-
1
-
-
84857037061
-
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(4):441-6. doi:10.1016/j.ejca.2011.11.036.
-
(2012)
Eur J Cancer
, vol.48
, Issue.4
, pp. 441-446
-
-
Lambin, P.1
Rios-Velazquez, E.2
Leijenaar, R.3
Carvalho, S.4
van Stiphout, R.G.5
Granton, P.6
-
2
-
-
84901946941
-
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: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
-
3
-
-
84875211731
-
Cancer heterogeneity: implications for targeted therapeutics
-
Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer (2013) 108(3):479-85. doi:10.1038/bjc.2012.581.
-
(2013)
Br J Cancer
, vol.108
, Issue.3
, pp. 479-485
-
-
Fisher, R.1
Pusztai, L.2
Swanton, C.3
-
4
-
-
84867082692
-
Breast cancer intratumor genetic heterogeneity: causes and implications
-
Ng CK, Pemberton HN, Reis-Filho JS. Breast cancer intratumor genetic heterogeneity: causes and implications. Expert Rev Anticancer Ther (2012) 12(8):1021-32. doi:10.1586/era.12.85.
-
(2012)
Expert Rev Anticancer Ther
, vol.12
, Issue.8
, pp. 1021-1032
-
-
Ng, C.K.1
Pemberton, H.N.2
Reis-Filho, J.S.3
-
5
-
-
84857397985
-
Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response
-
Szerlip NJ, Pedraza A, Chakravarty D, Azim M, McGuire J, Fang Y, et al. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc Natl Acad Sci U S A (2012) 109(8):3041-6. doi:10.1073/pnas.1114033109.
-
(2012)
Proc Natl Acad Sci U S A
, vol.109
, Issue.8
, pp. 3041-3046
-
-
Szerlip, N.J.1
Pedraza, A.2
Chakravarty, D.3
Azim, M.4
McGuire, J.5
Fang, Y.6
-
6
-
-
84857820489
-
Tumor heterogeneity and personalized medicine
-
Longo DL. Tumor heterogeneity and personalized medicine. N Engl J Med (2012) 366(10):956-7. doi:10.1056/NEJMe1200656.
-
(2012)
N Engl J Med
, vol.366
, Issue.10
, pp. 956-957
-
-
Longo, D.L.1
-
7
-
-
84908191509
-
Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review
-
Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One (2014) 9(10):e110300. doi:10.1371/journal.pone.0110300.
-
(2014)
PLoS One
, vol.9
, Issue.10
-
-
Alic, L.1
Niessen, W.J.2
Veenland, J.F.3
-
8
-
-
84884579594
-
Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer
-
Carvalho S, Leijenaar RT, Velazquez ER, Oberije C, Parmar C, Van Elmpt W, et al. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol (2013) 52(7):1398-404. doi:10.3109/0284186X.2013.812795.
-
(2013)
Acta Oncol
, vol.52
, Issue.7
, pp. 1398-1404
-
-
Carvalho, S.1
Leijenaar, R.T.2
Velazquez, E.R.3
Oberije, C.4
Parmar, C.5
Van Elmpt, W.6
-
9
-
-
84908702403
-
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features
-
Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology (2014) 273(1):168-74. doi:10.1148/radiol.14131731.
-
(2014)
Radiology
, vol.273
, Issue.1
, pp. 168-174
-
-
Gevaert, O.1
Mitchell, L.A.2
Achrol, A.S.3
Xu, J.4
Echegaray, S.5
Steinberg, G.K.6
-
10
-
-
84885428106
-
Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma
-
Hatt M, Tixier F, Le Rest CC, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging (2013) 40(11):1662-71. doi:10.1007/s00259-013-2486-8.
-
(2013)
Eur J Nucl Med Mol Imaging
, vol.40
, Issue.11
, pp. 1662-1671
-
-
Hatt, M.1
Tixier, F.2
Le Rest, C.C.3
Pradier, O.4
Visvikis, D.5
-
11
-
-
84905046756
-
Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor
-
Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology (2014) 272(2):484-93. doi:10.1148/radiol.14131691.
