[en] While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Ibrahim, Abdalla
Refaee, Turkey
Primakov, Sergey
Barufaldi, Bruno
Acciavatti, Raymond J.
Granzier, Renée W. Y.
Hustinx, Roland ; Université de Liège - ULiège > Département des sciences cliniques > Médecine nucléaire
Mottaghy, Felix M.
Woodruff, Henry C.
Wildberger, Joachim E.
Lambin, Philippe
Maidment, Andrew D. A.
Language :
English
Title :
The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization.
Publication date :
2021
Journal title :
Cancers
eISSN :
2072-6694
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Switzerland
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Bibliography
Walsh, S.; De Jong, E.E.; Van Timmeren, J.E.; Ibrahim, A.; Compter, I.; Peerlings, J.; Sanduleanu, S.; Refaee, T.; Keek, S.; LaRue, R.T.; et al. Decision Support Systems in Oncology. JCO Clin. Cancer Inform. 2019, 3, 1–9. [CrossRef] [PubMed]
Lambin, P.; Leijenaar, R.T.; Deist, T.M.; Peerlings, J.; De Jong, E.E.; Van Timmeren, J.; Sanduleanu, S.; LaRue, R.T.; Even, A.J.; Jochems, A.; et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749–762. [CrossRef] [PubMed]
Ibrahim, A.; Vallières, M.; Woodruff, H.; Primakov, S.; Beheshti, M.; Keek, S.; Refaee, T.; Sanduleanu, S.; Walsh, S.; Morin, O.; et al. Radiomics analysis for clinical decision support in nuclear medicine. Semin. Nucl. Med. 2019, 49, 438–449. [CrossRef] [PubMed]
Kumar, V.; Gu, Y.; Basu, S.; Berglund, A.; Eschrich, S.A.; Schabath, M.B.; Forster, K.; Aerts, H.J.; Dekker, A.; Fenstermacher, D.; et al. Radiomics: The process and the challenges. Magn. Reson. Imaging 2012, 30, 1234–1248. [CrossRef] [PubMed]
Miller, A.S.; Blott, B.H.; Hames, T.K. Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 1992, 30, 449–464. [CrossRef] [PubMed]
Kjaer, L.; Ring, P.; Thomsen, C.; Henriksen, O. Texture analysis in quantitative MR imaging. Tissue characterisation of normal brain and intracranial tumours at 1.5 T. Acta Radiol. 1995, 36, 127–135.
Lambin, P.; Rios-Velazquez, E.; Leijenaar, R.; Carvalho, S.; van Stiphout, R.G.; Granton, P.; Zegers, C.M.; Gillies, R.; Boellard, R.; Dekker, A.; et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 2012, 48, 441–446. [CrossRef] [PubMed]
Gillies, R.J.; Kinahan, P.E.; Hricak, H. Radiomics: Images are more than pictures, they are data. Radiology 2016, 278, 563–577. [CrossRef] [PubMed]
Refaee, T.; Wu, G.; Ibrahim, A.; Halilaj, I.; Leijenaar, R.T.; Rogers, W.; Gietema, H.A.; Hendriks, L.E.; Lambin, P.; Woodruff, H.C. The emerging role of radiomics in COPD and lung cancer. Respiration 2020, 99, 99–107. [CrossRef]
Rogers, W.; Seetha, S.T.; Refaee, T.A.G.; Lieverse, R.I.Y.; Granzier, R.W.Y.; Ibrahim, A.; Keek, S.A.; Sanduleanu, S.; Primakov, S.P.; Beuque, M.P.L.; et al. Radiomics: From qualitative to quantitative imaging. Br. J. Radiol. 2020, 93, 20190948. [CrossRef]
