Abstract
Purpose
Material and Methods
Results
Conclusion
Graphical abstract
Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Physica Medica: European Journal of Medical PhysicsReferences
- Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer. 2012; 48: 441-446https://doi.org/10.1016/j.ejca.2011.11.036
- Applications and limitations of radiomics.Phys Med Biol. 2016; 61: R150-R166https://doi.org/10.1088/0031-9155/61/13/R150
- The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.Sci Rep. 2015; 5: 11075https://doi.org/10.1038/srep11075
- Image biomarker standardisation initiative.Radiology. 2020;
Goodman SN, Fanelli D and Ioannidis JPA, What does research reproducibility mean? Science Translational Medicine, Vol. 8, Issue 341, pp. 341ps12 01 Jun 2016.
- Robust radiomics feature quantification using semiautomatic volumetric segmentation.PLoS ONE. 2014; 9e102107https://doi.org/10.1371/journal.pone.0102107
P. Hu J. Wang H. Zhong Z. Zhou Shen L,Hu W, et al. Reproducibility with repeat CT in radiomics study for rectal cancer Oncotarget 7 71440 2016 pp. 10.18632/oncotarget.12199.
- Reproducibility of radiomics for deciphering tumor phenotype with imaging.Sci Rep. 2016; 6: 23428https://doi.org/10.1038/srep23428
- Repeatability and reproducibility of radiomic features: a systematic review.Int J Radiation Oncol Biol Phys. 2018; 102: 1143-1158
C. Parmar R.T. Leijenaar P. Grossmann E.R. Velazquez J. Bussink D. Rietveld et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer Sci Rep 2015;5:srep11044.
Drzymala, R.E.; Mohan, R.; Brewster, L.; Chu, J.; Goitein, M.; Harms, W. et al. (May 1991). “Dose-volume histograms”. International Journal of Radiation Oncology*Biology*Physics. 21 (1): 71–78. doi:10.1016/0360-3016(91)90168-4. PMID 2032898.
L. Rossi R. Nijman W. Schillemans S. Aluwini C. Cavedon M. Witte et al. Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy Radiation and Oncology 129 3 December 2018Volume 548 553.
- Radio-morphology: Parametric shape-based features in radiotherapy.Med Phys. 2019 Feb; 46 (Epub 2018 Dec 24): 704-713https://doi.org/10.1002/mp.13323
- Dosiomics: extracting 3D spatial features from dose distribution to predict incidence of radiation pneumonitis.Front Oncol. 2019; 12: 269https://doi.org/10.3389/fonc.2019.00269. eCollection 2019
Gabrys HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and selection of Machine Learning methods using radiomics and dosiomics for normal tissue complication probability modelling of Xerostomia, Front Oncol, 2018 Mr 5;8:35. Doi 10.3389/fonc.2018.00035. eCollection 2018.
M. Avanzo, J. Stancanello, I. El Naqa, Beyond imaging: the promise of radiomics, Physica Medica June 2017, Volume 38, Pages 122-139.
- Towards a modular decision support system for radiomics: A case study on rectal cancer.Artif Intell Med. 2018; https://doi.org/10.1016/j.artmed.2018.09.003
- Method agreement analysis: a review of correct methodology.Theriogenology. 2010; 73: 1167-1179
- A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.J Chiropract Med. 2016; 15https://doi.org/10.1016/j.jcm.2016.02.012
- Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM.J Strength Cond Res. 2005; 19: 231-240
- Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.PLoS ONE. 2016; 11e0164924
- Biomedical texture analysis, chapter 3.1st edition,. Academic Press, London, UK2017: 63-101
- Radiomics: the bridge between medical imaging and personalized medicine.Nat Rev Clin Oncol. 2017; 14: 749-762
- Decoding tumour: phenotype by noninvasive imaging using a quantitative radiomics approach.Nat Commu. 2014; 5: 4006
- Predicting malignant nodules from screening CT scans.J Thorac Oncol. 2016; 11: 2120-2128
- Radiomic phenotype features predict phatological response in non-small cell lung cancer.Radiother Oncolo. 2016; 119: 480-486
- Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.Sci Rep. 2017; 7: 588
- Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer.Sci Rep. 2015; 5: 11044
- Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features.Tomography. 2016; 2: 430-437
D. Mackin X. Fave L. Zhang D. Fried J. Yang B. Taylor et al. Measuring Computed Tomography Scanner Variability of Radiomics Features Invest Radiol 50 11 2015 Nov pp. 757–765. pmid:26115366.
Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, et al. Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability. PLoS One 2016 Oct 14;11(10):e0164924. pmid:27741289.
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 Mar 24;6:23428. pmid:27009765.
Du Q, Baine M, Bavitz K, McAllister J, Liang X, Yu H et al, Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction, PLOS, Published: May 7, 2019 https://doi.org/10.1371/journal.pone.0216480.
- Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing.Phys Med. 2019 May; 61 (Epub 2019 Apr 28): 44-51https://doi.org/10.1016/j.ejmp.2019.04.009
- Radiomic phenotype features predict pathological response in non-small cell lung cancer.Radiother Oncol. 2016; 119: 480-486
F.H.P. van Velden, G.M. Kramer, V.Frings, et al.Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: Impact of reconstruction and delineation. Mol Imaging Biol, 18 (2016), pp. 788-795.
M. Bogowicz, O. Riesterer, R.A.Bundschuh, et al.Stability of radiomic features in CT perfusion maps. Phys Med Biol, 61 (2016), pp. 8736-8749.
Boldrini L., Cusumano D., Chiloiro G., Casà C, Maschiocchi C, Lenkowicz J et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Med. 2019 Feb;124(2):145-153. doi: 10.1007/s11547-018-0951-y. Epub 2018 Oct 29.
