The role of quantitative PET in predicting cancer treatment outcomes

Clinical and Translational Imaging

Volumn 2, Issue 4, 2014, Pages 305-320

  Naqa, Issam El ^a  

^a MONTREAL GENERAL HOSPITAL   (Canada)

Author keywords

Cancer; Multimodality; PET; Prediction; Radiomics; Treatment outcomes

Indexed keywords

FLUORODEOXYGLUCOSE F 18; TRACER;

CANCER CONTROL; CANCER STAGING; CANCER THERAPY; CELL HETEROGENEITY; CELL PROLIFERATION; CHEMORADIOTHERAPY; CLINICAL DECISION MAKING; COMPARTMENT MODEL; CORRELATION COEFFICIENT; GLYCOLYSIS; HISTOGRAM; HUMAN; IMAGING SOFTWARE; LOGISTIC REGRESSION ANALYSIS; LUNG CANCER; MACHINE LEARNING; OUTCOME ASSESSMENT; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIATION DOSE DISTRIBUTION; RADIOTHERAPY PLANNING SYSTEM; RECEIVER OPERATING CHARACTERISTIC; RECURRENCE FREE SURVIVAL; REVIEW; SPINE TUMOR; TREATMENT OUTCOME; TUMOR GROWTH; TUMOR MICROENVIRONMENT; TUMOR VOLUME;

EID: 84913525860     PISSN: 22815872     EISSN: 22817565     Source Type: Journal    
DOI: 10.1007/s40336-014-0063-1     Document Type: Review

References (118)

