Clinical and Translational Imaging

, Volume 5, Issue 4, pp 343–358 | Cite as

Clinical overview of the current state and future applications of positron emission tomography in bone and soft tissue sarcoma

  • Po-Hao ChenEmail author
  • David A. Mankoff
  • Ronnie A. Sebro
Systematic Review
Part of the following topical collections:
  1. Musculoskeletal



Positron emission tomography (PET) provides a noninvasive, functional assessment providing incremental diagnostic value over magnetic resonance imaging (MRI) and computed tomography (CT) for the initial staging, restaging, response assessment, and prognosis of bone and soft tissue sarcomas. The purpose of this article is to review the current state and future applications of PET in sarcoma imaging, including the clinical roles of 18F-fluorodeoxyglucose (FDG) and other PET radiotracers as well as the use of PET with concurrent MRI.


A PubMed search using the query “(‘positron emission tomography’ OR PET) AND (‘CT’ OR ‘computed tomograph*’ OR ‘MR*’ OR ‘magnetic resonance’) AND (FDG OR hypox* OR prolif*) AND sarcoma*” for PET examinations involving bone and soft tissue sarcoma studies up to February 1, 2017 were performed. Additionally, analogous Google Scholar, Scopus, and Web of Science search queries were also performed. Subsequently, references for the retrieved articles were reviewed, and the relevant publications on the subject were also included.


A total of 30 studies were included in the review. FDG-PET with concurrent computed tomography (CT) can provide incremental diagnostic value relative to MRI to provide additional insight into the grading, staging, restaging, and response assessment in sarcomas, particularly when neoadjuvant therapy is an option. FDG-PET/CT can be used for noninvasive prediction of tumor grade and assess regional heterogeneity, providing guidance for tissue sampling and reducing the risk of undergrading and understaging. In addition, early clinical studies of sarcoma PET imaging using hypoxia and cellular proliferation agents suggest incremental diagnostic benefit over FDG-PET. The use of PET/MRI is under active investigation and may yield additional clinically impactful findings over PET/CT.


PET imaging used with concurrent CT or MRI provides a unique noninvasive way to assess regional biological and biochemical features for bone and soft tissue sarcomas.


PET FDG Soft tissue sarcoma Osteosarcoma Review 


Author contribution statement

P-HC: literature search and review, and manuscript writing and editing. DAM: content planning, manuscript editing, and literature review. RAS: content planning, manuscript editing, and literature review.

Compliance with ethical standards

Conflict of interest

Po-Hao Chen declares that he has no relevant conflict of interest. David Mankoff declares that he has no relevant conflict of interest. Ronnie Sebro declares that he has no relevant conflict of interest.

Ethical approval

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

Supplementary material

40336_2017_236_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 14 kb)


