#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer


Autoři: Concetta Piazzese aff001;  Kieran Foley aff002;  Philip Whybra aff001;  Chris Hurt aff003;  Tom Crosby aff002;  Emiliano Spezi aff001
Působiště autorů: School of Engineering, Cardiff University, Cardiff, United Kingdom aff001;  Velindre Cancer Centre, Cardiff, United Kingdom aff002;  Centre for Trials Research, Cardiff, United Kingdom aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225550

Souhrn

The aim of this work was to investigate radiomic analysis of contrast and non-contrast enhanced planning CT images of oesophageal cancer (OC) patients in terms of stability, dimensionality and contrast agent dependency. The prognostic significance of CT-based radiomic features was also evaluated. Different 2D and 3D radiomic features were extracted from contrast and non-contrast enhanced CT images of 213 patients from the multi-centre SCOPE1 randomised controlled trial (RCT) in OC. Feature stability was evaluated by randomly dividing patients into three groups and identifying textures with similar distributions among groups with a Kruskal-Wallis analysis. A paired two-sided Wilcoxon signed rank test was used to assess for significant differences in the remaining corresponding 2D and 3D stable features. A prognostic model was constructed using clinical characteristics and remaining filtered features. The discriminative ability of significant variables was tested using Kaplan-Meier analysis. A total of 238 2D and 3D radiomic features were computed from oesophageal CT images. More than 75 features were stable if extracted from homogeneous cohort (contrast or non-contrast enhanced CT images) and inhomogeneous cohort (contrast and non-contrast enhanced CT images). Among the remaining corresponding stable features computed from both cohorts, only 4 features did not show a statistically significant difference if obtained in 2D or in 3D (p-value < 0.05). A Cox regression model constructed using 5 clinical variables (age, sex, tumour, node and metastasis (TNM) stage, WHO performance status and contrast administration) and 4 radiomic variables (inverse varianceGLCM, large distance emphasisGLDZM, zone distance non uniformity normGLDZM, zone distance varianceGLDZM), identified one radiomic feature (zone distance varianceGLDZM) that was significantly associated with overall survival (p-value = 0.032, HR = 1.25, 95% CI = 1.02–1.52). A significant difference in overall survival between groups was found when considering a threshold of zone distance varianceGLDZM equals to 1.70 (X2 = 7.692, df = 1, p-value = 0.006). Zone distance varianceGLDZM was identified as the only stable CT radiomic feature statistically correlated with overall survival, independent of dimensionality and contrast administration. This feature was able to identify high-risk patients and if validated, could be the subject of a future clinical trial aiming to improve clinical decision making and personalise OC treatment.

Klíčová slova:

Cancer treatment – Biomarkers – Computed axial tomography – Decision making – Prognosis – Randomized controlled trials – Esophageal cancer – Clinical trials (cancer treatment)


Zdroje

1. Pennathur A, Gibson M, Jobe B, Luketich J. Oesophageal carcinoma. The Lancet. 2013 Feb 2;381(9864):400–12.

2. Allum WH, Blazeby JM, Griffin SM, Cunningham D, Jankowski JA, Wong R. Guidelines for the management of oesophageal and gastric cancer. Gut. 2011 Nov 1;60(11):1449–72. doi: 10.1136/gut.2010.228254 21705456

3. van Hagen P, Hulshof MC, Van Lanschot JJ, Steyerberg EW, Henegouwen MV, Wijnhoven BP et al. Preoperative chemoradiotherapy for esophageal or junctional cancer. New England Journal of Medicine. 2012 May 31;366(22):2074–84. doi: 10.1056/NEJMoa1112088 22646630

4. Rice TW, Ishwaran H, Ferguson MK, Blackstone EH, Goldstraw P. Cancer of the Esophagus and Esophagogastric Junction: An Eighth Edition Staging Primer. Journal of Thoracic Oncology. 2017 Jan 1;12(1):36–42. doi: 10.1016/j.jtho.2016.10.016 27810391

5. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015 Nov 18;278(2):563–77. doi: 10.1148/radiol.2015151169 26579733

6. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P et al. Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer. 2012 Mar 1;48(4):441–6. doi: 10.1016/j.ejca.2011.11.036 22257792

7. van Rossum PS, Xu C, Fried DV, Goense L, Lin SH. The emerging field of radiomics in esophageal cancer: current evidence and future potential. Translational cancer research. 2016 Aug;5(4):410. doi: 10.21037/tcr.2016.06.19 30687593

8. Foley KG, Hills RK, Berthon B, Marshall C, Parkinson C, Lewis WG et al. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European radiology. 2018 Jan 1;28(1):428–36. doi: 10.1007/s00330-017-4973-y 28770406

9. Lambin P, Zindler J, Vanneste BG, Van De Voorde L, Eekers D, Compter I et al. Decision support systems for personalized and participative radiation oncology. Advanced drug delivery reviews. 2017 Jan 15;109:131–53. doi: 10.1016/j.addr.2016.01.006 26774327

10. Pavic M, Bogowicz M, Würms X, Glatz S, Finazzi T, Riesterer O et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncologica. 2018 Aug 3;57(8):1070–4. doi: 10.1080/0284186X.2018.1445283 29513054

11. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Scientific reports. 2016 Mar 24;6:23428. doi: 10.1038/srep23428 27009765

12. van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ et al. Repeatability of radiomic features in non-small-cell lung cancer [18F] FDG-PET/CT studies: impact of reconstruction and delineation. Molecular imaging and biology. 2016 Oct 1;18(5):788–95. doi: 10.1007/s11307-016-0940-2 26920355

13. Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of radiomic features: A systematic review. International Journal of Radiation Oncology* Biology* Physics. 2018 Nov 15;102(4):1143–58.

14. Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clinical radiology. 2012 Feb 1;67(2):157–64. doi: 10.1016/j.crad.2011.08.012 21943720

15. Hou Z, Ren W, Li S, Liu J, Sun Y, Yan et al. Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma. Oncotarget. 2017 Nov 28;8(61):104444. doi: 10.18632/oncotarget.22304 29262652

16. Nakajo M, Jinguji M, Nakabeppu Y, Nakajo M, Higashi R, Fukukura Y et al. Texture analysis of 18 F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy. European journal of nuclear medicine and molecular imaging. 2017 Feb 1;44(2):206–14. doi: 10.1007/s00259-016-3506-2 27613542

17. Singh J, Daftary A. Iodinated contrast media and their adverse reactions. Journal of nuclear medicine technology. 2008 Jun 1;36(2):69–74. doi: 10.2967/jnmt.107.047621 18483141

18. Hurt CN, Nixon LS, Griffiths GO, Al-Mokhtar R, Gollins S, Staffurth JN et al. SCOPE1: a randomised phase II/III multicentre clinical trial of definitive chemoradiation, with or without cetuximab, in carcinoma of the oesophagus. BMC cancer. 2011 Dec;11(1):466.

19. Crosby T, Hurt CN, Falk S, Gollins S, Staffurth J, Ray et al. Long-term results and recurrence patterns from SCOPE-1: a phase II/III randomized trial of definitive chemoradiotherapy +/- cetuximab in oesophageal cancer. Br J Cancer. 2017 Mar 14;116(6):709–716. doi: 10.1038/bjc.2017.21 28196063

20. Deasy JO, Blanco AI, Clark VH. CERR: a computational environment for radiotherapy research. Medical physics. 2003 May 1;30(5):979–85. doi: 10.1118/1.1568978 12773007

21. Gwynne S, Spezi E, Wills L, Nixon L, Hurt C, Joseph G et al. Toward semi-automated assessment of target volume delineation in radiotherapy trials: the SCOPE 1 pretrial test case. International Journal of Radiation Oncology* Biology* Physics. 2012 Nov 15;84(4):1037–42.

22. Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative-feature definitions. arXiv preprint arXiv:1612.07003. 2016 Dec 21.

23. Mantel N. Why stepdown procedures in variable selection. Technometrics. 1970 Aug 1;12(3):621–5.

24. Badic B, Desseroit MC, Hatt M, Visvikis D. Potential Complementary Value of Noncontrast and Contrast Enhanced CT Radiomics in Colorectal Cancers. Academic radiology. 2019 Apr 1;26(4):469–79. doi: 10.1016/j.acra.2018.06.004 30072293

25. Shen C, Liu Z, Guan M, Song J, Lian Y, Wang S et al. 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Translational oncology. 2017 Dec 1;10(6):886–94. doi: 10.1016/j.tranon.2017.08.007 28930698

26. Zhao B, Tan Y, Tsai WY, Schwartz LH, Lu L. Exploring variability in CT characterization of tumors: a preliminary phantom study. Translational oncology. 2014 Feb 1;7(1):88–93. doi: 10.1593/tlo.13865 24772211

27. Larue RT, van Timmeren JE, de Jong EE, Feliciani G, Leijenaar RT, Schreurs WM 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 oncologica. 2017 Nov 2;56(11):1544–53. doi: 10.1080/0284186X.2017.1351624 28885084

28. Mackin D, Fave X, Zhang L, Yang J, Jones AK, Ng CS et al. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PloS one. 2017 Sep 21;12(9):e0178524. doi: 10.1371/journal.pone.0178524 28934225

29. Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?. European journal of radiology. 2013 Feb 1;82(2):342–8. doi: 10.1016/j.ejrad.2012.10.023 23194641

30. Shafiq‐ul‐Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics. 2017 Mar 1;44(3):1050–62. doi: 10.1002/mp.12123 28112418

31. Zhao B, Schwartz LH, Moskowitz CS, Wang L, Ginsberg MS, Cooper CA et al. Pulmonary metastases: effect of CT section thickness on measurement—initial experience. Radiology. 2005 Mar;234(3):934–9. doi: 10.1148/radiol.2343040020 15681690

32. Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V et al. Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer. 2018 Jan 1;115:34–41. doi: 10.1016/j.lungcan.2017.10.015 29290259

33. Yip C, Davnall F, Kozarski R, Landau DB, Cook GJ, Ross et al. Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Diseases of the Esophagus. 2015 Mar 1;28(2):172–9. doi: 10.1111/dote.12170 24460831

34. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy. Journal of nuclear medicine. 2013;54(1):19–26. doi: 10.2967/jnumed.112.107375 23204495

35. Bogowicz M, Riesterer O, Stark LS, Studer G, Unkelbach J, Guckenberger M et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta oncologica. 2017 Nov 2;56(11):1531–6. doi: 10.1080/0284186X.2017.1346382 28820287

36. Chalkidou A, O'Doherty MJ, Marsden PK. False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS One. 2015;10(suppl 5):e0124165.


Článok vyšiel v časopise

PLOS One


2019 Číslo 11
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#