Berenguer, R.; Pastor-Juan, M.D.R.; Canales-Vázquez, J.; Castro-García, M.; Villas, M.V.; Legorburo, F.M.; Sabater, S. Radiomics of CT features may be nonreproducible and redundant: Influence of CT acquisition parameters. Radiology 2018, 288, 407–415. [CrossRef]
Strimbu, K.; Tavel, J.A. What are biomarkers? Curr. Opin. HIV AIDS 2010, 5, 463. [CrossRef]
Davis, A.T.; Palmer, A.L.; Pani, S.; Nisbet, A. Assessment of the variation in CT scanner performance (image quality and Hounsfield units) with scan parameters, for image optimisation in radiotherapy treatment planning. Phys. Med. 2018, 45, 59–64. [CrossRef]
Ibrahim, A.; Primakov, S.; Beuque, M.; Woodruff, H.; Halilaj, I.; Wu, G.; Refaee, T.; Granzier, R.; Widaatalla, Y.; Hustinx, R.; et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 2021, 188, 20–29. [CrossRef]
Van Timmeren, J.E.; Leijenaar, R.T.; van Elmpt, W.; Wang, J.; Zhang, Z.; Dekker, A.; Lambin, P. Test-retest data for radiomics feature stability analysis: Generalizable or study-specific? Tomography 2016, 2, 361–365. [CrossRef] [PubMed]
Peerlings, J.; Woodruff, H.C.; Winfield, J.M.; Ibrahim, A.; Van Beers, B.E.; Heerschap, A.; Jackson, A.; Wildberger, J.E.; Mottaghy, F.M.; DeSouza, N.M.; et al. Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Sci. Rep. 2019, 9, 4800. [CrossRef] [PubMed]
Zhovannik, I.; Bussink, J.; Traverso, A.; Shi, Z.; Kalendralis, P.; Wee, L.; Dekker, A.; Fijten, R.; Monshouwer, R. Learning from scanners: Bias reduction and feature correction in radiomics. Clin. Transl. Radiat. Oncol. 2019, 19, 33–38. [CrossRef] [PubMed]
Traverso, A.; Wee, L.; Dekker, A.; Gillies, R. Repeatability and reproducibility of radiomic features: A systematic review. Int. J. Radiat. Oncol. 2018, 102, 1143–1158. [CrossRef] [PubMed]
Papanikolaou, N.; Matos, C.; Koh, D.M. How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging 2020, 20, 1–10. [CrossRef]
Shafiq-Ul-Hassan, M.; Latifi, K.; Zhang, G.; Ullah, G.; Gillies, R.; Moros, E. Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci. Rep. 2018, 8, 10545. [CrossRef] [PubMed]
Shafiq-Ul-Hassan, M.; Zhang, G.G.; Latifi, K.; Ullah, G.; Hunt, D.C.; Balagurunathan, Y.; Abdalah, M.A.; Schabath, M.B.; Goldgof, D.G.; Mackin, D.; et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med. Phys. 2017, 44, 1050–1062. [CrossRef] [PubMed]
Thévenaz, P.; Blu, T.; Unser, M. Image interpolation and resampling of medical imaging, processing and analysis. In Handbook of Medical Imaging, Processing and Analysis; Academic Press: Cambridge, MA, USA, 2000.
Haddad, M.; Porenta, G. Impact of reorientation algorithms on quantitative myocardial SPECT perfusion imaging. J. Nucl. Med. 1998, 39, 1864–1869.
Menon, S.; Damian, A.; Hu, S.; Ravi, N.; Rudin, C. PULSE: Self-supervised photo upsampling via latent space exploration of generative models. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 16–18 June 2020; pp. 2437–2445. Available online: openaccess.thecvf.com (accessed on 15 March 2021).