  1. 1.
    Rodriguez R, Rubio R, Menendez P (2012) Modeling sarcomagenesis using multipotent mesenchymal stem cells. Cell Res 22:62–77. doi: 10.1038/cr.2011.157 PubMedCrossRefGoogle Scholar
  2. 2.
    Pittenger MF, Mackay AM, Beck SC et al (1999) Multilineage potential of adult human mesenchymal stem cells. Science 284:143–147PubMedCrossRefGoogle Scholar
  3. 3.
    da Silva Meirelles L, Chagastelles PC, Nardi NB (2006) Mesenchymal stem cells reside in virtually all post-natal organs and tissues. J Cell Sci 119:2204–2213. doi: 10.1242/jcs.02932 CrossRefGoogle Scholar
  4. 4.
    Doyle LA (2014) Sarcoma classification: an update based on the 2013 World Health Organization Classification of tumors of soft tissue and bone. Cancer 120:1763–1774. doi: 10.1002/cncr.28657 PubMedCrossRefGoogle Scholar
  5. 5.
    Fletcher CDM, World Health Organization, International Agency for Research on Cancer (2013) WHO classification of tumours of soft tissue and bone, 4th edn. IARC Press, LyonGoogle Scholar
  6. 6.
    Jo VY, Fletcher CDM (2014) WHO classification of soft tissue tumours: an update based on the 2013 (4th) edition. Pathology (Phila) 46:95–104. doi: 10.1097/PAT.0000000000000050 Google Scholar
  7. 7.
    The ESMO/European Sarcoma Network Working Group (2014) Bone sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 25:iii113–iii123. doi: 10.1093/annonc/mdu256 CrossRefGoogle Scholar
  8. 8.
    The ESMO/European Sarcoma Network Working Group (2014) Soft tissue and visceral sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 25:iii102–iii112. doi: 10.1093/annonc/mdu254 CrossRefGoogle Scholar
  9. 9.
    US Cancer Statistics Working Group (2016) United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. US Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute, AtlantaGoogle Scholar
  10. 10.
    Berquist TH, Ehman RL, King BF et al (1990) Value of MR imaging in differentiating benign from malignant soft-tissue masses: study of 95 lesions. Am J Roentgenol 155:1251–1255. doi: 10.2214/ajr.155.6.2122675 CrossRefGoogle Scholar
  11. 11.
    Choi YY, Kim JY, Yang S-O (2014) PET/CT in benign and malignant musculoskeletal tumors and tumor-like conditions. Semin Musculoskelet Radiol 18:133–148. doi: 10.1055/s-0034-1371016 PubMedCrossRefGoogle Scholar
  12. 12.
    Bielack SS, Kempf-Bielack B, Delling G et al (2002) Prognostic factors in high-grade osteosarcoma of the extremities or trunk: an analysis of 1702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol Off J Am Soc Clin Oncol 20:776–790. doi: 10.1200/jco.2002.20.3.776 CrossRefGoogle Scholar
  13. 13.
    Schnapauff D, Zeile M, Niederhagen MB et al (2009) Diffusion-weighted echo-planar magnetic resonance imaging for the assessment of tumor cellularity in patients with soft-tissue sarcomas. J Magn Reson Imaging JMRI 29:1355–1359. doi: 10.1002/jmri.21755 PubMedCrossRefGoogle Scholar
  14. 14.
    Chou S-HS, Hippe DS, Lee AY et al (2017) Gadolinium contrast enhancement improves confidence in diagnosing recurrent soft tissue sarcoma by MRI. Acad Radiol. doi: 10.1016/j.acra.2016.12.010 Google Scholar
  15. 15.
    Garner HW, Kransdorf MJ (2016) Musculoskeletal sarcoma: update on imaging of the post-treatment patient. Can Assoc Radiol J 67:12–20. doi: 10.1016/j.carj.2014.11.002 PubMedCrossRefGoogle Scholar
  16. 16.
    Subhawong TK, Wilky BA (2015) Value added: functional MR imaging in management of bone and soft tissue sarcomas. Curr Opin Oncol 27:323–331. doi: 10.1097/CCO.0000000000000199 PubMedCrossRefGoogle Scholar
  17. 17.
    Andersen KF, Fuglo HM, Rasmussen SH et al (2015) Volume-based F-18 FDG PET/CT imaging markers provide supplemental prognostic information to histologic grading in patients with high-grade bone or soft tissue sarcoma. Medicine (Baltimore) 94:e2319. doi: 10.1097/MD.0000000000002319 CrossRefGoogle Scholar
  18. 18.
    Bastiaannet E, Groen H, Jager PL et al (2004) The value of FDG-PET in the detection, grading and response to therapy of soft tissue and bone sarcomas; a systematic review and meta-analysis. Cancer Treat Rev 30:83–101. doi: 10.1016/j.ctrv.2003.07.004 PubMedCrossRefGoogle Scholar
