Advertisement

Nuclear Medicine and Molecular Imaging

, Volume 52, Issue 3, pp 170–189 | Cite as

Radiomics in Oncological PET/CT: Clinical Applications

  • Jeong Won Lee
  • Sang Mi Lee
Review

Abstract

18F–fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Keywords

Positron emission tomography Neoplasm Radiomics Heterogeneity Image analysis 

Notes

Compliance with Ethical Standards

Conflict of Interest

Jeong Won Lee and Sang Mi Lee declare that they have no conflict of interest.

Ethical Approval

This work does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

References

  1. 1.
    Lee JW, Kim EY, Kim DJ, Lee JH, Kang WJ, Lee JD, et al. The diagnostic ability of 18F-FDG PET/CT for mediastinal lymph node staging using 18F-FDG uptake and volumetric CT histogram analysis in non-small cell lung cancer. Eur Radiol. 2016;26:4515–23.PubMedCrossRefGoogle Scholar
  2. 2.
    Lebon V, Alberini JL, Pierga JY, Dieras V, Jehanno N, Wartski M. Rate of distant metastases on 18F-FDG PET/CT at initial staging of breast cancer: comparison of women younger and older than 40 years. J Nucl Med. 2017;58:252–7.PubMedCrossRefGoogle Scholar
  3. 3.
    Moon SH, Cho SH, Park LC, Ji JH, Sun JM, Ahn JS, et al. Metabolic response evaluated by 18F-FDG PET/CT as a potential screening tool in identifying a subgroup of patients with advanced non-small cell lung cancer for immediate maintenance therapy after first-line chemotherapy. Eur J Nucl Med Mol Imaging. 2013;40:1005–13.PubMedCrossRefGoogle Scholar
  4. 4.
    Lee JW, Lee SM, Son MW, Lee MS. Diagnostic performance of FDG PET/CT for surveillance in asymptomatic gastric cancer patients after curative surgical resection. Eur J Nucl Med Mol Imaging. 2016;43:881–8.PubMedCrossRefGoogle Scholar
  5. 5.
    Lee JW, Lee SM, Lee MS, Shin HC. Role of (1)(8)F-FDG PET/CT in the prediction of gastric cancer recurrence after curative surgical resection. Eur J Nucl Med Mol Imaging. 2012;39:1425–34.PubMedCrossRefGoogle Scholar
  6. 6.
    Machtay M, Duan F, Siegel BA, Snyder BS, Gorelick JJ, Reddin JS, et al. Prediction of survival by [18F]fluorodeoxyglucose positron emission tomography in patients with locally advanced non-small-cell lung cancer undergoing definitive chemoradiation therapy: results of the ACRIN 6668/RTOG 0235 trial. J Clin Oncol. 2013;31:3823–30.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Lee JW, Lee SM, Yun M, Cho A. Prognostic value of volumetric parameters on staging and posttreatment FDG PET/CT in patients with stage IV non-small cell lung cancer. Clin Nucl Med. 2016;41:347–53.PubMedCrossRefGoogle Scholar
  8. 8.
    O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2015;21:249–57.PubMedCrossRefGoogle Scholar
  9. 9.
    Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present... Any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.PubMedCrossRefGoogle Scholar
  10. 10.
    Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010;467:1109–13.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    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. Eur J Cancer. 2012;48:441–6.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Hatt M, Tixier F, Visvikis D. Cheze le rest C. Radiomics in PET/CT: more than meets the eye? J Nucl Med. 2017;58:365–6.PubMedCrossRefGoogle Scholar
  13. 13.
    Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT-images for early detection of liver malignancy. Biomed Sci Instrum. 1995;31:213–7.PubMedGoogle Scholar
  14. 14.
    Schad LR, Bluml S, Zuna I. MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging. 1993;11:889–96.PubMedCrossRefGoogle Scholar
  15. 15.
    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 Recogn. 2009;42:1162–71.CrossRefGoogle Scholar
  16. 16.
    Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging. 2016;43:133–41.PubMedCrossRefGoogle Scholar
  17. 17.
    Wray R, Solnes L, Mena E, Meoded A, Subramaniam RM. (18)F-Flourodeoxy-glucose PET/computed tomography in brain tumors: value to patient management and survival outcomes. PET Clin. 2015;10:423–30.PubMedCrossRefGoogle Scholar
  18. 18.
    Colavolpe C, Metellus P, Mancini J, Barrie M, Bequet-Boucard C, Figarella-Branger D, et al. Independent prognostic value of pre-treatment 18-FDG-PET in high-grade gliomas. J Neuro-Oncol. 2012;107:527–35.CrossRefGoogle Scholar