1. Verhagen AF, Bootsma GP, Tjan-Heijnen VC, van der Wilt GJ, Cox AL, Brouwer MH et al (2004) FDG-PET in staging lung cancer: how does it change the algorithm? Lung Cancer 44: 175-181.
2. Bradley J, Thorstad WL, Mutic S, Miller TR, Dehdashti F, Siegel BA et al (2004) Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 59: 78-86.
3. Bradley JD, Perez CA, Dehdashti F, Siegel BA (2004) Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 45(Suppl 1): 96S-101S.
4. Bradley J (2004) Applications for FDG-PET in lung cancer; staging, targeting, and follow-up. The Radiological Society of North America, Chicago.
5. Erdi YE, Macapinlac H, Rosenzweig KE, Humm JL, Larson SM, Erdi AK et al (2000) Use of PET to monitor the response of lung cancer to radiation treatment. Eur J Nucl Med 27: 861-866.
6. Mac Manus MP, Hicks RJ (2003) PET scanning in lung cancer: current status and future directions. Semin Surg Oncol 21: 149-155.
7. Mac Manus MP, Hicks RJ, Matthews JP, McKenzie A, Rischin D, Salminen EK et al (2003) Positron emission tomography is superior to computed tomography scanning for response-assessment after radical radiotherapy or chemoradiotherapy in patients with non-small-cell lung cancer. J Clin Oncol 21: 1285-1292.
8. MacManus MR, Hicks R, Fisher R, Rischin D, Michael M, Wirth A et al (2003) FDG-PET-detected extracranial metastasis in patients with non-small cell lung cancer undergoing staging for surgery or radical radiotherapy-survival correlates with metastatic disease burden. Acta Oncol 42: 48-54.
9. Pandit N, Gonen M, Krug L, Larson SM (2003) Prognostic value of [18F]FDG-PET imaging in small cell lung cancer. Eur J Nucl Med Mol Imaging 30: 78-84.
10. Toloza EM, Harpole L, McCrory DC (2003) Noninvasive staging of non-small cell lung cancer: a review of the current evidence. Chest 123: 137S-146S.
11. Schwartz DL, Ford E, Rajendran J, Yueh B, Coltrera MD, Virgin J et al (2005) FDG-PET/CT imaging for preradiotherapy staging of head-and-neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 61: 129-136.
12. Suarez Fernandez JP, Maldonado Suarez A, Dominguez Grande ML, Santos Ortega M, Rodriguez Villalba S, Garcia Camanaque L et al (2004) Positron emission tomography (PET) imaging in head and neck cancer. Acta Otorrinolaringol Esp 55: 303-309.
13. Oyama N, Miller TR, Dehdashti F, Siegel BA, Fischer KC, Michalski JM et al (2003) 11C-acetate PET imaging of prostate cancer: detection of recurrent disease at PSA relapse. J Nucl Med 44: 549-555.
14. Mutic S, Malyapa RS, Grigsby PW, Dehdashti F, Miller TR, Zoberi I et al (2003) PET-guided IMRT for cervical carcinoma with positive para-aortic lymph nodes-a dose-escalation treatment planning study. Int J Radiat Oncol Biol Phys 55: 28-35.
15. Miller TR, Grigsby PW (2002) Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy. Int J Radiat Oncol Biol Phys 53: 353-359.
16. Ciernik IF (2004) Radiotherapy of rectal cancer. Schweiz Rundsch Med Prax 93: 1441-1446.
17. 18F-FDG PET early after radiotherapy in lymphoma patients. Cancer Biother Radiopharm 19: 606-612.
18. 18)F]FDG for therapy response monitoring in lymphoma patients. Eur J Nucl Med Mol Imaging 30(Suppl 1): S97-S105.
19. Fogarty GB, Tartaglia CJ, Peters LJ (2004) Primary melanoma of the oesophagus well palliated by radiotherapy. Br J Radiol 77: 1050-1052.
20. Biersack HJ, Bender H, Palmedo H (2004) FDG-PET in monitoring therapy of breast cancer. Eur J Nucl Med Mol Imaging 31(Suppl 1): S112-S117.
21. Lind P, Igerc I, Beyer T, Reinprecht P, Hausegger K (2004) Advantages and limitations of FDG PET in the follow-up of breast cancer. Eur J Nucl Med Mol Imaging 31(Suppl 1): S125-S134.
22. Zangheri B, Messa C, Picchio M, Gianolli L, Landoni C, Fazio F (2004) PET/CT and breast cancer. Eur J Nucl Med Mol Imaging 31(Suppl 1): S135-S142.
23. Brun E, Kjellen E, Tennvall J, Ohlsson T, Sandell A, Perfekt R et al (2002) FDG PET studies during treatment: prediction of therapy outcome in head and neck squamous cell carcinoma. Head Neck 24: 127-135.
24. Hope AJ, Saha P, Grigsby PW (2006) FDG-PET in carcinoma of the uterine cervix with endometrial extension. Cancer 106: 196-200.
25. 18F-FDG PET scans after neoadjuvant chemoradiation provides prognostic stratification in patients with locally advanced rectal carcinoma subsequently treated by radical surgery. J Nucl Med 47: 14-22.
26. 18F] fluorodeoxyglucose positron emission tomography in carcinoma of the cervix: response and outcome. J Clin Oncol 22: 2167-2171.