  19. 19.
    Fuglø HM, Jørgensen SM, Loft A et al (2012) The diagnostic and prognostic value of 18F-FDG PET/CT in the initial assessment of high-grade bone and soft tissue sarcoma. A retrospective study of 89 patients. Eur J Nucl Med Mol Imaging 39:1416–1424. doi: 10.1007/s00259-012-2159-z PubMedCrossRefGoogle Scholar
  20. 20.
    Iagaru A, Masamed R, Chawla SP et al (2008) F-18 FDG PET and PET/CT evaluation of response to chemotherapy in bone and soft tissue sarcomas. Clin Nucl Med 33:8–13. doi: 10.1097/RLU.0b013e31815c4fd4 PubMedCrossRefGoogle Scholar
  21. 21.
    Kubo T, Furuta T, Johan MP (1990) Ochi M (2016) Prognostic significance of (18)F-FDG PET at diagnosis in patients with soft tissue sarcoma and bone sarcoma; systematic review and meta-analysis. Eur J Cancer Oxf Engl 58:104–111. doi: 10.1016/j.ejca.2016.02.007 CrossRefGoogle Scholar
  22. 22.
    Benz MR, Czernin J, Allen-Auerbach MS et al (2009) FDG-PET/CT imaging predicts histopathologic treatment responses after the initial cycle of neoadjuvant chemotherapy in high-grade soft-tissue sarcomas. Clin Cancer Res Off J Am Assoc Cancer Res 15:2856–2863. doi: 10.1158/1078-0432.CCR-08-2537 CrossRefGoogle Scholar
  23. 23.
    Piperkova E, Mikhaeil M, Mousavi A et al (2009) Impact of PET and CT in PET/CT studies for staging and evaluating treatment response in bone and soft tissue sarcomas. Clin Nucl Med 34:146–150. doi: 10.1097/RLU.0b013e3181966f9d PubMedCrossRefGoogle Scholar
  24. 24.
    Eary JF, O’Sullivan F, O’Sullivan J, Conrad EU (2008) Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J Nucl Med 49:1973–1979. doi: 10.2967/jnumed.108.053397 PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Byun BH, Kong C-B, Lim I et al (2013) Comparison of (18)F-FDG PET/CT and (99m)Tc-MDP bone scintigraphy for detection of bone metastasis in osteosarcoma. Skeletal Radiol 42:1673–1681. doi: 10.1007/s00256-013-1714-4 PubMedCrossRefGoogle Scholar
  26. 26.
    Franzius C, Sciuk J, Daldrup-Link HE et al (2000) FDG-PET for detection of osseous metastases from malignant primary bone tumours: comparison with bone scintigraphy. Eur J Nucl Med 27:1305–1311PubMedCrossRefGoogle Scholar
  27. 27.
    Györke T, Zajic T, Lange A et al (2006) Impact of FDG PET for staging of Ewing sarcomas and primitive neuroectodermal tumours. Nucl Med Commun 27:17–24PubMedCrossRefGoogle Scholar
  28. 28.
    Walter F, Czernin J, Hall T et al (2012) Is there a need for dedicated bone imaging in addition to 18F-FDG PET/CT imaging in pediatric sarcoma patients? J Pediatr Hematol Oncol 34:131–136. doi: 10.1097/MPH.0b013e3182282825 PubMedCrossRefGoogle Scholar
  29. 29.
    The National Comprehensive Cancer Network (2017) NCCN Clinical Practice Guidelines in OncologyGoogle Scholar
  30. 30.
    Maruzzo M, Rastrelli M, Lumachi F et al (2013) Adjuvant and neoadjuvant chemotherapy for soft tissue sarcomas. Curr Med Chem 20:613–620PubMedCrossRefGoogle Scholar
  31. 31.
    Maretty-Nielsen K, Aggerholm-Pedersen N, Safwat A et al (2014) Prognostic factors for local recurrence and mortality in adult soft tissue sarcoma of the extremities and trunk wall: a cohort study of 922 consecutive patients. Acta Orthop 85:323–332. doi: 10.3109/17453674.2014.908341 PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Rajendran JG, Wilson DC, Conrad EU et al (2003) [(18)F]FMISO and [(18)F]FDG PET imaging in soft tissue sarcomas: correlation of hypoxia, metabolism and VEGF expression. Eur J Nucl Med Mol Imaging 30:695–704. doi: 10.1007/s00259-002-1096-7 PubMedCrossRefGoogle Scholar
  33. 33.
    Benz MR, Czernin J, Allen-Auerbach MS et al (2012) 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography for response assessment in soft tissue sarcoma: a pilot study to correlate imaging findings with tissue thymidine kinase 1 and Ki-67 activity and histopathologic response. Cancer 118:3135–3144. doi: 10.1002/cncr.26630 PubMedCrossRefGoogle Scholar
  34. 34.
