , vol.258 , pp. 906-914
    • Buckler, A.J.1    Bresolin, L.2    Dunnick, N.R.3    Sullivan, D.C.4
  • 43
    • 84951335120 scopus 로고    scopus 로고
    • Variability of clinical target volume delineation for definitive radiotherapy in cervix cancer
    • Eminowicz G, McCormack M. Variability of clinical target volume delineation for definitive radiotherapy in cervix cancer. Radiother Oncol 2015; 117: 542-7. doi: https://doi. org/10.1016/j.radonc.2015.10.007
    • (2015) Radiother Oncol , vol.117 , pp. 542-547
    • Eminowicz, G.1    McCormack, M.2
  • 44
    • 85011709050 scopus 로고    scopus 로고
    • Core samples for radiomics features that are insensitive to tumor segmentation: Method and pilot study using CT images of hepatocellular carcinoma
    • Echegaray S, Gevaert O, Shah R, Kamaya A, Louie J, Kothary N, et al. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. J Med Imaging (Bellingham) 2015; 2: 041011. doi: https://doi.org/10.1117/1.JMI.2.4.041011
    • (2015) J Med Imaging (Bellingham) , vol.2 , pp. 041011
    • Echegaray, S.1    Gevaert, O.2    Shah, R.3    Kamaya, A.4    Louie, J.5    Kothary, N.6
  • 45
    • 84902515123 scopus 로고    scopus 로고
    • Reproducibility and prognosis of quantitative features extracted from CT images
    • Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S, et al. Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 2014; 7: 72-87. doi: https://doi.org/10.1593/ tlo.13844
    • (2014) Transl Oncol , vol.7 , pp. 72-87
    • Balagurunathan, Y.1    Gu, Y.2    Wang, H.3    Kumar, V.4    Grove, O.5    Hawkins, S.6
  • 46
    • 84904248018 scopus 로고    scopus 로고
    • Robust radiomics feature quantification using semiautomatic volumetric segmentation
    • Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014; 9: e102107. doi: https://doi. org/10.1371/journal.pone.0102107
    • (2014) PLoS One , vol.9 , pp. e102107
    • Parmar, C.1    Rios Velazquez, E.2    Leijenaar, R.3    Jermoumi, M.4    Carvalho, S.5    Mak, R.H.6
  • 48
    • 34249275726 scopus 로고    scopus 로고
    • PET-CT-based auto-contouring in nonsmall-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes
    • van Baardwijk A, Bosmans G, Boersma L, Buijsen J, Wanders S, Hochstenbag M, et al. PET-CT-based auto-contouring in nonsmall-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007; 68: 771-78. doi: https://doi. org/10.1016/j.ijrobp.2006.12.067
    • (2007) Int J Radiat Oncol Biol Phys , vol.68 , pp. 771-778
    • Van Baardwijk, A.1    Bosmans, G.2    Boersma, L.3    Buijsen, J.4    Wanders, S.5    Hochstenbag, M.6
  • 49
    • 0021583718 scopus 로고
    • FCM: The fuzzy c-means clustering algorithm
    • Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosci 1984; 10: 191-203.