Johnson, W.E.; Li, C.; Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2006, 8, 118–127. [CrossRef] [PubMed]
Orlhac, F.; Frouin, F.; Nioche, C.; Ayache, N.; Buvat, I. Validation of a method to compensate multicenter effects affecting CT radiomic features. Radiology 2019, 291, 53–59. [CrossRef]
Orlhac, F.; Boughdad, S.; Philippe, C.; Stalla-Bourdillon, H.; Nioche, C.; Champion, L.; Soussan, M.; Frouin, F.; Frouin, V.; Buvat, I. A postreconstruction harmonization method for multicenter radiomic studies in PET. J. Nucl. Med. 2018, 59, 1321–1328. [CrossRef]
Mackin, D.; Fave, X.; Zhang, L.; Fried, D.; Yang, J.; Taylor, B.; Rodriguez-Rivera, E.; Dodge, C.; Jones, A.K.; Court, L. Credence cartridge radiomics phantom CT Scans. The Cancer Imaging Archive (TCIA) 2017. Available online: https://wiki. cancerimagingarchive.net/display/Public/Credence+Cartridge+Radiomics+Phantom+CT+Scans (accessed on 5 March 2021).
Clark, K.; Vendt, B.; Smith, K.; Freymann, J.; Kirby, J.; Koppel, P.; Moore, S.; Phillips, S.; Maffitt, D.; Pringle, M.; et al. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 2013, 26, 1045–1057. [CrossRef]
Van Griethuysen, J.J.; Fedorov, A.; Parmar, C.; Hosny, A.; Aucoin, N.; Narayan, V.; Beets-Tan, R.G.; Fillion-Robin, J.-C.; Pieper, S.; Aerts, H.J. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017, 77, e104–e107. [CrossRef]
Lowekamp, B.C.; Chen, D.T.; Eibanez, L.; Eblezek, D. The design of SimpleITK. Front. Aging Neurosci. 2013, 7, 45. [CrossRef]
Hou, H.; Andrews, H.C. Cubic splines for image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Process. 1978, 26, 508–517. [CrossRef]
Meijering, E.H.; Niessen, W.J.; Viergever, M.A. Quantitative evaluation of convolution-based methods for medical image interpolation. Med. Image Anal. 2001, 5, 111–126. [CrossRef]
Stevenson, M.; Stevenson, M.M.; BiasedUrn, I. Package “epiR.”. Available online: https://vps.fmvz.usp.br/CRAN/web/packages/epiR/epiR.pdf (accessed on 10 March 2021).
R Core Team. R Language Definition; R Foundation for Statistical Computing: Vienna, Austria, 2000.
Gandrud, C. Reproducible Research with R and R Studio; Productivity Press: Boca Raton, FL, USA, 2013.
Lin, L.I.-K. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 1989, 45, 255. [CrossRef]
McBride, G.B. A Proposal for Strength-of-Agreement Criteria for Lin’s Concordance Correlation Coefficient; National Institute of Water & Atmospheric Research Ltd.: Hamilton, New Zealand, 2005.
Zar, J.H. Spearman Rank Correlation. In Encyclopedia of Biostatistics; Wiley: Hoboken, NJ, USA, 2005.
LaRue, R.T.H.M.; Van Timmeren, J.E.; De Jong, E.E.C.; Feliciani, G.; Leijenaar, R.T.H.; Schreurs, W.M.J.; Sosef, M.N.; Raat, F.H.P.J.; Van Der Zande, F.H.R.; Das, M.; et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: A comprehensive phantom study. Acta Oncol. 2017, 56, 1544–1553. [CrossRef]
Ligero, M.; Jordi-Ollero, O.; Bernatowicz, K.; Garcia-Ruiz, A.; Delgado-Muñoz, E.; Leiva, D.; Mast, R.; Suarez, C.; Sala-Llonch, R.; Calvo, N.; et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur. Radiol. 2021, 31, 1460–1470. [CrossRef] [PubMed]
Da-Ano, R.; Masson, I.; Lucia, F.; Doré, M.; Robin, P.; Alfieri, J.; Rousseau, C.; Mervoyer, A.; Reinhold, C.; Castelli, J.; et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci. Rep. 2020, 10, 10248. [CrossRef] [PubMed]
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