-
(2014)
Radiology
, vol.272
, Issue.2
, pp. 484-493
-
-
Jain, R.1
Poisson, L.M.2
Gutman, D.3
Scarpace, L.4
Hwang, S.N.5
Holder, C.A.6
-
12
-
-
84884562832
-
Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer 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 inter-observer variability. Acta Oncol (2013) 52(7):1391-7. doi:10.3109/0284186X.2013.812798.
-
(2013)
Acta Oncol
, vol.52
, Issue.7
, 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
-
13
-
-
84904248018
-
Robust radiomics feature quantification using semiautomatic volumetric segmentation
-
Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One (2014) 9(7):e102107. doi:10.1371/journal.pone.0102107.
-
(2014)
PLoS One
, vol.9
, Issue.7
-
-
Parmar, C.1
Velazquez, E.R.2
Leijenaar, R.3
Jermoumi, M.4
Carvalho, S.5
Mak, R.H.6
-
14
-
-
84890056425
-
High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images
-
Hunter LA, Krafft S, Stingo F, Choi H, Martel MK, Kry SF. High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images. Med Phys (2013) 40(12):121916. doi:10.1118/1.4829514.
-
(2013)
Med Phys
, vol.40
, Issue.12
-
-
Hunter, L.A.1
Krafft, S.2
Stingo, F.3
Choi, H.4
Martel, M.K.5
Kry, S.F.6
-
15
-
-
83755172956
-
Developing a classifier model for lung tumors in CT-scan images. Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference On
-
(Anchorage, AK)
-
Basu S, Hall LO, Goldgof DB, Gu Y, Kumar V, Choi J, et al., editors. Developing a classifier model for lung tumors in CT-scan images. Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference On. IEEE (2011) (Anchorage, AK). p. 1306-12.
-
(2011)
IEEE
, pp. 1306-1312
-
-
Basu, S.1
Hall, L.O.2
Goldgof, D.B.3
Gu, Y.4
Kumar, V.5
Choi, J.6
-
16
-
-
84938850455
-
Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer
-
Parmar C, Leijenaar RT, Grossmann P, Velazquez ER, 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:10.1038/srep11044.
-
(2015)
Sci Rep
, vol.5
, pp. 11044
-
-
Parmar, C.1
Leijenaar, R.T.2
Grossmann, P.3
Velazquez, E.R.4
Bussink, J.5
Rietveld, D.6
-
17
-
-
84871704418
-
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(1):326-36. doi:10.1148/radiol.12112428.
-
(2013)
Radiology
, vol.266
, Issue.1
, 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
-
18
-
-
84886566321
-
Prediction of 2 years-survival in patients with stage I and II non-small cell lung cancer utilizing 18F-FDG PET/CT SUV quantifica
-
Cistaro A, Quartuccio N, Mojtahedi A, Fania P, Filosso PL, Campenni A, et al. Prediction of 2 years-survival in patients with stage I and II non-small cell lung cancer utilizing 18F-FDG PET/CT SUV quantifica. Radiol Oncol (2013) 47(3):219-23. doi:10.2478/raon-2013-0023.
-
(2013)
Radiol Oncol
, vol.47
, Issue.3
, pp. 219-223
-
-
Cistaro, A.1
Quartuccio, N.2
Mojtahedi, A.3
Fania, P.4
Filosso, P.L.5
Campenni, A.6
-
19
-
-
84872015239
-
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(1):19-26. doi:10.2967/jnumed.112.107375.
-
(2013)
J Nucl Med
, vol.54
, Issue.1
, pp. 19-26
-
-
Cook, G.J.1
Yip, C.2
Siddique, M.3
Goh, V.4
Chicklore, S.5
Roy, A.6
-
20
-
-
84923319145
-
Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
-
Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S, et al. Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features. (IEEE) (2014). p. 1418-26.
-
(2014)
(IEEE)
, pp. 1418-1426
-
-
Hawkins, S.H.1
Korecki, J.N.2
Balagurunathan, Y.3
Gu, Y.4
Kumar, V.5
Basu, S.6
-
21
-
-
77957909316
-
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage
-
Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging (2010) 10(1):137. doi:10.1102/1470-7330.2010.0021.