27. Hicks RJ, Mac Manus MP, Matthews JP, Hogg A, Binns D, Rischin D et al (2004) Early FDG-PET imaging after radical radiotherapy for non-small-cell lung cancer: inflammatory changes in normal tissues correlate with tumor response and do not confound therapeutic response evaluation. Int J Radiat Oncol Biol Phys 60: 412-418.
28. Pugachev A, Ruan S, Carlin S, Larson SM, Campa J, Ling CC et al (2005) Dependence of FDG uptake on tumor microenvironment. Int J Radiat Oncol Biol Phys 62: 545-553.
29. Shields AF (2006) Positron emission tomography measurement of tumor metabolism and growth: its expanding role in oncology. Mol Imaging Biol 8: 141-150.
30. Dehdashti F, Mintun MA, Lewis JS, Bradley J, Govindan R, Laforest R et al (2003) In vivo assessment of tumor hypoxia in lung cancer with 60Cu-ATSM. Eur J Nucl Med Mol Imaging 30: 844-850.
31. Lee ST, Scott AM (2007) Hypoxia positron emission tomography imaging with 18f-fluoromisonidazole. Semin Nucl Med 37: 451-461.
32. 18F-fluorothymidine for monitoring tumor response to radiotherapy and photodynamic therapy in mice. J Nucl Med 45: 1754-1758.
33. 18)F-FLT positron emission tomography/computed tomography imaging for non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 75: 1098-1104.
34. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48: 441-446.
35. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30: 1234-1248.
36. Wong CY, Schmidt J, Bong JS, Chundru S, Kestin L, Yan D et al (2007) Correlating metabolic and anatomic responses of primary lung cancers to radiotherapy by combined F-18 FDG PET-CT imaging. Radiat Oncol 2: 18.
37. Coon D, Gokhale AS, Burton SA, Heron DE, Ozhasoglu C, Christie N (2008) Fractionated stereotactic body radiation therapy in the treatment of primary, recurrent, and metastatic lung tumors: the role of positron emission tomography/computed tomography-based treatment planning. Clin Lung Cancer 9: 217-221.
38. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D et al (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 42: 1162-1171.
39. El Naqa I, Wei L, Yang Y (2009) Content-based image retrieval for digital mammography. In: Mohammed S, Fiaidhi J (eds) Ubiquitous health and medical informatics: the ubiquity 20 trend and beyond. IGI Global, Hershey.
40. El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN (2004) A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging 23: 1233-1244.
41. El Naqa I, Bradley J, Guild C, Apte A, Fergus S, Dehdashti F et al (2008) Pattern recognition analysis of FDG-PET uptake characteristics for assessing response in NSCLC post-radiotherapy treatment. Int J Radiat Oncol Biol Phys 72: S113.
42. Watabe H, Ikoma Y, Kimura Y, Naganawa M, Shidahara M (2006) PET kinetic analysis-compartmental model. Ann Nucl Med 20: 583-588.
43. Seibert RM, Ramsey CR, Hines JW, Kupelian PA, Langen KM, Meeks SL et al (2007) A model for predicting lung cancer response to therapy. Int J Radiat Oncol Biol Phys 67: 601-609.
44. Ramsey CR, Langen KM, Kupelian PA, Scaperoth DD, Meeks SL, Mahan SL et al (2006) A technique for adaptive image-guided helical tomotherapy for lung cancer. Int J Radiat Oncol Biol Phys 64: 1237-1244.
45. Stasi M, Munoz F, Fiorino C, Pasquino M, Baiotto B, Marini P et al (2006) Emptying the rectum before treatment delivery limits the variations of rectal dose-volume parameters during 3DCRT of prostate cancer. Radiother Oncol 80: 363-370.
46. de Crevoisier R, Tucker SL, Dong L, Mohan R, Cheung R, Cox JD et al (2005) Increased risk of biochemical and local failure in patients with distended rectum on the planning CT for prostate cancer radiotherapy. Int J Radiat Oncol Biol Phys 62: 965-973.
47. Bussink J, Kaanders JH, van der Graaf WT, Oyen WJ (2011) PET-CT for radiotherapy treatment planning and response monitoring in solid tumors. Nat Rev Clin Oncol 8: 233-242.
48. Denecke T, Rau B, Hoffmann KT, Hildebrandt B, Ruf J, Gutberlet M et al (2005) Comparison of CT, MRI and FDG-PET in response prediction of patients with locally advanced rectal cancer after multimodal preoperative therapy: is there a benefit in using functional imaging? Eur Radiol 15: 1658-1666.
49. Benz MR, Allen-Auerbach MS, Eilber FC, Chen HJJ, Dry S, Phelps ME et al (2008) Combined assessment of metabolic and volumetric changes for assessment of tumor response in patients with soft-tissue sarcomas. J Nucl Med 49: 1579-1584.
50. Yang D-H, Min J-J, Jeong Y, Ahn J-S, Kim Y-K, Cho S-H et al (2009) The combined evaluation of interim contrast-enhanced computerized tomography (CT) and FDG-PET/CT predicts the clinical outcomes and may impact on the therapeutic plans in patients with aggressive non-Hodgkin's lymphoma. Ann Hematol 88: 425-432.