    • (1984) Comput Geosci , vol.10 , pp. 191-203
    • Bezdek, J.C.1    Ehrlich, R.2    Full, W.3
  • 50
    • 34249749945 scopus 로고    scopus 로고
    • Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
    • Hatt M, Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, et al. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Phys Med Biol 2007; 52: 3467-91. doi: https://doi. org/10.1088/0031-9155/52/12/010
    • (2007) Phys Med Biol , vol.52 , pp. 3467-3491
    • Hatt, M.1    Lamare, F.2    Boussion, N.3    Turzo, A.4    Collet, C.5    Salzenstein, F.6
  • 51
    • 66249139913 scopus 로고    scopus 로고
    • A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET
    • Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging 2009; 28: 881-93. doi: https://doi.org/10.1109/ TMI.2008.2012036
    • (2009) IEEE Trans Med Imaging , vol.28 , pp. 881-893
    • Hatt, M.1    Cheze Le Rest, C.2    Turzo, A.3    Roux, C.4    Visvikis, D.5
  • 52
    • 77950689496 scopus 로고    scopus 로고
    • Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications
    • Hatt M, Cheze le Rest C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 2010; 77: 301-8. doi: https://doi.org/10.1016/j.ijrobp.2009.08.018
    • (2010) Int J Radiat Oncol Biol Phys , vol.77 , pp. 301-308
    • Hatt, M.1    Cheze Le Rest, C.2    Descourt, P.3    Dekker, A.4    De Ruysscher, D.5    Oellers, M.6
  • 53
    • 80455132144 scopus 로고    scopus 로고
    • Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation
    • Hatt M, Cheze-le Rest C, van Baardwijk A, Lambin P, Pradier O, Visvikis D. Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med 2011; 52: 1690-7. doi: https://doi.org/10.2967/ jnumed.111.092767
    • (2011) J Nucl Med , vol.52 , pp. 1690-1697
    • Hatt, M.1    Cheze-Le Rest, C.2    Van Baardwijk, A.3    Lambin, P.4    Pradier, O.5    Visvikis, D.6
  • 54
    • 84885428106 scopus 로고    scopus 로고
    • Robustness of intratumour 18FFDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma
    • Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. Robustness of intratumour 18FFDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging 2013; 40: 1662-71. doi: https://doi.org/ 10.1007/s00259-013-2486-8
    • (2013) Eur J Nucl Med Mol Imaging , vol.40 , pp. 1662-1671
    • Hatt, M.1    Tixier, F.2    Cheze Le Rest, C.3    Pradier, O.4    Visvikis, D.5
  • 56
    • 84863229324 scopus 로고    scopus 로고
    • The National Lung Screening Trial: Overview and study design
    • National Lung Screening Trial Research Team
    • National Lung Screening Trial Research Team; Aberle DR, Berg CD, Black WC, Church TR, Fagerstrom RM, Galen B, et al. The National Lung Screening Trial: overview and study design. Radiology 2011; 258: 243-53. doi: https://doi.org/10.1148/ radiol.10091808
    • (2011) Radiology , vol.258 , pp. 243-253
    • Aberle, D.R.1    Berg, C.D.2    Black, W.C.3    Church, T.R.4    Fagerstrom, R.M.5    Galen, B.6
  • 57
    • 85010817811 scopus 로고    scopus 로고
    • Predicting malignant nodules from screening CTscans
    • Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H, et al. Predicting malignant nodules from screening CTscans. J Thorac Oncol 2016; 11: 2120-8. doi: https:// doi.org/10.1016/j.jtho.2016.07.002
    • (2016) J Thorac Oncol , vol.11 , pp. 2120-2128
    • Hawkins, S.1    Wang, H.2    Liu, Y.3    Garcia, A.4    Stringfield, O.5    Krewer, H.6
  • 58
    • 84888074704 scopus 로고    scopus 로고
    • High-risk CT features for detection of local recurrence after stereotactic ablative radiotherapy for lung cancer
    • Huang K, Senthi S, Palma DA, Spoelstra FO, Warner A, Slotman BJ, et al. High-risk CT features for detection of local recurrence after stereotactic ablative radiotherapy for lung cancer. Radiother Oncol 2013; 109: 51-7. doi: https://doi.org/10.1016/j.radonc.2013.06.047
    • (2013) Radiother Oncol , vol.109 , pp. 51-57
    • Huang, K.1    Senthi, S.2    Palma, D.A.3    Spoelstra, F.O.4    Warner, A.5    Slotman, B.J.6
  • 59
    • 84990822036 scopus 로고    scopus 로고
    • Validation of high-risk computed tomography features for detection of local recurrence after stereotactic body radiation therapy for early-stage non-small cell lung cancer
    • Peulen H, Mantel F, Guckenberger M, Belderbos J, Werner-Wasik M, Hope A, et al. Validation of high-risk computed tomography features for detection of local recurrence after stereotactic body radiation therapy for early-stage non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2016; 96: 134-41. doi: https://doi.org/10.1016/j.ijrobp.2016.04.003