-
(2010)
Cancer Imaging
, vol.10
, Issue.1
, pp. 137
-
-
Ganeshan, B.1
Abaleke, S.2
Young, R.C.3
Chatwin, C.R.4
Miles, K.A.5
-
22
-
-
84927569956
-
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
-
Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol (2015) 114(3):345-50. doi:10.1016/j.radonc.2015.02.015.
-
(2015)
Radiother Oncol
, vol.114
, Issue.3
, pp. 345-350
-
-
Coroller, T.P.1
Grossmann, P.2
Hou, Y.3
Velazquez, E.R.4
Leijenaar, R.T.5
Hermann, G.6
-
23
-
-
69949186449
-
Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy
-
Pickles MD, Manton DJ, Lowry M, Turnbull LW. Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy. Eur J Radiol (2009) 71(3):498-505. doi:10.1016/j.ejrad.2008.05.007.
-
(2009)
Eur J Radiol
, vol.71
, Issue.3
, pp. 498-505
-
-
Pickles, M.D.1
Manton, D.J.2
Lowry, M.3
Turnbull, L.W.4
-
24
-
-
34250195010
-
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(6):675-80. doi:10.1038/nbt1306.
-
(2007)
Nat Biotechnol
, vol.25
, Issue.6
, pp. 675-680
-
-
Segal, E.1
Sirlin, C.B.2
Ooi, C.3
Adler, A.S.4
Gollub, J.5
Chen, X.6
-
25
-
-
84937640437
-
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients
-
Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol (2015) 42:212-21. doi:10.1016/j.neurad.2014.02.006.
-
(2015)
J Neuroradiol
, vol.42
, pp. 212-221
-
-
Nicolasjilwan, M.1
Hu, Y.2
Yan, C.3
Meerzaman, D.4
Holder, C.A.5
Gutman, D.6
-
26
-
-
84871609385
-
Predicting outcomes in radiation oncology-multifactorial decision support systems
-
Lambin P, van Stiphout RG, Starmans MH, Rios-Velazquez E, Nalbantov G, Aerts HJ, et al. Predicting outcomes in radiation oncology-multifactorial decision support systems. Nat Rev Clin Oncol (2013) 10(1):27-40. doi:10.1038/nrclinonc.2012.196.
-
(2013)
Nat Rev Clin Oncol
, vol.10
, Issue.1
, pp. 27-40
-
-
Lambin, P.1
van Stiphout, R.G.2
Starmans, M.H.3
Rios-Velazquez, E.4
Nalbantov, G.5
Aerts, H.J.6
-
27
-
-
84867139157
-
Radiomics: the process and the challenges
-
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging (2012) 30(9):1234-48. doi:10.1016/j.mri.2012.06.010.
-
(2012)
Magn Reson Imaging
, vol.30
, Issue.9
, pp. 1234-1248
-
-
Kumar, V.1
Gu, Y.2
Basu, S.3
Berglund, A.4
Eschrich, S.A.5
Schabath, M.B.6
-
29
-
-
33745561205
-
An introduction to variable and feature selection
-
Guyon I, André E. An introduction to variable and feature selection. J Mach Learn Res (2003) 3:1157-82.
-
(2003)
J Mach Learn Res
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
André, E.2
-
30
-
-
84939498419
-
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: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
-
31
-
-
84863403768
-
Conditional likelihood maximisation: a unifying framework for information theoretic feature selection
-
Brown G, Pocock A, Zhao M-J, Luján M. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res (2012) 13(1):27-66.
-
(2012)
J Mach Learn Res
, vol.13
, Issue.1
, pp. 27-66
-
-
Brown, G.1
Pocock, A.2
Zhao, M.-J.3
Luján, M.4
-
32
-
-
79957625130
-
-
Tempe, AZ: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
-
Zhao Z, Morstatter F, Sharma S, Alelyani S, Anand A, Liu H. Advancing Feature Selection Research. ASU Feature Selection Repository. Tempe, AZ: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University (2010).
