18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer

PLoS ONE

Volumn 10, Issue 12, 2015, Pages

  Orlhac, Fanny ^a   Soussan, Michaël ^a   Chouahnia, Kader ^b   Martinod, Emmanuel ^b   Buvat, Irène ^b  

^a DIF   (France)

^b AVICENNE HOSPITAL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

FLUORODEOXYGLUCOSE F 18;

ABSOLUTE RESAMPLING; ADULT; AGED; ARTICLE; CLINICAL ARTICLE; COMPARATIVE STUDY; COMPUTER ASSISTED EMISSION TOMOGRAPHY; CONTROLLED STUDY; ENTROPY; FEMALE; HUMAN; IMAGE ANALYSIS; IMAGING PHANTOM; LUNG ADENOCARCINOMA; MALE; MAXIMUM STANDARDIZED UPTAKE VALUE; METABOLIC VOLUME; NON SMALL CELL LUNG CANCER; RADIATION DOSE; RADIOLOGICAL PARAMETERS; RELATIVE RESAMPLING; SAMPLING; SQUAMOUS CELL LUNG CARCINOMA; TEXTURE INDEX; TISSUE DISTRIBUTION; TISSUE SPECIFICITY; TREND STUDY; TUMOR VOLUME; ADENOCARCINOMA; DIAGNOSTIC IMAGING; IMAGE PROCESSING; LUNG TUMOR; MIDDLE AGED; POSITRON EMISSION TOMOGRAPHY; SQUAMOUS CELL CARCINOMA; VERY ELDERLY;

ADENOCARCINOMA; AGED; AGED, 80 AND OVER; CARCINOMA, NON-SMALL-CELL LUNG; CARCINOMA, SQUAMOUS CELL; FEMALE; FLUORODEOXYGLUCOSE F18; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG NEOPLASMS; MALE; MIDDLE AGED; POSITRON-EMISSION TOMOGRAPHY;

EID: 84956678400     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0145063     Document Type: Article

References (27)

1. Marusyk A, Polyak K. Tumor heterogeneity: causes and consequences. Biochim Biophys Acta. 2010; 1805(1):105-117. doi: 10.1016/j.bbcan.2009.11.002 PMID: 19931353.
2. Cook GJR, 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? J Nucl Med. 2013; 54(1):19-26. doi: 10.2967/jnumed.112.107375 PMID: 23204495.
3. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009; 42(6):1162-1171. PMID: 20161266.
4. 18F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys. 2013; 85(5):1375-1382. doi: 10.1016/j.ijrobp.2012.10.017 PMID: 23219566.
5. Zhang H, Tan S, Chen W, Kligerman S, Kim G, D'Souza WD, et al. Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics. Int J Radiat Oncol Biol Phys. 2014; 88(1):195-203. doi: 10.1016/j. ijrobp.2013.09.037 PMID: 24189128.
6. Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges J-P, 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. 2011; 52(3):369-378. doi: 10.2967/jnumed. 110.082404 PMID: 21321270.
7. Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013; 40 (5):716-727. doi: 10.1007/s00259-012-2332-4 PMID: 23340594.
8. Cheng N-M, Fang Y-HD, Chang JT-C, Huang C-G, Tsan D-L, Ng S-H, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013; 54(10):1703-1709. doi: 10.2967/jnumed.112. 119289 PMID: 24042030.
9. Cheng N-M, Fang Y-HD, Lee L-Y, Chang JT-C, Tsan D-L, Ng S-H, et al. Zone-size nonuniformity of (18)F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015; 42(3):419-428. doi: 10.1007/s00259-014-2933-1 PMID: 25339524.
10. Dong X, Xing L, Wu P, Fu Z, Wan H, Li D, et al. Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage. Nucl Med Commun. 2013; 34(1):40-46. PMID: 23111378.
11. Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012; 102(2):239-245. doi: 10. 1016/j.radonc.2011.10.014 PMID: 22098794.
12. Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Eder V, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PloS One. 2014; 9(4):e94017. doi: 10.1371/journal.pone.0094017 PMID: 24722644.
13. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 2010; 49 (7):1012-1006. doi: 10.3109/0284186X.2010.498437 PMID: 20831489.
14. Orlhac F, Soussan M, Maisonobe J-A, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014; 55(3):414-422. doi: 10.2967/jnumed.113.129858 PMID: 24549286.
15. Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014; 55(1):37-42. doi: 10.2967/jnumed.112.116715 PMID: 24263086.
16. Brooks FJ. On some misconceptions about tumor heterogeneity quantification. Eur J Nucl Med Mol Imaging. 2013; 40(8):1292-1294. doi: 10.1007/s00259-013-2430-y PMID: 23632962.
17. Buvat I, Orlhac F, Soussan M. Tumor Texture Analysis in PET: Where Do We Stand? J Nucl Med. 2015; 56(11):1642-1644. doi: 10.2967/jnumed.115.163469 PMID: 26294296.
18. 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(5):e0124165. doi: 10.1371/journal.pone.0124165 PMID: 25938522.
19. Leijenaar RTH, Carvalho S, Velazquez ER, van Elmpt WJC, Parmar C, Hoekstra OS, et al. Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013; 52(7):1391-1397. doi: 10.3109/0284186X.2013.812798 PMID: 24047337.
20. Orlhac F, Soussan M, Chouahnia K, Martinod E, Buvat I. Enhanced textural indices distinguish tissue types in FDG PET. J Nucl Med. 2014; 56(3):430.
21. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med. 2005; 46(8):1342-1348. PMID: 16085592.
22. Buvat I. A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images. Phys Med Biol. 2002; 47(10):1761-1775. PMID: 12069092.
23. Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K, et al. Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 2014; 28(9):926-935. doi: 10.1007/s12149-014-0895-9 PMID: 25107363.
24. Willaime JMY, Turkheimer FE, Kenny LM, Aboagye EO. Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol. 2013; 58(2):187-203. doi: 10.1088/0031-9155/58/2/187 PMID: 23257054.
25. Asamura H, Goya T, Koshiishi Y, Sohara Y, Eguchi K, Mori M, et al. A Japanese Lung Cancer Registry study: prognosis of 13, 010 resected lung cancers. J Thorac Oncol. 2008; 3(1):46-52. doi: 10.1097/JTO.0b013e31815e8577 PMID: 18166840.
26. Mangili F, Cigala C, Arrigoni G, Rovere E, Gattuso C, Santambrogio G, et al. Cell loss and proliferation in non-small cell lung carcinoma: correlation with histological subtype. Eur J Histochem. 1998; 42 (4):287-295. PMID: 10068901.
27. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: The process and the challenges. Magn Reson Imaging. 2012; 30(9):1234-1248. doi: 10.1016/j.mri.2012.06.010 PMID: 22898692.