  19. 19.
    Nihashi T, Dahabreh IJ, Terasawa T. Diagnostic accuracy of PET for recurrent glioma diagnosis: a meta-analysis. AJNR Am J Neuroradiol. 2013;34:944–50. s1-11PubMedCrossRefGoogle Scholar
  20. 20.
    Mitamura K, Yamamoto Y, Kudomi N, Maeda Y, Norikane T, Miyake K, et al. Intratumoral heterogeneity of 18F-FLT uptake predicts proliferation and survival in patients with newly diagnosed gliomas. Ann Nucl Med. 2017;31:46–52.PubMedCrossRefGoogle Scholar
  21. 21.
    Greene-Schloesser D, Robbins ME, Peiffer AM, Shaw EG, Wheeler KT, Chan MD. Radiation-induced brain injury: a review. Front Oncol. 2012;2:73.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Lohmann P, Stoffels G, Ceccon G, Rapp M, Sabel M, Filss CP, et al. Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans. Eur Radiol. 2017;27:2916–27.PubMedCrossRefGoogle Scholar
  23. 23.
    Radbruch A, Fladt J, Kickingereder P, Wiestler B, Nowosielski M, Baumer P, et al. Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro Oncology. 2015;17:151–9.PubMedCrossRefGoogle Scholar
  24. 24.
    Kebir S, Khurshid Z, Gaertner FC, Essler M, Hattingen E, Fimmers R, et al. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma. Oncotarget. 2017;8:8294–304.PubMedCrossRefGoogle Scholar
  25. 25.
    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.PubMedCrossRefGoogle Scholar
  26. 26.
    Curado MP, Hashibe M. Recent changes in the epidemiology of head and neck cancer. Curr Opin Oncol. 2009;21:194–200.PubMedCrossRefGoogle Scholar
  27. 27.
    Chan SC, Chang KP, Fang YD, Tsang NM, Ng SH, Hsu CL, et al. Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr virus load predicts prognosis in patients with primary nasopharyngeal carcinoma. Laryngoscope. 2017;127:E22–e8.PubMedCrossRefGoogle Scholar
  28. 28.
    Chen SW, Shen WC, Lin YC, Chen RY, Hsieh TC, Yen KY, et al. Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes. Eur J Nucl Med Mol Imaging. 2017;44:567–80.PubMedCrossRefGoogle Scholar
  29. 29.
    Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419–28.PubMedCrossRefGoogle Scholar
  30. 30.
    Choi JW, Lee D, Hyun SH, Han M, Kim JH, Lee SJ. Intratumoural heterogeneity measured using FDG PET and MRI is associated with tumour-stroma ratio and clinical outcome in head and neck squamous cell carcinoma. Clin Radiol. 2017;72:482–9.PubMedCrossRefGoogle Scholar
  31. 31.
    Hofheinz F, Lougovski A, Zophel K, Hentschel M, Steffen IG, Apostolova I, et al. Increased evidence for the prognostic value of primary tumor asphericity in pretherapeutic FDG PET for risk stratification in patients with head and neck cancer. Eur J Nucl Med Mol Imaging. 2015;42:429–37.PubMedCrossRefGoogle Scholar
  32. 32.
    Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schoder H, et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol. 2017;62:5327–43.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Kim BS, Kim SJ, Pak K. Diagnostic value of metabolic heterogeneity as a reliable parameter for differentiating malignant parotid gland tumors. Ann Nucl Med. 2016;30:346–54.PubMedCrossRefGoogle Scholar
  34. 34.
    Kwon SH, Yoon JK, An YS, Shin YS, Kim CH, Lee DH, et al. Prognostic significance of the intratumoral heterogeneity of (18) F-FDG uptake in oral cavity cancer. J Surg Oncol. 2014;110:702–6.PubMedCrossRefGoogle Scholar
  35. 35.
    Mena E, Taghipour M, Sheikhbahaei S, Jha AK, Rahmim A, Solnes L, et al. Value of intratumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT parameters to predict prognosis in patients with HPV-positive primary oropharyngeal squamous cell carcinoma. Clin Nucl Med. 2017;42:e227–e34.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Oh JS, Kang BC, Roh JL, Kim JS, Cho KJ, Lee SW, et al. Intratumor textural heterogeneity on pretreatment (18)F-FDG PET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol. 2015;22:2746–54.PubMedCrossRefGoogle Scholar
  37. 37.
    Wang HM, Cheng NM, Lee LY, Fang YH, Chang JT, Tsan DL, et al. Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. Int J Cancer. 2016;138:731–8.PubMedCrossRefGoogle Scholar
  38. 38.
    Huang SH, O’Sullivan B. Overview of the 8th edition TNM classification for head and neck cancer. Curr Treat Options in Oncol. 2017;18:40.CrossRefGoogle Scholar
