Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization

Molecular Imaging and Biology

Volumn 18, Issue 6, 2016, Pages 935-945

  Lu, Lijun ^a   Lv, Wenbing ^a   Jiang, Jun ^a   Ma, Jianhua ^a   Feng, Qianjin ^a   Rahmim, Arman ^b,c   Chen, Wufan ^a  

^a SOUTHERN MEDICAL UNIVERSITY   (China)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c Johns Hopkins University   (United States)

Author keywords

Nasopharyngeal carcinoma; PET; Radiomic; 11C choline; 18F FDG

Indexed keywords

CHOLINE C 11; FLUORODEOXYGLUCOSE F 18; CARBON; CHOLINE;

ADOLESCENT; ADULT; AGED; ARTICLE; CLINICAL ARTICLE; CLINICAL EVALUATION; CLINICAL FEATURE; COMPUTER ASSISTED EMISSION TOMOGRAPHY; CONTROLLED STUDY; FEMALE; HISTOGRAM; HUMAN; MALE; NASOPHARYNX CARCINOMA; PET-CT SCANNER; PRIORITY JOURNAL; CARCINOMA; CHEMISTRY; CLINICAL TRIAL; DIAGNOSTIC IMAGING; IMAGE PROCESSING; MIDDLE AGED; NASOPHARYNX TUMOR; POSITRON EMISSION TOMOGRAPHY-COMPUTED TOMOGRAPHY; RADIOMETRY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; CARBON RADIOISOTOPES; CARCINOMA; CHOLINE; FEMALE; FLUORODEOXYGLUCOSE F18; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; MIDDLE AGED; NASOPHARYNGEAL NEOPLASMS; POSITRON EMISSION TOMOGRAPHY COMPUTED TOMOGRAPHY; RADIOMETRY; YOUNG ADULT;

EID: 84975260856     PISSN: 15361632     EISSN: 18602002     Source Type: Journal    
DOI: 10.1007/s11307-016-0973-6     Document Type: Article

References (55)

1. Yu MC, Yuan JM (2002) Epidemiology of nasopharyngeal carcinoma. Semin Cancer Biol 12:421–429
2. Lee AW, Ma BB, Ng WT et al (2015) Management of nasopharyngeal carcinoma: current practice and future perspective. J Clin Oncol 33:3356–3364
3. Krause BJ, Schwarzenbock S, Souvatzoglou M (2013) FDG PET and PET/CT. Recent Results Cancer Res 187:351–369
4. 18F-FDG PET can replace conventional work-up in primary M staging of nonkeratinizing nasopharyngeal carcinoma. J Nucl Med 48:1614–1619
5. O’Donnell HE, Plowman PN, Khaira MK et al (2008) PET scanning and Gamma Knife radiosurgery in the early diagnosis and salvage “cure” of locally recurrent nasopharyngeal carcinoma. Br J Radiol 81:e26–e30
6. Ng SH, Chan SC, Yen TC et al (2009) Staging of untreated nasopharyngeal carcinoma with PET/CT: comparison with conventional imaging work-up. Eur J Nucl Med Mol Imaging 36:12–22
7. 18F-FDG PET/CT. J Nucl Med 52:341–346
8. Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New Engl J Med 366:883–892
9. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006
10. Asselin M, O’Connor JP, Boellaard R et al (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48:447–455
11. 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40:133–140
12. 18F-FDG uptake as a predictor of patient outcome. J Nucl Med 49:1973–1979
13. El Naqa I, Grigsby PW, Apte A et al (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn 42:1162–1171
14. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med 56:38–44
15. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248
16. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446
17. Rahmim A, Schmidtlein CR, Jackson A et al (2015) A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction. Phys Med Biol 61:227–242
18. 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378
19. 18F-FDG PET. J Nucl Med 53:693–700
20. 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med 55:1235–1241
21. Van Velden FH, Cheebsumon P, Yaqub M et al (2011) Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 38:1636–1647
22. Vriens D, Disselhorst JA, Oyen WJ et al (2012) Quantitative assessment of heterogeneity in tumor metabolism using FDG-PET. Int J Radiat Oncol Biol Phys 82:e725–e731
23. 18F-FDG PET scans of oesophageal cancer. Eur Radiol 25:2805–2812
24. 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging 40:1662–1671
25. Galavis PE, Hollensen C, Jallow N et al (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49:1012–1016
26. 18F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. doi:10.1007/s11307-016-0940-2
27. Vallieres M, Freeman CR, Skamene SR et al (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496
28. 18F fluorothymidine-positron emission tomography. Phys Med Biol 58:187–203
29. Zaidi H, El Naqa I (2010) PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 37:2165–2187
30. 18F-FDG PET/CT 1.0. J Nucl Med 47:885–895
31. 18F-FDG PET/CT. PLoS ONE 10, e131801
32. Frings V, van Velden FH, Velasquez LM et al (2014) Repeatability of metabolically active tumor volume measurements with FDG PET/CT in advanced gastrointestinal malignancies: a multicenter study. Radiology 273:539–548
33. 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med 46:1342–1348
34. Tian J, Xue J, Dai Y et al (2008) A novel software platform for medical image processing and analyzing. IEEE Trans Inf Technol Biomed 12:800–812
35. Adams R, Bishof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647
36. Leijenaar RT, Carvalho S, Velazquez ER et al (2013) Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52:1391–1397
37. Wahl RL, Jacene H, Kasamon Y et al (2009) From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 50:122S–150S
38. Leijenaar RT, Nalbantov G, Carvalho S et al (2015) The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 5:11075
39. Thibault G, Fertil B, Navarro C et al. (2009) Texture indexes and gray level size zone matrix application to cell nuclei classification. Pattern Recognition Inf Process: 140–145
40. Galloway MM (1975) Texture analysis using grey level run lengths. Comput Graphics Image Process 4:172–179
41. Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274
42. Bartko JJ (1966) The intraclass correlation coefficient as a measure of reliability. Psychol Rep 19:3–11
43. 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med 54:19–26
44. 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 55:891–897
45. 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55:414–422
46. Rahmim A, Salimpour Y, Jain S et al (2016) Application of texture analysis to DAT SPECT imaging: relationship to clinical assessments. NeuroImage: Clin. doi:10.1016/j.nicl.2016.02.012
47. Brooks FJ (2013) On some misconceptions about tumor heterogeneity quantification. Eur J Nucl Med Mol Imaging 40:1292–1294
48. Naqa IE (2014) The role of quantitative PET in predicting cancer treatment outcomes. Clin Translat Imaging 2:305–320
49. Cheng NM, Fang YH, Yen TC (2013) The promise and limits of PET texture analysis. Ann Nucl Med 27:867–869
50. Brooks FJ, Grigsby PW (2014) The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med 55:37–42
51. Ashrafinia S, Gonzalez E, Mohy-Ud-Din H et al. (2016) Adaptive PSF modeling for enhanced heterogeneity quantification in oncologic PET imaging. SNMMI Annual Meeting
52. Yu H, Caldwell C, Mah K et al (2009) Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging 28:374–383
53. Hatt M, Rest CCL, Descourt P, Dekker A, Ruysscher DD, Oellers M, Lambin P, Pradier O, Visvikis D (2010) Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 77:301–308
54. Zaidi H, Abdoli M, Fuentes CL, EI Naga IM (2012) Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging 39:881–891
55. Abdoli M, Dierckx RA, Zaidi H (2013) Contourlet-based active contour model for PET image segmentation. Med Phys 40(082507):1–12