    Buchbender C, Heusner TA, Lauenstein TC et al (2012) Oncologic PET/MRI, Part 2: bone tumors, soft-tissue tumors, melanoma, and lymphoma. J Nucl Med 53:1244–1252. doi: 10.2967/jnumed.112.109306 PubMedCrossRefGoogle Scholar
  35. 35.
    Roberge D, Vakilian S, Alabed YZ et al (2012) FDG PET/CT in initial staging of adult soft-tissue sarcoma. Sarcoma 2012:960194. doi: 10.1155/2012/960194 PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Ferguson WS, Goorin AM (2001) Current treatment of osteosarcoma. Cancer Invest 19:292–315PubMedCrossRefGoogle Scholar
  37. 37.
    Jeffree GM, Price CH, Sissons HA (1975) The metastatic patterns of osteosarcoma. Br J Cancer 32:87–107PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Mariani L, Miceli R, Kattan MW et al (2005) Validation and adaptation of a nomogram for predicting the survival of patients with extremity soft tissue sarcoma using a three-grade system. Cancer 103:402–408. doi: 10.1002/cncr.20778 PubMedCrossRefGoogle Scholar
  39. 39.
    Ilaslan H, Schils J, Nageotte W et al (2010) Clinical presentation and imaging of bone and soft-tissue sarcomas. Cleve Clin J Med 77(Suppl 1):S2–S7. doi: 10.3949/ccjm.77.s1.01 PubMedCrossRefGoogle Scholar
  40. 40.
    Vanhoenacker FM, Van Looveren K, Trap K et al (2012) Grading and characterization of soft tissue tumors on magnetic resonance imaging: the value of an expert second opinion report. Insights Imaging 3:131–138. doi: 10.1007/s13244-012-0151-6 PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Gielen JLMA, De Schepper AM, Vanhoenacker F et al (2004) Accuracy of MRI in characterization of soft tissue tumors and tumor-like lesions. A prospective study in 548 patients. Eur Radiol 14:2320–2330. doi: 10.1007/s00330-004-2431-0 PubMedCrossRefGoogle Scholar
  42. 42.
    Fernebro J, Wiklund M, Jonsson K et al (2006) Focus on the tumour periphery in MRI evaluation of soft tissue sarcoma: infiltrative growth signifies poor prognosis. Sarcoma 2006:21251. doi: 10.1155/SRCM/2006/21251 PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Eary JF, Mankoff DA (1998) Tumor metabolic rates in sarcoma using FDG PET. J Nucl Med Off Publ Soc Nucl Med 39:250–254Google Scholar
  44. 44.
    Aoki J, Watanabe H, Shinozaki T et al (2001) FDG PET of primary benign and malignant bone tumors: standardized uptake value in 52 lesions. Radiology 219:774–777. doi: 10.1148/radiology.219.3.r01ma08774 PubMedCrossRefGoogle Scholar
  45. 45.
    Carneiro A, Bendahl P-O, Engellau J et al (2011) A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern. Cancer 117:1279–1287. doi: 10.1002/cncr.25621 PubMedCrossRefGoogle Scholar
  46. 46.
    Inwards CY, Unni KK (1995) Classification and grading of bone sarcomas. Hematol Oncol Clin North Am 9:545–569PubMedGoogle Scholar
  47. 47.
    Folpe AL, Lyles RH, Sprouse JT et al (2000) (F-18) fluorodeoxyglucose positron emission tomography as a predictor of pathologic grade and other prognostic variables in bone and soft tissue sarcoma. Clin Cancer Res Off J Am Assoc Cancer Res 6:1279–1287Google Scholar
  48. 48.
    Eary JF, Conrad EU, Bruckner JD et al (1998) Quantitative [F-18]fluorodeoxyglucose positron emission tomography in pretreatment and grading of sarcoma. Clin Cancer Res Off J Am Assoc Cancer Res 4:1215–1220Google Scholar
  49. 49.
    Ioannidis JPA, Lau J (2003) 18F-FDG PET for the diagnosis and grading of soft-tissue sarcoma: a meta-analysis. J Nucl Med Off Publ Soc Nucl Med 44:717–724Google Scholar
  50. 50.
    Brenner W, Conrad EU, Eary JF (2004) FDG PET imaging for grading and prediction of outcome in chondrosarcoma patients. Eur J Nucl Med Mol Imaging 31:189–195. doi: 10.1007/s00259-003-1353-4 PubMedCrossRefGoogle Scholar
  51. 51.
    Geirnaerdt MJA, Hogendoorn PCW, Bloem JL et al (2000) Cartilaginous tumors: fast contrast-enhanced MR imaging. Radiology 214:539–546. doi: 10.1148/radiology.214.2.r00fe12539 PubMedCrossRefGoogle Scholar
  52. 52.
    Nakamura T, Matsumine A, Niimi R et al (2009) Management of small pulmonary nodules in patients with sarcoma. Clin Exp Metastasis 26:713–718. doi: 10.1007/s10585-009-9270-y PubMedCrossRefGoogle Scholar