  39. 39.
    Choi EK. Chong a, ha JM, Jung CK, O JH, Kim SH. Clinicopathological characteristics including BRAF V600E mutation status and PET/CT findings in papillary thyroid carcinoma. Clin Endocrinol. 2017;87:73–9.CrossRefGoogle Scholar
  40. 40.
    Lapa C, Werner RA, Schmid JS, Papp L, Zsoter N, Biko J, et al. Prognostic value of positron emission tomography-assessed tumor heterogeneity in patients with thyroid cancer undergoing treatment with radiopeptide therapy. Nucl Med Biol. 2015;42:349–54.PubMedCrossRefGoogle Scholar
  41. 41.
    Xing M, Westra WH, Tufano RP, Cohen Y, Rosenbaum E, Rhoden KJ, et al. BRAF mutation predicts a poorer clinical prognosis for papillary thyroid cancer. J Clin Endocrinol Metab. 2005;90:6373–9.PubMedCrossRefGoogle Scholar
  42. 42.
    Yoon S, An YS, Lee SJ, So EY, Kim JH, Chung YS, et al. Relation between F-18 FDG uptake of PET/CT and BRAFV600E mutation in papillary thyroid cancer. Medicine (Baltimore). 2015;94:e2063.CrossRefGoogle Scholar
  43. 43.
    Rothenberg SM, McFadden DG, Palmer EL, Daniels GH, Wirth LJ. Redifferentiation of iodine-refractory BRAF V600E-mutant metastatic papillary thyroid cancer with dabrafenib. Clin Cancer Res. 2015;21:1028–35.PubMedCrossRefGoogle Scholar
  44. 44.
    Kim SJ, Chang S. Predictive value of intratumoral heterogeneity of F-18 FDG uptake for characterization of thyroid nodules according to Bethesda categories of fine needle aspiration biopsy results. Endocrine. 2015;50:681–8.PubMedCrossRefGoogle Scholar
  45. 45.
    Chang JW, Park KW, Heo JH, Jung SN, Liu L, Kim SM, et al. Relationship between 18F-fluorodeoxyglucose accumulation and the BRAF V600E mutation in papillary thyroid cancer. World J Surg. 2017.  https://doi.org/10.1007/s00268-017-4136-y.
  46. 46.
    Nagarajah J, Ho AL, Tuttle RM, Weber WA, Grewal RK. Correlation of BRAFV600E mutation and glucose metabolism in thyroid cancer patients: an (1)(8)F-FDG PET study. J Nucl Med. 2015;56:662–7.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Sheikhbahaei S, Mena E, Yanamadala A, Reddy S, Solnes LB, Wachsmann J, et al. The value of FDG PET/CT in treatment response assessment, follow-up, and surveillance of lung cancer. AJR Am J Roentgenol. 2017;208:420–33.PubMedCrossRefGoogle Scholar
  48. 48.
    Apostolova I, Rogasch J, Buchert R, Wertzel H, Achenbach HJ, Schreiber J, et al. Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC. BMC Cancer. 2014;14:896.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Apostolova I, Ego K, Steffen IG, Buchert R, Wertzel H, Achenbach HJ, et al. The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers. Eur J Nucl Med Mol Imaging. 2016;43:2360–73.PubMedCrossRefGoogle Scholar
  50. 50.
    Cook GJ, O’Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of (18)F-FDG uptake at PET-association with treatment response and prognosis. Radiology. 2015;276:883–93.PubMedCrossRefGoogle Scholar
  51. 51.
    Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, et al. Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging. 2016;43:1477–85.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Dong X, Wu P, Sun X, Li W, Wan H, Yu J, et al. Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J Med Imaging Radiat Oncol. 2015;59:338–45.PubMedCrossRefGoogle Scholar
  53. 53.
    Dong X, Sun X, Sun L, Maxim PG, Xing L, Huang Y, et al. Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS One. 2016;11:e0157836.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, et al. Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors. Radiology. 2016;278:214–22.PubMedCrossRefGoogle Scholar
  55. 55.
    Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, et al. Potential use of (18)F-fluorodeoxyglucose positron emission tomography-based quantitative imaging features for guiding dose escalation in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2016;94:368–76.PubMedCrossRefGoogle Scholar
  56. 56.
    Gao X, Chu C, Li Y, Lu P, Wang W, Liu W, et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur J Radiol. 2015;84:312–7.PubMedCrossRefGoogle Scholar
  57. 57.
    Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, et al. Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: a preliminary result. Nucl Med Mol Imaging. 2014;48:278–86.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Hatt M, Majdoub M, Vallieres M, Tixier F, Le Rest CC, Groheux D, et al. 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. 2015;56:38–44.PubMedCrossRefGoogle Scholar
  59. 59.
    Kim DH, Jung JH, Son SH, Kim CY, Hong CM, Oh JR, et al. Prognostic significance of intratumoral metabolic heterogeneity on 18F-FDG PET/CT in pathological N0 non-small cell lung cancer. Clin Nucl Med. 2015;40:708–14.PubMedCrossRefGoogle Scholar
  60. 60.
    Kim DH, Jung JH, Son SH, Kim CY, Jeong SY, Lee SW, et al. Quantification of intratumoral metabolic macroheterogeneity on 18F-FDG PET/CT and its prognostic significance in pathologic N0 squamous cell lung carcinoma. Clin Nucl Med. 2016;41:e70–5.PubMedCrossRefGoogle Scholar
  61. 61.
    Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging. 2016;43:1453–60.PubMedCrossRefGoogle Scholar
  62. 62.
    Miwa K, Inubushi M, Wagatsuma K, Nagao M, Murata T, Koyama M, et al. FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules. Eur J Radiol. 2014;83:715–9.PubMedCrossRefGoogle Scholar
  63. 63.
    Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A, et al. Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med. 2016;57:842–8.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Orlhac F, Soussan M, Chouahnia K, Martinod E, Buvat I. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. PLoS One. 2015;10:e0145063.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S, et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med. 2014;55:1235–41.PubMedCrossRefGoogle Scholar
  67. 67.
    van Gomez Lopez O, Vicente AMG, Martinez AFH, Castrejon AMS, Londono GAJ, Udias JM, et al. Heterogeneity in [18F]fluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging. Mol Imaging. 2014;13:7290201400032.CrossRefGoogle Scholar
  68. 68.
    Wu J, Aguilera T, Shultz D, Gudur M, Rubin DL, Loo BW Jr, et al. Early-stage non-small cell lung cancer: quantitative imaging characteristics of (18)F fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology. 2016;281:270–8.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, et al. Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med. 2017;58:569–76.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Ginsburg O, Bray F, Coleman MP, Vanderpuye V, Eniu A, Kotha SR, et al. The global burden of women’s cancers: a grand challenge in global health. Lancet. 2017;389:847–60.PubMedCrossRefGoogle Scholar
  71. 71.
    Harris LN, Ismaila N, McShane LM, Andre F, Collyar DE, Gonzalez-Angulo AM, et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol. 2016;34:1134–50.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Crown J, O’Shaughnessy J, Gullo G. Emerging targeted therapies in triple-negative breast cancer. Ann Oncol. 2012;23(Suppl 6):vi56–65.PubMedCrossRefGoogle Scholar
  73. 73.
    Steenbruggen TG, van Ramshorst MS, Kok M, Linn SC, Smorenburg CH, Sonke GS. Neoadjuvant therapy for breast cancer: established concepts and emerging strategies. Drugs. 2017;77:1313–36.PubMedCrossRefGoogle Scholar
  74. 74.
    Yoon HJ, Kang KW, Chun IK, Cho N, Im SA, Jeong S, et al. Correlation of breast cancer subtypes, based on estrogen receptor, progesterone receptor, and HER2, with functional imaging parameters from (6)(8)Ga-RGD PET/CT and (1)(8)F-FDG PET/CT. Eur J Nucl Med Mol Imaging. 2014;41:1534–43.PubMedCrossRefGoogle Scholar
  75. 75.
    Groheux D, Giacchetti S, Moretti JL, Porcher R, Espie M, Lehmann-Che J, et al. Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. Eur J Nucl Med Mol Imaging. 2011;38:426–35.PubMedCrossRefGoogle Scholar
  76. 76.
    Hyun SH, Ahn HK, Park YH, Im YH, Kil WH, Lee JE, et al. Volume-based metabolic tumor response to neoadjuvant chemotherapy is associated with an increased risk of recurrence in breast cancer. Radiology. 2015;275:235–44.PubMedCrossRefGoogle Scholar
  77. 77.
    Koo HR, Park JS, Kang KW, Han W, Park IA, Moon WK. Correlation between (18)F-FDG uptake on PET/CT and prognostic factors in triple-negative breast cancer. Eur Radiol. 2015;25:3314–21.PubMedCrossRefGoogle Scholar
  78. 78.
    Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging. 2015;42:1682–91.PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Groheux D, Martineau A, Teixeira L, Espie M, de Cremoux P, Bertheau P, et al. 18FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis. Breast Cancer Res. 2017;19:3.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Ha S, Park S, Bang JI, Kim EK, Lee HY. Metabolic radiomics for pretreatment 18F-FDG PET/CT to characterize locally advanced breast cancer: histopathologic characteristics, response to neoadjuvant chemotherapy, and prognosis. Sci Rep. 2017;7:1556.PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Kim TH, Yoon JK, Kang DK, Lee SJ, Jung YS, Yim H, et al. Correlation between F-18 fluorodeoxyglucose positron emission tomography metabolic parameters and dynamic contrast-enhanced MRI-derived perfusion data in patients with invasive ductal breast carcinoma. Ann Surg Oncol. 2015;22:3866–72.PubMedCrossRefGoogle Scholar
  82. 82.
    Lemarignier C, Martineau A, Teixeira L, Vercellino L, Espie M, Merlet P, et al. Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients. Eur J Nucl Med Mol Imaging. 2017;44:1145–54.PubMedCrossRefGoogle Scholar
  83. 83.
    Shin S, Pak K, Park DY, Kim SJ. Tumor heterogeneity assessed by 18F-FDG PET/CT is not significantly associated with nodal metastasis in breast cancer patients. Oncol Res Treat. 2016;39:61–6.PubMedCrossRefGoogle Scholar
  84. 84.
    Son SH, Kim DH, Hong CM, Kim CY, Jeong SY, Lee SW, et al. Prognostic implication of intratumoral metabolic heterogeneity in invasive ductal carcinoma of the breast. BMC Cancer. 2014;14:585.PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    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:e94017.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Yoon HJ, Kim Y, Kim BS. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol. 2015;25:3648–58.PubMedCrossRefGoogle Scholar
  87. 87.
    Horiuchi D, Kusdra L, Huskey NE, Chandriani S, Lenburg ME, Gonzalez-Angulo AM, et al. MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibition. J Exp Med. 2012;209:679–96.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Henry KE, Dilling TR, Abdel-Atti D, Edwards KJ, Evans MJ, Lewis JS. Non-invasive 89Zr-transferrin PET shows improved tumor targeting compared to 18F-FDG PET in MYC-overexpressing human triple negative breast cancer. J Nucl Med. 2017.  https://doi.org/10.2967/jnumed.117.192286.
  89. 89.
    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.PubMedCrossRefPubMedCentralGoogle Scholar
  90. 90.
    Lagergren J, Smyth E, Cunningham D, Lagergren P. Oesophageal cancer. Lancet. 2017.  https://doi.org/10.1016/S0140-6736(17)31462-9.
  91. 91.
    Tepper J, Krasna MJ, Niedzwiecki D, Hollis D, Reed CE, Goldberg R, et al. Phase III trial of trimodality therapy with cisplatin, fluorouracil, radiotherapy, and surgery compared with surgery alone for esophageal cancer: CALGB 9781. J Clin Oncol. 2008;26:1086–92.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Kwee RM. Prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer with use of 18F FDG PET: a systematic review. Radiology. 2010;254:707–17.PubMedCrossRefGoogle Scholar
  93. 93.
    van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med. 2016;57:691–700.PubMedCrossRefGoogle Scholar
  94. 94.
    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;34:40–6.PubMedCrossRefGoogle Scholar
  95. 95.
    Dong X, Sun X, Zhao X, Zhu W, Sun L, Huang Y, et al. The impact of intratumoral metabolic heterogeneity on postoperative recurrence and survival in resectable esophageal squamous cell carcinoma. Oncotarget. 2017;8:14969–77.PubMedPubMedCentralGoogle Scholar
  96. 96.
    Kim SJ, Pak K, Chang S. Determination of regional lymph node status using (18)F-FDG PET/CT parameters in oesophageal cancer patients: comparison of SUV, volumetric parameters and intratumoral heterogeneity. Br J Radiol. 2016;89:20150673.PubMedCrossRefGoogle Scholar
  97. 97.
    Nakajo M, Jinguji M, Nakabeppu Y, Nakajo M, Higashi R, Fukukura Y, et al. Texture analysis of 18F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2017;44:206–14.PubMedCrossRefGoogle Scholar
  98. 98.
    Tan S, Kligerman S, Chen W, Lu M, Kim G, Feigenberg S, et al. Spatial-temporal [(1)(8)F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys. 2013;85:1375–82.PubMedCrossRefGoogle Scholar
