참고문헌 (30)

1. 1. Aerts HJ , Velazquez ER , Leijenaar RT , Parmar C , Grossmann P , Carvalho S , Bussink J , Monshouwer R , Haibe-Kains B , Rietveld D , Hoebers F , Rietbergen MM , Leemans CR , Dekker A , Quackenbush J , Gillies RJ , Lambin P Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach . Nat Commun . 2014 ; 5 : 4006 . 24892406 

   상세보기

2. 2. Gillies RJ , Kinahan PE , Hricak H Radiomics: images are more than pictures, they are data . Radiology . 2015 ; 278 : 563 – 577 . 26579733 

3. 3. Gevaert O , Xu J , Hoang CD , Leung AN , Xu Y , Quon A , Rubin DL , Napel S , Plevritis SK Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results . Radiology . 2012 ; 264 : 387 – 396 . 22723499 

   상세보기

4. 4. Parmar C , Leijenaar RT , Grossmann P , Rios Velazquez E , Bussink J , Rietveld D , Rietbergen MM , Haibe-Kains B , Lambin P , Aerts HJ Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer . Sci Rep . 2015 ; 5 : 11044 . 26251068 

   상세보기

5. 5. Liu Y , Kim J , Balagurunathan Y , Li Q , Garcia AL , Stringfield O , Ye Z , Gillies RJ Radiomic features are associated with EGFR mutation status in lung adenocarcinomas . Clin Lung Cancer . 2016 ; 17 : 441 – 448.e6 . 27017476 

6. 6. Zhou M , Scott J , Chaudhury B , Hall L , Goldgof D , Yeom KW , Iv M , Ou Y , Kalpathy-Cramer J , Napel S , Gillies R , Gevaert O , Gatenby R Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches . AJNR Am J Neuroradiol . 2018 ; 39 : 208 – 216 . 28982791 

7. 7. Valdora F , Houssami N , Rossi F , Calabrese M , Tagliafico AS Rapid review: radiomics and breast cancer . Breast Cancer Res Treat . 2018 ; 169 : 217 – 229 . 29396665 

   상세보기

8. 8. West DL , Kotrotsou A , Niekamp AS , Idris T , Giniebra Camejo D , Mazal NJ , Cardenas NJ , Goldberg JL , Colen RR CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information [abstract] . JCO . 2017 ; 35.15_suppl.e15623 . 

9. 9. Jeong WK , Jamshidi N , Felker ER , Raman SS , Lu DS Radiomics and radiogenomics of primary liver cancers . Clin Mol Hepatol . 2018 [Epub ahead of print] . 

10. 10. Starmans MP , Miclea RL , van der Voort SR , Niessen WJ , Thomeer MG , Klein S , editors. Classification of malignant and benign liver tumors using a radiomics approach . Proc. SPIE . 2018 ; 105741D . 

11. 11. Yu H , Scalera J , Khalid M , Touret A-S , Bloch N , Li B , Qureshi MM , Soto JA , Anderson SW Texture analysis as a radiomic marker for differentiating renal tumors . Abdom Radiol (NY) . 2017 ; 42 : 2470 – 2478 . 28421244 

    상세보기

12. 12. van Rossum PS , Xu C , Fried DV , Goense L , Lin SH The emerging field of radiomics in esophageal cancer: current evidence and future potential . Transl Cancer Res . 2016 ; 5 : 410 – 23 . 30687593 

13. 13. Cook GJ , Yip C , Siddique M , Goh V , Chicklore S , Roy A , Marsden P , Ahmad S , Landau D Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? qj J Nucl Med . 2013 ; 54 : 19 – 26 . 23204495 

    상세보기

14. 14. Aerts HJ , Grossmann P , Tan Y , Oxnard GR , Rizvi N , Schwartz LH , Zhao B Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC . Sci Rep . 2016 ; 6 : 33860 . 27645803 

    상세보기

15. 15. Ganeshan B , Panayiotou E , Burnand K , Dizdarevic S , Miles K Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival . Eur Radiol . 2012 ; 22 : 796 – 802 . 22086561 