  53. 53.
    Flavell RR, Behr SC, Mabray MC et al (2016) Detecting pulmonary nodules in lung cancer patients using whole body FDG PET/CT, high-resolution lung reformat of FDG PET/CT, or diagnostic breath hold chest CT. Acad Radiol 23:1123–1129. doi: 10.1016/j.acra.2016.04.007 PubMedCrossRefGoogle Scholar
  54. 54.
    Huang T-L, Liu R-S, Chen T-H et al (2006) Comparison between F-18-FDG positron emission tomography and histology for the assessment of tumor necrosis rates in primary osteosarcoma. J Chin Med Assoc JCMA 69:372–376. doi: 10.1016/S1726-4901(09)70275-8 PubMedCrossRefGoogle Scholar
  55. 55.
    Shapeero LG, Vanel D (2000) Imaging evaluation of the response of high-grade osteosarcoma and Ewing sarcoma to chemotherapy with emphasis on dynamic contrast-enhanced magnetic resonance imaging. Semin Musculoskelet Radiol 4:137–146PubMedCrossRefGoogle Scholar
  56. 56.
    Skougaard K, Nielsen D, Jensen BV, Hendel HW (2013) Comparison of EORTC criteria and PERCIST for PET/CT response evaluation of patients with metastatic colorectal cancer treated with irinotecan and cetuximab. J Nucl Med Off Publ Soc Nucl Med 54:1026–1031. doi: 10.2967/jnumed.112.111757 Google Scholar
  57. 57.
    Whelan JS, Bielack SS, Marina N et al (2015) EURAMOS-1, an international randomised study for osteosarcoma: results from pre-randomisation treatment. Ann Oncol Off J Eur Soc Med Oncol 26:407–414. doi: 10.1093/annonc/mdu526 CrossRefGoogle Scholar
  58. 58.
    Winkler K, Bielack SS, Delling G et al (1993) Treatment of osteosarcoma: experience of the Cooperative Osteosarcoma Study Group (COSS). Cancer Treat Res 62:269–277PubMedCrossRefGoogle Scholar
  59. 59.
    Raymond AK, Chawla SP, Carrasco CH et al (1987) Osteosarcoma chemotherapy effect: a prognostic factor. Semin Diagn Pathol 4:212–236PubMedGoogle Scholar
  60. 60.
    Kong C-B, Byun BH, Lim I et al (2013) 18F-FDG PET SUVmax as an indicator of histopathologic response after neoadjuvant chemotherapy in extremity osteosarcoma. Eur J Nucl Med Mol Imaging 40:728–736. doi: 10.1007/s00259-013-2344-8 PubMedCrossRefGoogle Scholar
  61. 61.
    Bajpai J, Kumar R, Sreenivas V et al (2011) Prediction of chemotherapy response by PET-CT in osteosarcoma: correlation with histologic necrosis. J Pediatr Hematol Oncol 33:e271–e278. doi: 10.1097/MPH.0b013e31820ff78e PubMedGoogle Scholar
  62. 62.
    Cheon GJ, Kim MS, Lee JA et al (2009) Prediction model of chemotherapy response in osteosarcoma by 18F-FDG PET and MRI. J Nucl Med Off Publ Soc Nucl Med 50:1435–1440. doi: 10.2967/jnumed.109.063602 Google Scholar
  63. 63.
    Hawkins DS, Rajendran JG, Conrad EU et al (2002) Evaluation of chemotherapy response in pediatric bone sarcomas by [F-18]-fluorodeoxy-d-glucose positron emission tomography. Cancer 94:3277–3284. doi: 10.1002/cncr.10599 PubMedCrossRefGoogle Scholar
  64. 64.
    Picci P, Böhling T, Bacci G et al (1997) Chemotherapy-induced tumor necrosis as a prognostic factor in localized Ewing’s sarcoma of the extremities. J Clin Oncol Off J Am Soc Clin Oncol 15:1553–1559. doi: 10.1200/jco.1997.15.4.1553 CrossRefGoogle Scholar
  65. 65.
    Albergo JI, Gaston CL, Laitinen M et al (2016) Ewing’s sarcoma: only patients with 100% of necrosis after chemotherapy should be classified as having a good response. Bone Jt J 98-B:1138–1144. doi: 10.1302/0301-620X.98B8.37346 CrossRefGoogle Scholar
  66. 66.
    Raciborska A, Bilska K, Drabko K et al (2016) Response to chemotherapy estimates by FDG PET is an important prognostic factor in patients with Ewing sarcoma. Clin Transl Oncol 18:189–195. doi: 10.1007/s12094-015-1351-6 PubMedCrossRefGoogle Scholar
  67. 67.
    Dantonello TM, Int-Veen C, Leuschner I et al (2008) Mesenchymal chondrosarcoma of soft tissues and bone in children, adolescents, and young adults: experiences of the CWS and COSS study groups. Cancer 112:2424–2431. doi: 10.1002/cncr.23457 PubMedCrossRefGoogle Scholar
  68. 68.
    Stacchiotti S, Pantaleo MA, Astolfi A et al (2014) Activity of sunitinib in extraskeletal myxoid chondrosarcoma. Eur J Cancer Oxf Engl 1990 50:1657–1664. doi: 10.1016/j.ejca.2014.03.013 Google Scholar
