Radiomics: the facts and the challenges of image analysis

Stefania Rizzo1, Francesca Botta2, Sara Raimondi3, Daniela Origgi2, Cristiana Fanciullo4, Alessio Giuseppe Morganti5, Massimo Bellomi6
1Department of Radiology, IEO, European Institute of Oncology, IRCCS, Milan, IT, Italy
2Medical Physics, European Institute of Oncology, Milan, Italy
3Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy
4Università degli Studi di Milano, Postgraduate School in Radiodiagnostics, Milan, Italy
5Radiation Oncology Center, School of Medicine, Department of Experimental, Diagnostic and Specialty Medicine – DIMES, University of Bologna, Bologna, Italy
6Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy

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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

Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10:257–273

Peeken JC, Bernhofer M, Wiestler B et al (2018) Radiomics in radiooncology—challenging the medical physicist. Phys Med 48:27–36

Giger ML (2018) Machine learning in medical imaging. J Am Coll Radiol 15:512–520

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

Wilkinson L, Friendly M (2009) The history of the cluster heat map. Am Stat 63:179–184

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

Breiman L (2001) Random forests. Mach Learn 45:5–32

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

Velazquez ER, Parmar C, Jermoumi M et al (2013) Volumetric CT-based segmentation of NSCLC using 3D-slicer. Sci Rep 3:3529