Reproducibility of radiomics for deciphering tumor phenotype with imaging

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Zhao, Binsheng ^a   Tan, Yongqiang ^a   Tsai, Wei Yann ^a   Qi, Jing ^a   Xie, Chuanmiao ^a   Lu, Lin ^a   Schwartz, Lawrence H ^a  

^a New York Presbyterian Morgan Stanley Children's Hospital   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; HUMAN; LUNG TUMOR; PATHOLOGY; PHENOTYPE; PROCEDURES; REPRODUCIBILITY; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; HUMANS; LUNG NEOPLASMS; PHENOTYPE; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84962314053     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep23428     Document Type: Article

References (29)

1. Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48, 441-446 (2012).
2. Kumar, V. et al. Radiomics: the process and the challenges. Magn Reson Imaging 30, 1234-1248 (2012).
3. Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5, 4006 (2014).
4. Colen, R. et al. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Transl Oncol 7, 556-569 (2014).
5. Kuo, M. D. et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol 18, 821-831 (2007).
6. Zinn, P. O. et al. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. Plos One 6, e25451 (2011).
7. Gevaert, O. et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Radiology 264, 387-396 (2012).
8. Lee, H. J. et al. Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. Radiology 268, 254-264 (2013).
9. Halpenny, D. F. et al. Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements? Lung Cancer 86, 190-194 (2014).
10. Coroller, T. P. et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114, 345-350 (2015).
11. Tixier, F. et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52, 369-378 (2011).
12. Karlo, C. A. et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270, 464-471 (2014).
13. Leijenaar, R. T. et al. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52, 1391-1397 (2013).
14. Balagurunathan, Y. et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27, 805-823 (2014).
15. Galavis, P. E. et al. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49, 1012-1016 (2010).
16. Zhao, B. et al. Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study. Transl Oncol 7, 88-93 (2014).
17. Zhao, B. et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 252, 263-272 (2009).
18. Zhao, B. et al. RIDER Lung CT: MSKCC. Data collection report. (2012) Available at: https://wiki. cancerimagingarchive. net/display/ Public/RIDER+ Collections. (Date of access: 18/10/2012).
19. Oxnard, G. R. et al. Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes. J Clin Oncol 29, 3114-3119 (2011).
20. Hunter, A. et al. High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images. Med Phys. 40, 121916 (2013).
21. McNitt-Gray, M. F. et al. Determining the Variability of Lesion Size Measurements from CT Patient Data Sets Acquired under "No Change" Conditions. Transl Oncol 8, 55-64 (2015).
22. Tan, Y., Schwartz, L. H. & Zhao, B. Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 40, 043502 (2013).
23. Lin, L. I. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255-268 (1989).
24. R. Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www. R-project. org/.
25. Dixon, W. J. Processing data for outliers. Biometrics 9, 74-89 (1953).
26. Tan, Y. et al. Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours. Cancer Imaging 12, 497-505 (2012).
27. Zhao, B. et al. Exploring intra-and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals. Eur J Radiol 82, 959-968 (2013).
28. Ng, F. et al. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82, 342-348 (2012).
29. Parmar, C. et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. Plos One 9, e102107 (2014).