  99. 99.
    Tochigi T, Shuto K, Kono T, Ohira G, Tohma T, Gunji H, et al. Heterogeneity of glucose metabolism in esophageal cancer measured by fractal analysis of fluorodeoxyglucose positron emission tomography image: correlation between metabolic heterogeneity and survival. Dig Surg. 2017;34:186–91.PubMedCrossRefGoogle Scholar
  100. 100.
    Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI. Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front Oncol. 2016;6:72.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    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;88:195–203.PubMedCrossRefGoogle Scholar
  102. 102.
    Conlon KC, Klimstra DS, Brennan MF. Long-term survival after curative resection for pancreatic ductal adenocarcinoma. Clinicopathologic analysis of 5-year survivors. Ann Surg. 1996;223:273–9.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Loehrer PJ Sr, Feng Y, Cardenes H, Wagner L, Brell JM, Cella D, et al. Gemcitabine alone versus gemcitabine plus radiotherapy in patients with locally advanced pancreatic cancer: an eastern cooperative oncology group trial. J Clin Oncol. 2011;29:4105–12.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Cui Y, Song J, Pollom E, Alagappan M, Shirato H, Chang DT, et al. Quantitative analysis of (18)F-fluorodeoxyglucose positron emission tomography identifies novel prognostic imaging biomarkers in locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2016;96:102–9.PubMedCrossRefGoogle Scholar
  105. 105.
    Hyun SH, Kim HS, Choi SH, Choi DW, Lee JK, Lee KH, et al. Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2016;43:1461–8.PubMedCrossRefGoogle Scholar
  106. 106.
    Kim YI, Kim YJ, Paeng JC, Cheon GJ, Lee DS, Chung JK, et al. Heterogeneity index evaluated by slope of linear regression on 18F–FDG PET/CT as a prognostic marker for predicting tumor recurrence in pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2017.  https://doi.org/10.1007/s00259-017-3755-8.
  107. 107.
    Yue Y, Osipov A, Fraass B, Sandler H, Zhang X, Nissen N, et al. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. J Gastrointest Oncol. 2017;8:127–38.PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Arnold CN, Goel A, Blum HE, Boland CR. Molecular pathogenesis of colorectal cancer: implications for molecular diagnosis. Cancer. 2005;104:2035–47.PubMedCrossRefGoogle Scholar
  109. 109.
    Karapetis CS, Khambata-Ford S, Jonker DJ, O’Callaghan CJ, Tu D, Tebbutt NC, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med. 2008;359:1757–65.PubMedCrossRefGoogle Scholar
  110. 110.
    Kotake M, Aoyama T, Munemoto Y, Doden K, Kataoka M, Kobayashi K, et al. Multicenter phase II study of infusional 5-fluorouracil (5-FU), leucovorin, and oxaliplatin, plus biweekly cetuximab as first-line treatment in patients with metastatic colorectal cancer (CELINE trial). Oncol Lett. 2017;13:747–53.PubMedCrossRefGoogle Scholar
  111. 111.
    Ku G, Tan IB, Yau T, Boku N, Laohavinij S, Cheng AL, et al. Management of colon cancer: resource-stratified guidelines from the Asian oncology summit 2012. Lancet Oncol. 2012;13:e470–81.PubMedCrossRefGoogle Scholar
  112. 112.
    Hu F, Tang W, Sun Y, Wan D, Cai S, Zhang Z, et al. The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging. Oncotarget. 2017;8:75597-75606.Google Scholar
  113. 113.
    Bang JI, Ha S, Kang SB, Lee KW, Lee HS, Kim JS, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016;43:422–31.PubMedCrossRefGoogle Scholar
  114. 114.
    Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsoter N, Papp L, et al. Textural parameters of tumor heterogeneity in (1)(8)F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55:891–7.PubMedCrossRefGoogle Scholar
  115. 115.
    Lovinfosse P, Koopmansch B, Lambert F, Jodogne S, Kustermans G, Hatt M, et al. (18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status. Br J Radiol. 2016;89:20160212.PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Kawada K, Nakamoto Y, Kawada M, Hida K, Matsumoto T, Murakami T, et al. Relationship between 18F-fluorodeoxyglucose accumulation and KRAS/BRAF mutations in colorectal cancer. Clin Cancer Res. 2012;18:1696–703.PubMedCrossRefGoogle Scholar
  117. 117.
