Radiomics: the facts and the challenges of image analysis
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Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
Lambin P, Leijenaar RTH, Deist T et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
Rizzo S, Petrella F, Buscarino V et al (2016) CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol 26:32–42
Larue RTHM, van Timmeren JE, de Jong EEC et al (2017) Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol 56:1544–1553
Ergen B, Baykara M (2014) Texture based feature extraction methods for content based medical image retrieval systems. Biomed Mater Eng 24:3055–3062.
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621
Balagurunathan Y, Kumar V, Gu Y et al (2014) Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27:805–823
Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179
Ollers M, Bosmans G, van Baardwijk A et al (2008) The integration of PET–CT scans from different hospitals into radiotherapy treatment planning. Radiother Oncol 87:142–146
Peeken JC, Bernhofer M, Wiestler B et al (2018) Radiomics in radiooncology—challenging the medical physicist. Phys Med 48:27–36
Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7:46349
Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087
Rizzo S, Botta F, Raimondi S et al (2018) Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol.
Huynh E, Coroller TP, Narayan V et al (2017) Associations of radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT. PLoS One 12:e0169172
Jolliffe IT (2002) Principal component analysis, Series: Springer Series in Statistics, 2nd edn. Springer, New York, p 487
Hochberg Y, Benjamini Y (1990) More powerful procedures for multiple significance testing. Stat Med 9:811–818
Eschrich S, Yang I, Bloom G et al (2005) Molecular staging for survival prediction of colorectal cancer patients. J Clin Oncol 23:3526–3535
Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol. 58:267–288
Shedden K, Taylor JM, Enkemann SA et al (2008) Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 14:822–827.
Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387
Lee H, Palm J, Grimes SM, Ji HP (2015) The cancer genome atlas clinical explorer: a web and mobile interface for identifying clinical-genomic driver associations. Genome Med 7:112
Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045e57
Panth KM, Leijenaar RT, Carvalho S et al (2015) 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 116:462–466
McCollough C, Bakalyar DM, Bostani M et al (2014) Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT: the report of AAPM task group 220. AAPM Rep 2014:6–23
Dalal T, Kalra MK, Rizzo SM et al (2005) Metallic prosthesis: technique to avoid increase in CT radiation dose with automatic tube current modulation in a phantom and patients. Radiology 236:671–675
Rizzo SM, Kalra MK, Schmidt B et al (2005) CT images of abdomen and pelvis: effect of nonlinear three-dimensional optimized reconstruction algorithm on image quality and lesion characteristics. Radiology 237:309–315
Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Invest Radiol 50:757–765
Theodorakou C, Horrocks JA, Marshall NW, Speller RD (2004) A novel method for producing x-ray test objects and phantoms. Phys Med Biol 49:1423–1438
van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2016) Test-retest data for radiomic feature stability analysis: generalizable or study-specific? Tomography 2:361–365
Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E (2016) Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology 279:185–194
Reuzé S, Schernberg A, Orlhac F et al (2018) Radiomics in nuclear medicine applied to radiation therapy: methods, pitfalls and challenges. Int J Radiat Oncol Biol Phys. https://doi.org/10.1016/j.ijrobp.2018.05.022
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D (2017) Characterization of PET/CT images using texture analysis: the past, the present…any future? Eur J Nucl Med Mol Imaging 44:151–165
Shiri I, Rahmin A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A (2017) The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 27:4498–4509
Altazi BA, Zhang GG, Fernandez DC et al (2017) Reproducibility of F18-FDG PET radiomic features for different cervical tumour segmentation methods, gray-level discretization, and reconstruction algorithm. J Appl Clin Med Phys 18:32–48
Reuzè S, Orlhac F, Chargari C et al (2017) Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget 8:43169–43179
Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE (2015) Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imaging (Bellingham) 2:041002. https://doi.org/10.1117/1.JMI.2.4.041002
Forgacs A, Pall Jonsson H, Dahlbom M et al (2016) A study on the basic criteria for selecting heterogeneity parameters of F18-FDG PET images. PLoS One 11:e0164113
Boellaard R (2009) Standards for PET image acquisition and quantitative data analysis. J Nucl Med 50:11S–20S
Madabhushi A, Udupa JK (2006) New methods of MR image intensity standardization via generalized scale. Med Phys 33:3426–3434
Mayerhoefer M, Szomolanyi P, Jirak D, Materka A, Trattnig S (2009) Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. Med Phys 36:1236–1243
Lerski RA, Schad LR, Luypaert R et al (1999) Multicentre magnetic resonance texture analysis trial using reticulated foam test objects. Magn Reson Imaging 17:1025–1031
Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248
Hojjatoleslami S, Kittler J (1998) Region growing: a new approach. IEEE Trans Image Process 7:1079–1084
Kalef-Ezra J, Karantanas A, Tsekeris P (1999) CT measurement of lung density. Acta Radiol 40:333–337
Sofka M, Wetzl J, Birkbeck N et al (2011) Multi-stage learning for robust lung segmentation in challenging CT volumes. Med Image Comput Comput Assist Interv 14:667–674
Knollmann FD, Kumthekar R, Fetzer D, Socinski MA (2014) Assessing response to treatment in non-small-cell lung cancer: role of tumor volume evaluated by computed tomography. Clin Lung Cancer 15:103–109
Gao H, Chae O (2010) Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognit 43:2406–2417
Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J (2012) Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21:2035–2046.
Ye X, Beddoe G, Slabaugh G (2010) Automatic graph cut segmentation of lesions in CT using mean shift superpixels. Int J Biomed Imaging 2010:983963. https://doi.org/10.1155/2010/983963
Suzuki K, Kohlbrenner R, Epstein ML, Obajuluwa AM, Xu J, Hori M (2010) Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Med Phys 37:2159
Lu K, Higgins WE (2007) Interactive segmentation based on the live wire for 3D CT chest image analysis. Int J Comput Assist Radiol Surg 2:151–167
Tan Y, Schwartz LH, Zhao B (2013) Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 40:043502
Sun S, Bauer C, Beichel R (2012) Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach. IEEE Trans Med Imaging 31:449–460