    • (2016) Int J Radiat Oncol Biol Phys , vol.96 , pp. 134-141
    • Peulen, H.1    Mantel, F.2    Guckenberger, M.3    Belderbos, J.4    Werner-Wasik, M.5    Hope, A.6
  • 60
    • 84925348152 scopus 로고    scopus 로고
    • Lung texture in serial thoracic computed tomography scans: Correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development
    • Cunliffe A, Armato SG 3rd, Castillo R, Pham N, Guerrero T, Al-Hallaq HA. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys 2015; 91: 1048-56. doi: https://doi. org/10.1016/j.ijrobp.2014.11.030
    • (2015) Int J Radiat Oncol Biol Phys , vol.91 , pp. 1048-1056
    • Cunliffe, A.1    Armato, S.G.2    Castillo, R.3    Pham, N.4    Guerrero, T.5    Al-Hallaq, H.A.6
  • 61
    • 84901946941 scopus 로고    scopus 로고
    • Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
    • Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006. doi: https://doi.org/ 10.1038/ncomms5006
    • (2014) Nat Commun , vol.5 , pp. 4006
    • Aerts, H.J.1    Velazquez, E.R.2    Leijenaar, R.T.3    Parmar, C.4    Grossmann, P.5    Carvalho, S.6
  • 62
    • 80955178859 scopus 로고    scopus 로고
    • Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies
    • van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 2011; 38: 1636-47. doi: https://doi.org/10.1007/s00259-011-1845-6
    • (2011) Eur J Nucl Med Mol Imaging , vol.38 , pp. 1636-1647
    • Van Velden, F.H.1    Cheebsumon, P.2    Yaqub, M.3    Smit, E.F.4    Hoekstra, O.S.5    Lammertsma, A.A.6
  • 63
    • 0015680481 scopus 로고
    • Textural features for image classification
    • Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cyb 1973; 3: 610-21. doi: https://doi.org/10.1109/ tsmc.1973.4309314
    • (1973) IEEE Trans Syst Man Cyb , vol.3 , pp. 610-621
    • Haralick, R.M.1    Shanmugam, K.2    Dinstein, I.3
  • 64
    • 0024738619 scopus 로고
    • Textural features corresponding to textural properties
    • Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cyb 1989; 19: 1264-74. doi: https://doi.org/10.1109/21.44046
    • (1989) IEEE Trans Syst Man Cyb , vol.19 , pp. 1264-1274
    • Amadasun, M.1    King, R.2
  • 65
    • 0020974699 scopus 로고
    • Neighboring gray level dependence matrix for texture classification
    • Sun C, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vision Graph 1982; 23: 341-52. doi: https://doi.org/10.1016/0734-189x(83) 90032-4
    • (1982) Comput Vision Graph , vol.23 , pp. 341-352
    • Sun, C.1    Wee, W.G.2
  • 66
    • 0001416258 scopus 로고
    • Texture analysis using gray level run lengths
    • Galloway MM. Texture analysis using gray level run lengths. Comput Vision Graph 1975; 4: 172-9. doi: https://doi.org/10.1016/s0146-664x(75)80008-6
    • (1975) Comput Vision Graph , vol.4 , pp. 172-179
    • Galloway, M.M.1
  • 67
    • 79952788113 scopus 로고    scopus 로고
    • Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer
    • Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 2011; 52: 369-78. doi: https://doi.org/ 10.2967/jnumed.110.082404
    • (2011) J Nucl Med , vol.52 , pp. 369-378
    • Tixier, F.1    Le Rest, C.C.2    Hatt, M.3    Albarghach, N.4    Pradier, O.5    Metges, J.P.6
  • 68
    • 84936085910 scopus 로고    scopus 로고
    • A radiomics model from joint FDGPET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities
    • Vallieres M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDGPET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015; 60: 5471-96. doi: https://doi.org/ 10.1088/0031-9155/60/14/5471
    • (2015) Phys Med Biol , vol.60 , pp. 5471-5496
    • Vallieres, M.1    Freeman, C.R.2    Skamene, S.R.3    El Naqa, I.4
  • 69
    • 84871704418 scopus 로고    scopus 로고
    • Non-small cell lung cancer: Histopathologic correlates for texture parameters at CT
    • Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 2013; 266: 326-36. doi: https://doi.org/10.1148/ radiol.12112428
    • (2013) Radiology , vol.266 , pp. 326-336
    • Ganeshan, B.1    Goh, V.2    Mandeville, H.C.3    Ng, Q.S.4    Hoskin, P.J.5    Miles, K.A.6
  • 70
    • 84871923501 scopus 로고    scopus 로고
    • Assessment of tumor heterogeneity by CT texture analysis: Can the largest crosssectional area be used as an alternative to whole tumor analysis?
    • Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest crosssectional area be used as an alternative to whole tumor analysis? Eur J Radiol 2013; 82: 342-8. doi: https://doi.org/10.1016/j. ejrad.2012.10.023
    • (2013) Eur J Radiol , vol.82 , pp. 342-348
    • Ng, F.1    Kozarski, R.2    Ganeshan, B.3    Goh, V.4
  • 71
    • 84939573489 scopus 로고    scopus 로고
    • Preliminary investigation into sources of uncertainty in quantitative imaging features
    • Fave X, Cook M, Frederick A, Zhang L, Yang J, Fried D, et al. Preliminary investigation into sources of uncertainty in quantitative imaging features. Comput Med Imaging Graph 2015; 44: 54-61. doi: https://doi.org/10.1016/j. compmedimag.2015.04.006
    • (2015) Comput Med Imaging Graph , vol.44 , pp. 54-61
    • Fave, X.1    Cook, M.2    Frederick, A.3    Zhang, L.4    Yang, J.5    Fried, D.6
  • 73
    • 84923913977 scopus 로고    scopus 로고
    • IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics
    • Zhang L, FriedDV, Fave XJ,Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 2015; 42: 1341-53. doi: https://doi.org/10.1118/ 1.4908210
    • (2015) Med Phys , vol.42 , pp. 1341-1353
    • Zhang, L.1    Fried, D.V.2    Fave, X.J.3    Hunter, L.A.4    Yang, J.5    Court, L.E.6
  • 74
    • 84876196444 scopus 로고    scopus 로고
    • Quantifying tumour heterogeneity with CT
    • Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging 2013; 13: 140-9. doi: https://doi.org/10.1102/1470-7330.2013.0015
    • (2013) Cancer Imaging , vol.13 , pp. 140-149
    • Ganeshan, B.1    Miles, K.A.2
  • 75
    • 84955604605 scopus 로고    scopus 로고
    • Radiomics: Images are more than pictures, they are data
    • Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563-77. doi: https://doi. org/10.1148/radiol.2015151169
    • (2016) Radiology , vol.278 , pp. 563-577
    • Gillies, R.J.1    Kinahan, P.E.2    Hricak, H.3
  • 76
    • 84897539994 scopus 로고    scopus 로고
    • Development and evaluation of an open-source software package "cGITA" for quantifying tumor heterogeneity with molecular images
    • Fang YH, Lin CY, Shih MJ, Wang HM, Ho TY, Liao CT, et al. Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images. Biomed Res Int 2014; 2014: 248505. doi: https://doi. org/10.1155/2014/248505
    • (2014) Biomed Res Int , vol.2014 , pp. 248505
    • Fang, Y.H.1    Lin, C.Y.2    Shih, M.J.3    Wang, H.M.4    Ho, T.Y.5    Liao, C.T.6
  • 78
    • 0037572347 scopus 로고    scopus 로고
    • CERR: A computational environment for radiotherapy research
    • Deasy JO, Blanco AI, Clark VH. CERR: a computational environment for radiotherapy research. Med Phys 2003; 30: 979-85. doi: https://doi.org/10.1118/1.1568978
    • (2003) Med Phys , vol.30 , pp. 979-985
    • Deasy, J.O.1    Blanco, A.I.2    Clark, V.H.3
  • 79
    • 0027002164 scopus 로고
    • The feature selection problem: Traditional methods and a new algorithm
    • Kira K, Rendell LA. The feature selection problem: traditional methods and a new algorithm. AAAI-92 proceedings; 1992. pp. 129-34.
    • (1992) AAAI-92 Proceedings , pp. 129-134
    • Kira, K.1    Rendell, L.A.2
  • 81
    • 84939498419 scopus 로고    scopus 로고
    • Machine learning methods for quantitative radiomic biomarkers
    • Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine learning methods for quantitative radiomic biomarkers. Sci Rep 2015; 5: 13087. doi: https://doi.org/10.1038/ srep13087
    • (2015) Sci Rep , vol.5 , pp. 13087
    • Parmar, C.1    Grossmann, P.2    Bussink, J.3    Lambin, P.4    Aerts, H.J.5
  • 82
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell 1997; 97: 273-324. doi: https//doi.org/10.1016/s0004-3702(97)00043-x
    • (1997) Artif Intell , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 84
    • 85011682314 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995; 57: 289-300.