    Wagner F, Hakami YA, Warnock G, Fischer G, Huellner MW, Veit-Haibach P. Comparison of contrast-enhanced CT and [18F]FDG PET/CT analysis using kurtosis and skewness in patients with primary colorectal cancer. Mol Imaging Biol. 2017.  https://doi.org/10.1007/s11307-017-1066-x.
  118. 118.
    Wiebe E, Denny L, Thomas G. Cancer of the cervix uteri. Int J Gynaecol Obstet. 2012;119(Suppl 2):S100–9.PubMedCrossRefGoogle Scholar
  119. 119.
    Duenas-Gonzalez A, Zarba JJ, Patel F, Alcedo JC, Beslija S, Casanova L, et al. Phase III, open-label, randomized study comparing concurrent gemcitabine plus cisplatin and radiation followed by adjuvant gemcitabine and cisplatin versus concurrent cisplatin and radiation in patients with stage IIB to IVA carcinoma of the cervix. J Clin Oncol. 2011;29:1678–85.PubMedCrossRefGoogle Scholar
  120. 120.
    Kang S, Park JY, Lim MC, Song YJ, Park SH, Kim SK, et al. Pelvic lymph node status assessed by 18F-fluorodeoxyglucose positron emission tomography predicts low-risk group for distant recurrence in locally advanced cervical cancer: a prospective study. Int J Radiat Oncol Biol Phys. 2011;79:788–93.PubMedCrossRefGoogle Scholar
  121. 121.
    Lee JW, Jeon S, Mun ST, Lee SM. Prognostic value of fluorine-18 fluorodeoxyglucose uptake of bone marrow on positron emission tomography/computed tomography for prediction of disease progression in cervical cancer. Int J Gynecol Cancer. 2017;27:776–83.PubMedCrossRefGoogle Scholar
  122. 122.
    Brooks FJ, Grigsby PW. FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement. Radiat Oncol. 2013;8:294.PubMedPubMedCentralCrossRefGoogle Scholar
  123. 123.
    Shen WC, Chen SW, Liang JA, Hsieh TC, Yen KY, Kao CH. [18]Fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type. Eur J Nucl Med Mol Imaging. 2017;10:1721–31.CrossRefGoogle Scholar
  124. 124.
    Mu W, Chen Z, Liang Y, Shen W, Yang F, Dai R, et al. Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images. Phys Med Biol. 2015;60:5123–39.PubMedCrossRefGoogle Scholar
  125. 125.
    Yang F, Young L, Grigsby P. Predictive value of standardized intratumoral metabolic heterogeneity in locally advanced cervical cancer treated with chemoradiation. Int J Gynecol Cancer. 2016;26:777–84.PubMedCrossRefGoogle Scholar
  126. 126.
    Reuze S, Orlhac F, Chargari C, Nioche C, Limkin E, Riet F, et al. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 2017;8:43169–79.PubMedPubMedCentralCrossRefGoogle Scholar
  127. 127.
    Dangoor A, Seddon B, Gerrand C, Grimer R, Whelan J, Judson I. UK guidelines for the management of soft tissue sarcomas. Clin Sarcoma Res. 2016;6:20.PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Coindre JM, Terrier P, Guillou L, Le Doussal V, Collin F, Ranchere D, et al. Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: a study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer. 2001;91:1914–26.PubMedCrossRefGoogle Scholar
  129. 129.
    Fendler WP, Chalkidis RP, Ilhan H, Knosel T, Herrmann K, Issels RD, et al. Evaluation of several FDG PET parameters for prediction of soft tissue tumour grade at primary diagnosis and recurrence. Eur Radiol. 2015;25:2214–21.PubMedCrossRefGoogle Scholar
  130. 130.
    Charest M, Hickeson M, Lisbona R, Novales-Diaz JA, Derbekyan V, Turcotte RE. FDG PET/CT imaging in primary osseous and soft tissue sarcomas: a retrospective review of 212 cases. Eur J Nucl Med Mol Imaging. 2009;36:1944–51.PubMedCrossRefGoogle Scholar
  131. 131.
    Bischoff M, Bischoff G, Buck A, von Baer A, Pauls S, Scheffold F, et al. Integrated FDG-PET-CT: its role in the assessment of bone and soft tissue tumors. Arch Orthop Trauma Surg. 2010;130:819–27.PubMedCrossRefGoogle Scholar
  132. 132.
    Nakajo M, Nakajo M, Jinguji M, Fukukura Y, Nakabeppu Y, Tani A, et al. The value of intratumoral heterogeneity of (18)F-FDG uptake to differentiate between primary benign and malignant musculoskeletal tumours on PET/CT. Br J Radiol. 2015;88:20150552.PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    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;28:926–35.PubMedCrossRefGoogle Scholar
  134. 134.
    Sagiyama K, Watanabe Y, Kamei R, Hong S, Kawanami S, Matsumoto Y, et al. Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: preliminary results. Eur Radiol. 2017.  https://doi.org/10.1007/s00330-017-4912-y.
