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

Lijun Lu1, Wenbing Lv1, Jun Jiang1, Jianhua Ma2, Qianjin Feng2, Arman Rahmim3, Wufan Chen1
1School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
2School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
3Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

Yu MC, Yuan JM (2002) Epidemiology of nasopharyngeal carcinoma. Semin Cancer Biol 12:421–429

Lee AW, Ma BB, Ng WT et al (2015) Management of nasopharyngeal carcinoma: current practice and future perspective. J Clin Oncol 33:3356–3364

Krause BJ, Schwarzenbock S, Souvatzoglou M (2013) FDG PET and PET/CT. Recent Results Cancer Res 187:351–369

Liu FY, Lin CY, Chang JT et al (2007) 18F-FDG PET can replace conventional work-up in primary M staging of nonkeratinizing nasopharyngeal carcinoma. J Nucl Med 48:1614–1619

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

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

Wu H, Wang Q, Wang M et al (2011) Preliminary study of 11C-choline PET/CT for T staging of locally advanced nasopharyngeal carcinoma: comparison with 18F-FDG PET/CT. J Nucl Med 52:341–346

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

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

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

Chicklore S, Goh V, Siddique M et al (2013) Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40:133–140

Eary JF, O’Sullivan F, O’Sullivan J et al (2008) Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J Nucl Med 49:1973–1979

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

Hatt M, Majdoub M, Vallieres M et al (2015) 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

Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

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

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

Tixier F, Le Rest CC, Hatt M et al (2011) 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

Tixier F, Hatt M, Le Rest CC et al (2012) Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 53:693–700

Tixier F, Hatt M, Valla C et al (2014) Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med 55:1235–1241

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

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

Doumou G, Siddique M, Tsoumpas C et al (2015) The precision of textural analysis in 18F-FDG PET scans of oesophageal cancer. Eur Radiol 25:2805–2812

Hatt M, Tixier F, Cheze LRC et al (2013) Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging 40:1662–1671

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

Van Velden FHP, Kramer GM, Frings V et al (2016) Repeatability of radiomic features in non-small-cell lung cancer [18F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. doi: 10.1007/s11307-016-0940-2

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

Willaime J, Turkheimer FE, Kenny LM et al (2013) Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol 58:187–203

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

Delbeke D, Coleman RE, Guiberteau MJ et al (2006) Procedure guideline for tumor imaging with 18F-FDG PET/CT 1.0. J Nucl Med 47:885–895

Jiang J, Wu H, Huang M et al (2015) Variability of gross tumor volume in nasopharyngeal carcinoma using 11C-choline and 18F-FDG PET/CT. PLoS ONE 10, e131801

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

Nestle U, Kremp S, Schaefer-Schuler A et al (2005) 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 46:1342–1348

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

Adams R, Bishof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647

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

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

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

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

Galloway MM (1975) Texture analysis using grey level run lengths. Comput Graphics Image Process 4:172–179

Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274

Bartko JJ (1966) The intraclass correlation coefficient as a measure of reliability. Psychol Rep 19:3–11

Cook GJ, Yip C, Siddique M et al (2013) Are pretreatment 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

Bundschuh RA, Dinges J, Neumann L et al (2014) Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 55:891–897

Orlhac F, Soussan M, Maisonobe J et al (2014) 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 55:414–422

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

Brooks FJ (2013) On some misconceptions about tumor heterogeneity quantification. Eur J Nucl Med Mol Imaging 40:1292–1294

Naqa IE (2014) The role of quantitative PET in predicting cancer treatment outcomes. Clin Translat Imaging 2:305–320

Cheng NM, Fang YH, Yen TC (2013) The promise and limits of PET texture analysis. Ann Nucl Med 27:867–869

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

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

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

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

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

Abdoli M, Dierckx RA, Zaidi H (2013) Contourlet-based active contour model for PET image segmentation. Med Phys 40(082507):1–12