51. Strauss LG, Conti PS (1991) The applications of PET in clinical oncology. J Nucl Med 32: 623-648.
52. Kidd EA, El Naqa I, Siegel BA, Dehdashti F, Grigsby PW (2012) FDG-PET-based prognostic nomograms for locally advanced cervical cancer. Gynecol Oncol 127: 136-140.
53. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD et al (1999) Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging 2: 159-171.
54. Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I (2012) Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol 102: 239-245.
55. Jain AK (1989) Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs.
56. O'Sullivan F, Roy S, O'Sullivan J, Vernon C, Eary J (2005) Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics 6: 293-301.
57. O'Sullivan F, Roy S, Eary J (2003) A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics 4: 433-448.
58. O'Sullivan F, Wolsztynski E, O'Sullivan J, Richards T, Conrad EU, Eary JF (2011) A statistical modeling approach to the analysis of spatial patterns of FDG-PET uptake in human sarcoma. IEEE Trans Med Imaging 30: 2059-2071.
59. Castleman KR (1996) Digital image processing. Prentice Hall, Englewood Cliffs.
60. Haralick R, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern 3: 610-621.
61. Zhang J, Tan T (2002) Brief review of invariant texture analysis methods. Pattern Recogn 35: 735-747.
62. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59: 1061-1069.
63. Chicklore S, Goh V, Siddique M, Roy A, Marsden P, Cook GR (2013) Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40: 133-140.
64. Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19: 1264-1274.
65. 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52: 369-378.
66. Cheng N-M, Dean Fang Y-H, Tung-Chieh Chang J, Huang C-G, Tsan D-L, Ng S-H et al (2013) Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med 54: 1703-1709.
67. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A 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.
68. Willaime JM, Turkheimer FE, Kenny LM, Aboagye EO (2013) Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol 58: 187-203.
69. Szczypinski PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda-a software package for image texture analysis. Comput Methods Progr Biomed 94: 66-76.
70. Graham MM, Peterson LM, Hayward RM (2000) Comparison of simplified quantitative analyses of FDG uptake. Nucl Med Biol 27: 647-655.
71. Patlak CS, Blasberg RG (1985) Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab 5: 584-590.
72. Choi NC, Fischman AJ, Niemierko A, Ryu JS, Lynch T, Wain J et al (2002) Dose-response relationship between probability of pathologic tumor control and glucose metabolic rate measured with FDG PET after preoperative chemoradiotherapy in locally advanced non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 54: 1024-1035.
73. 18F]FMISO correlates with radiation therapy outcome in head-and-neck cancer patients. Radiother Oncol 80: 151-156.
74. Thorwarth D, Eschmann S-M, Paulsen F, Alber M (2007) A model of reoxygenation dynamics of head-and-neck tumors based on serial 18F-fluoromisonidazole positron emission tomography investigations. Int J Radiat Oncol Biol Phys 68: 515-521.
75. Steel GG (2002) Basic clinical radiobiology, 3rd edn. Arnold/Oxford University Press, London/New York.
76. Webb S (2001) The physics of three-dimensional radiation therapy: conformal radiotherapy, radiosurgery, and treatment planning. Institute of Physics Pub, Bristol.
77. El Naqa I (2013) Outcomes modeling. In: Starkschall G, Siochi C (eds) Informatics in radiation oncology. CRC Press/Taylor and Francis, Boca Raton, pp 257-275.
78. El Naqa I, Suneja G, Lindsay PE, Hope AJ, Alaly JR, Vicic M et al (2006) Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships. Phys Med Biol 51: 5719-5735.
79. Deasy JO, El Naqa I (2007) Image-based modeling of normal tissue complication probability for radiation therapy. In: Mehta M, Bentzen S (eds) Radiation oncology advances. Springer, New York.
80. El Naqa I, Bradley JD, Lindsay PE, Blanco AI, Vicic M, Hope AJ et al (2006) Multi-variable modeling of radiotherapy outcomes including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys 64: 1275-1286.
81. Cherkassky VS, Mulier F (2007) Learning from data: concepts, theory, and methods, 2nd edn. IEEE Press/Wiley-Interscience, Hoboken.
82. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York.
83. El Naqa I, Bradley J, Deasy J (2005) Machine learning methods for radiobiological outcome modeling. In: Mehta M, Paliwal B, Bentzen S (eds) Physical, chemical, and biological targeting in radiation oncology. Medical Physics Publishing, Madison.