    • (1995) J R Stat Soc Series B Stat Methodol , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 85
    • 84938850455 scopus 로고    scopus 로고
    • Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer
    • Parmar C, Leijenaar RT, Grossmann P, Rios Velazquez E, Bussink J, Rietveld D, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 2015; 5: 11044. doi: https:// doi.org/10.1038/srep11044
    • (2015) Sci Rep , vol.5 , pp. 11044
    • Parmar, C.1    Leijenaar, R.T.2    Grossmann, P.3    Rios Velazquez, E.4    Bussink, J.5    Rietveld, D.6
  • 86
    • 84895137957 scopus 로고    scopus 로고
    • Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors
    • Hoeben BA, Starmans MH, Leijenaar RT, Dubois LJ, van der Kogel AJ, Kaanders JH, et al. Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors. BMC Cancer 2014; 14: 130. doi: https://doi.org/10.1186/1471-2407-14-130
    • (2014) BMC Cancer , vol.14 , pp. 130
    • Hoeben, B.A.1    Starmans, M.H.2    Leijenaar, R.T.3    Dubois, L.J.4    Van Der Kogel, A.J.5    Kaanders, J.H.6
  • 87
    • 84920654725 scopus 로고    scopus 로고
    • The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer
    • Gao X, Chu C, Li Y, Lu P, Wang W, Liu W, et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur J Radiol 2015; 84: 312-7. doi: https://doi.org/10.1016/j. ejrad.2014.11.006
    • (2015) Eur J Radiol , vol.84 , pp. 312-317
    • Gao, X.1    Chu, C.2    Li, Y.3    Lu, P.4    Wang, W.5    Liu, W.6
  • 88
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the Lasso
    • Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol 1996; 58: 267-88.
    • (1996) J R Stat Soc Series B Stat Methodol , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 89
    • 84973651850 scopus 로고    scopus 로고
    • Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer
    • Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016; 34: 2157-64. doi: https://doi.org/10.1200/ JCO.2015.65.9128
    • (2016) J Clin Oncol , vol.34 , pp. 2157-2164
    • Huang, Y.Q.1    Liang, C.H.2    He, L.3    Tian, J.4    Liang, C.S.5    Chen, X.6
  • 91
    • 84954549862 scopus 로고    scopus 로고
    • Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer
    • Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 2015; 5: 272. doi: https:// doi.org/10.3389/fonc.2015.00272
    • (2015) Front Oncol , vol.5 , pp. 272
    • Parmar, C.1    Grossmann, P.2    Rietveld, D.3    Rietbergen, M.M.4    Lambin, P.5    Aerts, H.J.6
  • 92
    • 84924036082 scopus 로고    scopus 로고
    • Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
    • Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med 2015; 13:1. doi: https://doi.org/ 10.1186/s12916-014-0241-z
    • (2015) BMC Med , vol.13 , pp. 1
    • Collins, G.S.1    Reitsma, J.B.2    Altman, D.G.3    Moons, K.G.4
  • 93
    • 85009861401 scopus 로고    scopus 로고
    • Introduction to big data in radiation oncology: Exploring opportunities for research, quality assessment, and clinical care
    • Benedict SH, El Naqa I, Klein EE. Introduction to big data in radiation oncology: exploring opportunities for research, quality assessment, and clinical care. Int J Radiat Oncol Biol Phys 2016; 95: 871-2. doi: https:// doi.org/10.1016/j.ijrobp.2015.12.358
    • (2016) Int J Radiat Oncol Biol Phys , vol.95 , pp. 871-872
    • Benedict, S.H.1    El Naqa, I.2    Klein, E.E.3
  • 94
    • 84944174994 scopus 로고    scopus 로고
    • Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells
    • Panth KM, Leijenaar RT, Carvalho S, Lieuwes NG, Yaromina A, Dubois L, et al. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol 2015; 116: 462-6. doi: https://doi. org/10.1016/j.radonc.2015.06.013
    • (2015) Radiother Oncol , vol.116 , pp. 462-466
    • Panth, K.M.1    Leijenaar, R.T.2    Carvalho, S.3    Lieuwes, N.G.4    Yaromina, A.5    Dubois, L.6
  • 95
    • 34250195010 scopus 로고    scopus 로고
    • Decoding global gene expression programs in liver cancer by noninvasive imaging
    • Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007; 25: 675-80. doi: https://doi.org/10.1038/nbt1306
    • (2007) Nat Biotechnol , vol.25 , pp. 675-680
    • Segal, E.1    Sirlin, C.B.2    Ooi, C.3    Adler, A.S.4    Gollub, J.5    Chen, X.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.