  69. 69.
    Villert A, Kolomiets L, Vasilyev N et al (2015) Extraskeletal myxoid chondrosarcoma of the vulva: a case report. Oncol Lett 10:2095–2099. doi: 10.3892/ol.2015.3586 PubMedPubMedCentralGoogle Scholar
  70. 70.
    Zaki M, Laszewski P, Robinette N et al (2015) Unresectable extraskeletal myxoid chondrosarcoma of the neck: early tumor response to chemoradiotherapy. Cureus 7:e432. doi: 10.7759/cureus.432 PubMedPubMedCentralGoogle Scholar
  71. 71.
    Vaynrub M, Taheri N, Ahlmann ER et al (2015) Prognostic value of necrosis after neoadjuvant therapy for soft tissue sarcoma. J Surg Oncol 111:152–157. doi: 10.1002/jso.23775 PubMedCrossRefGoogle Scholar
  72. 72.
    Eary JF, O’Sullivan F, Powitan Y et al (2002) Sarcoma tumor FDG uptake measured by PET and patient outcome: a retrospective analysis. Eur J Nucl Med Mol Imaging 29:1149–1154. doi: 10.1007/s00259-002-0859-5 PubMedCrossRefGoogle Scholar
  73. 73.
    Costelloe CM, Macapinlac HA, Madewell JE et al (2009) 18F-FDG PET/CT as an indicator of progression-free and overall survival in osteosarcoma. J Nucl Med Off Publ Soc Nucl Med 50:340–347. doi: 10.2967/jnumed.108.058461 Google Scholar
  74. 74.
    Skamene SR, Rakheja R, Dahlstrom KR et al (2014) Metabolic activity measured on PET/CT correlates with clinical outcomes in patients with limb and girdle sarcomas. J Surg Oncol 109:410–414. doi: 10.1002/jso.23523 PubMedCrossRefGoogle Scholar
  75. 75.
    Schwarzbach MHM, Hinz U, Dimitrakopoulou-Strauss A et al (2005) Prognostic significance of preoperative [18-F] fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging in patients with resectable soft tissue sarcomas. Ann Surg 241:286–294PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Heppner GH, Miller BE (1983) Tumor heterogeneity: biological implications and therapeutic consequences. Cancer Metastasis Rev 2:5–23. doi: 10.1007/BF00046903 PubMedCrossRefGoogle Scholar
  77. 77.
    Longo DL (2012) Tumor heterogeneity and personalized medicine. N Engl J Med 366:956–957. doi: 10.1056/NEJMe1200656 PubMedCrossRefGoogle Scholar
  78. 78.
    Franzetti G-A, Laud-Duval K, van der Ent W et al (2017) Cell-to-cell heterogeneity of EWSR1-FLI1 activity determines proliferation/migration choices in Ewing sarcoma cells. Oncogene. doi: 10.1038/onc.2016.498 PubMedGoogle Scholar
  79. 79.
    Jemaà M, Abdallah S, Lledo G et al (2016) Heterogeneity in sarcoma cell lines reveals enhanced motility of tetraploid versus diploid cells. Oncotarget. doi: 10.18632/oncotarget.14291 PubMedPubMedCentralGoogle Scholar
  80. 80.
    Skubitz KM, D’Adamo DR (2007) Sarcoma. Mayo Clin Proc 82:1409–1432. doi: 10.4065/82.11.1409 PubMedCrossRefGoogle Scholar
  81. 81.
    Helman LJ, Meltzer P (2003) Mechanisms of sarcoma development. Nat Rev Cancer 3:685–694. doi: 10.1038/nrc1168 PubMedCrossRefGoogle Scholar
  82. 82.
    O’Sullivan M, Budhraja V, Sadovsky Y, Pfeifer JD (2005) Tumor heterogeneity affects the precision of microarray analysis. Diagn Mol Pathol Am J Surg Pathol Part B 14:65–71CrossRefGoogle Scholar
  83. 83.
    Tellez-Gabriel M, Ory B, Lamoureux F et al (2016) Tumour heterogeneity: the key advantages of single-cell analysis. Int J Mol Sci. doi: 10.3390/ijms17122142 PubMedPubMedCentralGoogle Scholar
  84. 84.
    Singer AD, Pattany PM, Fayad LM et al (2016) Volumetric segmentation of ADC maps and utility of standard deviation as measure of tumor heterogeneity in soft tissue tumors. Clin Imaging 40:386–391. doi: 10.1016/j.clinimag.2015.11.017 PubMedCrossRefGoogle Scholar
  85. 85.
    Allen SD, Moskovic EC, Fisher C, Thomas JM (2007) Adult rhabdomyosarcoma: cross-sectional imaging findings including histopathologic correlation. AJR Am J Roentgenol 189:371–377. doi: 10.2214/AJR.07.2065 PubMedCrossRefGoogle Scholar
  86. 86.
    O’Sullivan F (2003) A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics 4:433–448. doi: 10.1093/biostatistics/4.3.433 PubMedCrossRefGoogle Scholar
  87. 87.