84. El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21: 1552-1563.
85. Schèolkopf B, Tsuda K, Vert J-P (2004) Kernel methods in computational biology. MIT Press, Cambridge.
86. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge.
87. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River.
88. El Naqa I, Deasy JO, Mu Y, Huang E, Hope AJ, Lindsay PE et al (2010) Datamining approaches for modeling tumor control probability. Acta Oncol 49: 1363-1373.
89. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405: 442-451.
90. Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36.
91. Sprent P, Smeeton NC (2001) Applied nonparametric statistical methods, 3rd edn. Chapman & Hall/CRC, Boca Raton.
92. Kennedy R, Lee Y, van Roy B, Reed CD, Lippman RP (1998) Solving data mining problems through pattern recognition. Prentice Hall, Englewood Cliffs.
93. Efron B, Tibshirani RJ (1998) An introduction to the bootstrap. 1st CRC Press, Boca Raton (reprint ed. Chapman & Hall/CRC, Boca Raton).
94. Bentzen SM (2008) Dose painting and theragnostic imaging: towards the prescription, planning and delivery of biologically targeted dose distributions in external beam radiation oncology. Cancer Treat Res 139: 41-62.
95. Bentzen SM, Gregoire V (2011) Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription. Semin Radiat Oncol 21: 101-110.
96. Grégoire V, Jeraj R, Lee JA, O'Sullivan B (2012) Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive? Lancet Oncol 13: e292-e300.
97. Fletcher GH (1984) Lucy Wortham James Lecture. Subclinical disease. Cancer 53: 1274-1284.
98. Ling CC, Humm J, Larson S, Amols H, Fuks Z, Leibel S et al (2000) Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47: 551-560.
99. Lebesque JV, Keus RB (1991) The simultaneous boost technique: the concept of relative normalized total dose. Radiother Oncol 22: 45-55.
100. Madani I, Duthoy W, Derie C, De Gersem W, Boterberg T, Saerens M et al (2007) Positron emission tomography-guided, focal-dose escalation using intensity-modulated radiotherapy for head and neck cancer. Int J Radiat Oncol Biol Phys 68: 126-135.
101. Seuntjens J, Serban M, Vallieres M, Hathout L, Freeman C, El Naqa I (2012) Dose-escalation based on MR-PET/CT for soft-tissue sarcoma. Int J Radiat Oncol Biol Phys 84: S660-S661.
102. Bassler N, Jäkel O, Søndergaard CS, Petersen JB (2010) Dose- and LET-painting with particle therapy. Acta Oncol 49: 1170-1176.
103. Thorwarth D, Eschmann SM, Paulsen F, Alber M (2007) Hypoxia dose painting by numbers: a planning study. Int J Radiat Oncol Biol Phys 68: 291-300.
104. Toma-Dasu I, Uhrdin J, Antonovic L, Dasu A, Nuyts S, Dirix P et al (2012) Dose prescription and treatment planning based on FMISO-PET hypoxia. Acta Oncol 51: 222-230.
105. 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.
106. Zaidi H, Abdoli M, Fuentes CL, El Naqa IM (2012) Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging 39(5): 881-891.
107. 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.
108. El Naqa I, Yang D, Apte A, Khullar D, Mutic S, Zheng J et al (2007) Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med Phys 34: 4738-4749.
109. Markel D, Zaidi H, El Naqa I (2013) Novel multimodality segmentation using level sets and Jensen-Renyi divergence. Med Phys 40: 121908.
110. 18F-FDG PET in malignant tumors. J Nucl Med 49: 1804-1808.
111. Kinahan PE, Fletcher JW (2010) Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MRI 31: 496-505.
112. Hatt M, Cheze-Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE et al (2010) Reproducibility of 18F-FDG and 3′-Deoxy-3′-18F-Fluorothymidine PET tumor volume measurements. J Nucl Med 51: 1368-1376.
113. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49: 1012-1016.
114. Cheng NM, Fang YH, Yen TC (2013) The promise and limits of PET texture analysis. Ann Nucl Med 27: 867-869.
115. Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D (2012) Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 53: 693-700.
116. O'Sullivan F, Saha A (1999) Use of ridge regression for improved estimation of kinetic constants from PET data. IEEE Trans Med Imaging 18: 115-125.
117. Turkheimer FE, Hinz R, Gunn RN, Aston JA, Gunn SR, Cunningham VJ (2003) Rank-shaping regularization of exponential spectral analysis for application to functional parametric mapping. Phys Med Biol 48: 3819-3841.
118. Turkheimer FE, Aston JA, Asselin MC, Hinz R (2006) Multi-resolution Bayesian regression in PET dynamic studies using wavelets. Neuroimage 32: 111-121.