    상세보기

16. 16. Kumar V , Gu Y , Basu S , Berglund A , Eschrich SA , Schabath MB , Forster K , Aerts HJ , Dekker A , Fenstermacher D , Goldgof DB , Hall LO , Lambin P , Balagurunathan Y , Gatenby RA , Gillies RJ Radiomics: the process and the challenges . Magn Reson Imaging . 2012 ; 30 : 1234 – 1248 . 22898692 

17. 17. Zhao B , Tan Y , Tsai WY , Qi J , Xie C , Lu L , Schwartz LH Reproducibility of radiomics for deciphering tumor phenotype with imaging . Sci Rep . 2016 ; 6 : 23428 . 27009765 

    상세보기

18. 18. Lu L , Ehmke RC , Schwartz LH , Zhao B Assessing agreement between radiomic features computed for multiple CT imaging settings . PloS One . 2016 ; 11 : e0166550 . 28033372 

    상세보기

19. 19. Mackin D , Fave X , Zhang L , Fried D , Yang J , Taylor B , Rodriguez-Rivera E , Dodge C , Jones AK , Court L Measuring CT scanner variability of radiomics features . Invest Radiol . 2015 ; 50 : 757 – 765 . 26115366 

20. 20. Berenguer R , Pastor-Juan MdR , Canales-Vázquez J , Castro-García M , Villas MV , Legorburo FM , Sabater S Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters . Radiology . 2018 ; 288 ( 2 ): 407 – 415 . 29688159 

    상세보기

21. 21. Li Q , Gavrielides MA , Sahiner B , Myers KJ , Zeng R , Petrick N Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study . Med Phys . 2015 ; 42 : 3932 – 3947 . 26133594 

    상세보기

22. 22. Gavrielides MA , Berman BP , Supanich M , Schultz K , Li Q , Petrick N , Zeng R , Siegelman J Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study . Quant Imaging Med Surg . 2017 ; 7 : 623 – 635 . 29312867 

23. 23. McCollough CH , Bruesewitz MR , McNitt-Gray MF , Bush K , Ruckdeschel T , Payne JT , Brink JA , Zeman RK ; American College of Radiology . The phantom portion of the American College of Radiology (ACR) computed tomography (CT) accreditation program: practical tips, artifact examples, and pitfalls to avoid . Med Phys . 2004 ; 31 : 2423 – 2442 . 15487722 

    상세보기

24. 24. Haralick RM , Shanmugam K Textural features for image classification . IEEE Trans Syst Man Cybern . 1973 : 610 – 621 . 

25. 25. Dercle L , Lu L , Lichtenstein P , Yang H , Wang D , Zhu J , Impact of variability in portal venous phase acquisition timing in tumor density measurement and treatment response assessment: metastatic colorectal cancer as a paradigm . JCO Clin Cancer Inform . 2017 ; 1 : 1 – 8 . 

26. 26. Van Cutsem E , Köhne CH , Hitre E , Zaluski J , Chang Chien CR , Makhson A , D'Haens G , Pintér T , Lim R , Bodoky G , Roh JK , Folprecht G , Ruff P , Stroh C , Tejpar S , Schlichting M , Nippgen J , Rougier P Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer . 2009 ; 360 : 1408 – 1417 . 

27. 27. Choi H , Charnsangavej C , Faria SC , Macapinlac HA , Burgess MA , Patel SR , Chen LL , Podoloff DA , Benjamin RS Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria . J Clin Oncol . 2007 ; 25 : 1753 – 1759 . 17470865 

28. 28. National Lung Screening Trial Research Team , Church TR , Black WC , Aberle DR , Berg CD , Clingan KL , Duan F , Fagerstrom RM , Gareen IF , Gierada DS , Jones GC , Mahon I , Marcus PM , Sicks JD , Jain A , Baum S Reduced lung-cancer mortality with low-dose computed tomographic screening . N Engl J Med . 2011 ; 365 : 395 – 409 . 21714641 

    상세보기

29. 29. Huynh E , Coroller TP , Narayan V , Agrawal V , Hou Y , Romano J , Franco I , Mak RH , Aerts HJ CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer . Radiother Oncol . 2016 ; 120 : 258 – 266 . 27296412 

    상세보기

30. 30. Filippou V , Tsoumpas C Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound . Med Phys . 2018 ; 45 : e740 – e60 . 

    상세보기