    O’Sullivan F, Roy S, O’Sullivan J et al (2005) Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostat Oxf Engl 6:293–301. doi: 10.1093/biostatistics/kxi010 CrossRefGoogle Scholar
  88. 88.
    Semenza GL (2007) Hypoxia-inducible factor 1 (HIF-1) pathway. Sci STKE Signal Transduct Knowl Environ. doi: 10.1126/stke.4072007cm8 Google Scholar
  89. 89.
    Rasey JS, Koh W, Evans ML et al (1996) Quantifying regional hypoxia in human tumors with positron emission tomography of [18F]fluoromisonidazole: a pretherapy study of 37 patients. Int J Radiat Oncol 36:417–428. doi: 10.1016/S0360-3016(96)00325-2 CrossRefGoogle Scholar
  90. 90.
    Yang DJ, Ilgan S, Higuchi T et al (1999) Noninvasive assessment of tumor hypoxia with 99mTc labeled metronidazole. Pharm Res 16:743–750PubMedCrossRefGoogle Scholar
  91. 91.
    Koh W-J, Rasey JS, Evans ML et al (1992) Imaging of hypoxia in human tumors with [F-18]fluoromisonidazole. Int J Radiat Oncol 22:199–212. doi: 10.1016/0360-3016(92)91001-4 CrossRefGoogle Scholar
  92. 92.
    Vaupel P, Mayer A (2007) Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev 26:225–239. doi: 10.1007/s10555-007-9055-1 PubMedCrossRefGoogle Scholar
  93. 93.
    Eschmann SM, Paulsen F, Bedeshem C et al (2007) Hypoxia-imaging with (18)F-Misonidazole and PET: changes of kinetics during radiotherapy of head-and-neck cancer. Radiother Oncol J Eur Soc Ther Radiol Oncol 83:406–410. doi: 10.1016/j.radonc.2007.05.014 CrossRefGoogle Scholar
  94. 94.
    Lee NY, Mechalakos JG, Nehmeh S et al (2008) Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: a feasibility study. Int J Radiat Oncol Biol Phys 70:2–13. doi: 10.1016/j.ijrobp.2007.06.039 PubMedCrossRefGoogle Scholar
  95. 95.
    Francis P, Namløs H, Müller C et al (2007) Diagnostic and prognostic gene expression signatures in 177 soft tissue sarcomas: hypoxia-induced transcription profile signifies metastatic potential. BMC Genom 8:73. doi: 10.1186/1471-2164-8-73 CrossRefGoogle Scholar
  96. 96.
    Nordsmark M, Alsner J, Keller J et al (2001) Hypoxia in human soft tissue sarcomas: adverse impact on survival and no association with p53 mutations. Br J Cancer 84:1070–1075. doi: 10.1054/bjoc.2001.1728 PubMedPubMedCentralCrossRefGoogle Scholar
  97. 97.
    Evans SM, Fraker D, Hahn SM et al (2006) EF5 binding and clinical outcome in human soft tissue sarcomas. Int J Radiat Oncol Biol Phys 64:922–927. doi: 10.1016/j.ijrobp.2005.05.068 PubMedCrossRefGoogle Scholar
  98. 98.
    Brizel DM, Scully SP, Harrelson JM et al (1996) Tumor oxygenation predicts for the likelihood of distant metastases in human soft tissue sarcoma. Cancer Res 56:941–943PubMedGoogle Scholar
  99. 99.
    Gray LH, Conger AD, Ebert M et al (1953) The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 26:638–648. doi: 10.1259/0007-1285-26-312-638 PubMedCrossRefGoogle Scholar
  100. 100.
    Mortensen LS, Johansen J, Kallehauge J et al (2012) FAZA PET/CT hypoxia imaging in patients with squamous cell carcinoma of the head and neck treated with radiotherapy: results from the DAHANCA 24 trial. Radiother Oncol 105:14–20. doi: 10.1016/j.radonc.2012.09.015 PubMedCrossRefGoogle Scholar
  101. 101.
    Vergis R, Corbishley CM, Norman AR et al (2008) Intrinsic markers of tumour hypoxia and angiogenesis in localised prostate cancer and outcome of radical treatment: a retrospective analysis of two randomised radiotherapy trials and one surgical cohort study. Lancet Oncol 9:342–351. doi: 10.1016/S1470-2045(08)70076-7 PubMedCrossRefGoogle Scholar
  102. 102.
    Kurihara H, Honda N, Kono Y, Arai Y (2012) Radiolabelled agents for PET imaging of tumor hypoxia. Curr Med Chem 19:3282–3289PubMedCrossRefGoogle Scholar
  103. 103.
    Krohn KA, Link JM, Mason RP (2008) Molecular imaging of hypoxia. J Nucl Med Off Publ Soc Nucl Med 49(Suppl 2):129S–148S. doi: 10.2967/jnumed.107.045914 Google Scholar
  104. 104.
    Chapman JD, Baer K, Lee J (1983) Characteristics of the metabolism-induced binding of misonidazole to hypoxic mammalian cells. Cancer Res 43:1523–1528PubMedGoogle Scholar
  105. 105.