  135. 135.
    Vallieres M, Freeman CR, Skamene SR, El Naqa I. 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. 2015;60:5471–96.PubMedCrossRefGoogle Scholar
  136. 136.
    Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32:3059–68.PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Chen-Liang TH, Martin-Santos T, Jerez A, Senent L, Orero MT, Remigia MJ, et al. The role of bone marrow biopsy and FDG-PET/CT in identifying bone marrow infiltration in the initial diagnosis of high grade non-Hodgkin B-cell lymphoma and Hodgkin lymphoma. Accuracy in a multicenter series of 372 patients. Am J Hematol. 2015;90:686–90.PubMedCrossRefGoogle Scholar
  138. 138.
    Lee JW, Lee SC, Kim HJ, Lee SM. Prognostic value of bone marrow 18F-FDG uptake on PET/CT in lymphoma patients with negative bone marrow involvement. Hell J Nucl Med. 2017;20:17–25.PubMedGoogle Scholar
  139. 139.
    Robertson VL, Anderson CS, Keller FG, Halkar R, Goodman M, Marcus RB, et al. Role of FDG-PET in the definition of involved-field radiation therapy and management for pediatric Hodgkin’s lymphoma. Int J Radiat Oncol Biol Phys. 2011;80:324–32.PubMedCrossRefGoogle Scholar
  140. 140.
    Kim J, Song YS, Lee JS, Lee WW, Kim SE. Risk stratification of diffuse large B-cell lymphoma with interim PET-CT based on different cutoff Deauville scores. Leuk Lymphoma. 2017.  https://doi.org/10.1080/10428194.2017.1339877.
  141. 141.
    Moon SH, Lee AY, Kim WS, Kim SJ, Cho YS, Choe YS, et al. Value of interim FDG PET/CT for predicting outcome of patients with angioimmunoblastic T-cell lymphoma. Leuk Lymphoma. 2017;58:1341–8.PubMedCrossRefGoogle Scholar
  142. 142.
    Lartizien C, Rogez M, Niaf E, Ricard F. Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J Biomed Health Inform. 2014;18:946–55.PubMedCrossRefGoogle Scholar
  143. 143.
    Watabe T, Tatsumi M, Watabe H, Isohashi K, Kato H, Yanagawa M, et al. Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT. Ann Nucl Med. 2012;26:222–7.PubMedCrossRefGoogle Scholar
  144. 144.
    Ko KY, Liu CJ, Ko CL, Yen RF. Intratumoral heterogeneity of pretreatment 18F-FDG PET images predict disease progression in patients with nasal type extranodal natural killer/T-cell lymphoma. Clin Nucl Med. 2016;41:922–6.PubMedCrossRefGoogle Scholar
  145. 145.
    Hanaoka K, Hosono M, Tatsumi Y, Ishii K, Im SW, Tsuchiya N, et al. Heterogeneity of intratumoral (111)in-ibritumomab tiuxetan and (18)F-FDG distribution in association with therapeutic response in radioimmunotherapy for B-cell non-Hodgkin’s lymphoma. EJNMMI Res. 2015;5:10.PubMedPubMedCentralCrossRefGoogle Scholar
  146. 146.
    Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014;55:37–42.PubMedCrossRefGoogle Scholar
  147. 147.
    Desseroit MC. Tixier F, Weber WA, Siegel BA, Cheze le rest C, Visvikis D, et al. reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med. 2017;58:406–11.PubMedPubMedCentralCrossRefGoogle Scholar
  148. 148.
    Orlhac F, Soussan M, Maisonobe JA, 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;55:414–22.PubMedCrossRefGoogle Scholar
  149. 149.
    Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.PubMedCrossRefGoogle Scholar
  150. 150.
    Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. Robustness of intratumour (1)(8)F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71.PubMedCrossRefGoogle Scholar
  151. 151.
    Lovat E, Siddique M, Goh V, Ferner RE, Cook GJR, Warbey VS. The effect of post-injection 18F-FDG PET scanning time on texture analysis of peripheral nerve sheath tumours in neurofibromatosis-1. EJNMMI Res. 2017;7:35.PubMedPubMedCentralCrossRefGoogle Scholar
  152. 152.
    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:e0124165.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Korean Society of Nuclear Medicine 2017

Authors and Affiliations

  1. 1.Department of Nuclear Medicine, International St. Mary’s HospitalCatholic Kwandong University College of MedicineIncheonSouth Korea
  2. 2.Institute for Integrative Medicine, International St. Mary’s HospitalCatholic Kwandong University College of MedicineIncheonSouth Korea
  3. 3.Department of Nuclear MedicineSoonchunhyang University Cheonan HospitalCheonanSouth Korea

Personalised recommendations