-
(2010)
Advancing Feature Selection Research. ASU Feature Selection Repository
-
-
Zhao, Z.1
Morstatter, F.2
Sharma, S.3
Alelyani, S.4
Anand, A.5
Liu, H.6
-
33
-
-
84919773193
-
Do we need hundreds of classifiers to solve real world classification problems?
-
Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res (2014) 15(1):3133-81.
-
(2014)
J Mach Learn Res
, vol.15
, Issue.1
, pp. 3133-3181
-
-
Fernández-Delgado, M.1
Cernadas, E.2
Barro, S.3
Amorim, D.4
-
35
-
-
0001416258
-
Texture analysis using gray level run lengths
-
Galloway MM. Texture analysis using gray level run lengths. Comput Graphics Image Process (1975) 4(2):172-9. doi:10.1016/S0146-664X(75)80008-6.
-
(1975)
Comput Graphics Image Process
, vol.4
, Issue.2
, pp. 172-179
-
-
Galloway, M.M.1
-
36
-
-
56249113343
-
Building predictive models in R using the caret package
-
Kuhn M. Building predictive models in R using the caret package. J Stat Softw (2008) 28(5):1-26. doi:10.18637/jss.v028.i05.
-
(2008)
J Stat Softw
, vol.28
, Issue.5
, pp. 1-26
-
-
Kuhn, M.1
-
37
-
-
83755163963
-
The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
-
Haury A-C, Gestraud P, Vert J-P. The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PLoS One (2011) 6(12):e28210. doi:10.1371/journal.pone.0028210.
-
(2011)
PLoS One
, vol.6
, Issue.12
-
-
Haury, A.-C.1
Gestraud, P.2
Vert, J.-P.3
-
38
-
-
84908222160
-
A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making
-
Oberije C, Nalbantov G, Dekker A, Boersma L, Borger J, Reymen B, et al. A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol (2014) 112(1):37-43. doi:10.1016/j.radonc.2014.04.012.
-
(2014)
Radiother Oncol
, vol.112
, Issue.1
, pp. 37-43
-
-
Oberije, C.1
Nalbantov, G.2
Dekker, A.3
Boersma, L.4
Borger, J.5
Reymen, B.6
-
39
-
-
84943774122
-
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(11):757-65. doi:10.1097/RLI.0000000000000180.
-
(2015)
Invest Radiol
, vol.50
, Issue.11
, pp. 757-765
-
-
Mackin, D.1
Fave, X.2
Zhang, L.3
Fried, D.4
Yang, J.5
Taylor, B.6
-
40
-
-
84902489272
-
Exploring variability in CT characterization of tumors: a preliminary phantom study
-
Zhao B, Tan Y, Tsai WY, Schwartz LH, Lu L. Exploring variability in CT characterization of tumors: a preliminary phantom study. Transl Oncol (2014) 7(1):88-93. doi:10.1593/tlo.13865.
-
(2014)
Transl Oncol
, vol.7
, Issue.1
, pp. 88-93
-
-
Zhao, B.1
Tan, Y.2
Tsai, W.Y.3
Schwartz, L.H.4
Lu, L.5
-
41
-
-
84904264664
-
Volumetric CT-based segmentation of NSCLC using 3D-Slicer
-
Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep (2013) 3:3529. doi:10.1038/srep03529.
-
(2013)
Sci Rep
, vol.3
, pp. 3529
-
-
Velazquez, E.R.1
Parmar, C.2
Jermoumi, M.3
Mak, R.H.4
van Baardwijk, A.5
Fennessy, F.M.6
-
42
-
-
77956565862
-
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(7):1012-6. doi:10.3109/0284186X.2010.498437.
-
(2010)
Acta Oncol
, vol.49
, Issue.7
, pp. 1012-1016
-
-
Galavis, P.E.1
Hollensen, C.2
Jallow, N.3
Paliwal, B.4
Jeraj, R.5
-
43
-
-
84938942773
-
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: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
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