    Liu J, Hajibeigi A, Ren G et al (2009) Retention of the radiotracers 64Cu-ATSM and 64Cu-PTSM in human and murine tumors is influenced by MDR1 protein expression. J Nucl Med Off Publ Soc Nucl Med 50:1332–1339. doi: 10.2967/jnumed.109.061879 Google Scholar
  106. 106.
    Beck R, Röper B, Carlsen JM et al (2007) Pretreatment 18F-FAZA PET predicts success of hypoxia-directed radiochemotherapy using tirapazamine. J Nucl Med Off Publ Soc Nucl Med 48:973–980. doi: 10.2967/jnumed.106.038570 Google Scholar
  107. 107.
    Sørensen M, Horsman MR, Cumming P et al (2005) Effect of intratumoral heterogeneity in oxygenation status on FMISO PET, autoradiography, and electrode Po2 measurements in murine tumors. Int J Radiat Oncol 62:854–861. doi: 10.1016/j.ijrobp.2005.02.044 CrossRefGoogle Scholar
  108. 108.
    Busk M, Horsman MR, Jakobsen S et al (2008) Imaging hypoxia in xenografted and murine tumors with 18F-fluoroazomycin arabinoside: a comparative study involving microPET, autoradiography, PO2-polarography, and fluorescence microscopy. Int J Radiat Oncol Biol Phys 70:1202–1212. doi: 10.1016/j.ijrobp.2007.11.034 PubMedCrossRefGoogle Scholar
  109. 109.
    Lewin J, Khamly KK, Young RJ et al (2014) A phase Ib/II translational study of sunitinib with neoadjuvant radiotherapy in soft-tissue sarcoma. Br J Cancer 111:2254–2261. doi: 10.1038/bjc.2014.537 PubMedPubMedCentralCrossRefGoogle Scholar
  110. 110.
    Mammar H, Kerrou K, Nataf V et al (2012) Positron emission tomography/computed tomography imaging of residual skull base chordoma before radiotherapy using fluoromisonidazole and fluorodeoxyglucose: potential consequences for dose painting. Int J Radiat Oncol Biol Phys 84:681–687. doi: 10.1016/j.ijrobp.2011.12.047 PubMedCrossRefGoogle Scholar
  111. 111.
    Coindre JM, Terrier P, Guillou L et al (2001) 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 91:1914–1926PubMedCrossRefGoogle Scholar
  112. 112.
    Buerkle A, Weber WA (2008) Imaging of tumor glucose utilization with positron emission tomography. Cancer Metastasis Rev 27:545–554. doi: 10.1007/s10555-008-9151-x PubMedCrossRefGoogle Scholar
  113. 113.
    Bading JR, Shields AF (2008) Imaging of cell proliferation: status and prospects. J Nucl Med Off Publ Soc Nucl Med 49(Suppl 2):64S–80S. doi: 10.2967/jnumed.107.046391 Google Scholar
  114. 114.
    Paproski RJ, Ng AML, Yao SYM et al (2008) The role of human nucleoside transporters in uptake of 3′-deoxy-3′-fluorothymidine. Mol Pharmacol 74:1372–1380. doi: 10.1124/mol.108.048900 PubMedCrossRefGoogle Scholar
  115. 115.
    Sala R, Nguyen Q-D, Patel CBK et al (2014) Phosphorylation status of thymidine kinase 1 following antiproliferative drug treatment mediates 3′-deoxy-3′-[18F]-fluorothymidine cellular retention. PLoS One 9:e101366. doi: 10.1371/journal.pone.0101366 PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Yang W, Zhang Y, Fu Z et al (2010) Imaging of proliferation with 18F-FLT PET/CT versus 18F-FDG PET/CT in non-small-cell lung cancer. Eur J Nucl Med Mol Imaging 37:1291–1299. doi: 10.1007/s00259-010-1412-6 PubMedCrossRefGoogle Scholar
  117. 117.
    Buck AK, Halter G, Schirrmeister H et al (2003) Imaging proliferation in lung tumors with PET: 18F-FLT versus 18F-FDG. J Nucl Med Off Publ Soc Nucl Med 44:1426–1431Google Scholar
  118. 118.
    Francis DL, Visvikis D, Costa DC et al (2003) Potential impact of [18F]3′-deoxy-3′-fluorothymidine versus [18F]fluoro-2-deoxy-d-glucose in positron emission tomography for colorectal cancer. Eur J Nucl Med Mol Imaging 30:988–994. doi: 10.1007/s00259-003-1187-0 PubMedCrossRefGoogle Scholar
  119. 119.
    Buck AK, Herrmann K, Büschenfelde CMZ et al (2008) Imaging bone and soft tissue tumors with the proliferation marker [18F]fluorodeoxythymidine. Clin Cancer Res Off J Am Assoc Cancer Res 14:2970–2977. doi: 10.1158/1078-0432.CCR-07-4294 CrossRefGoogle Scholar
  120. 120.
    Barwick T, Bencherif B, Mountz JM, Avril N (2009) Molecular PET and PET/CT imaging of tumour cell proliferation using F-18 fluoro-l-thymidine: a comprehensive evaluation. Nucl Med Commun 30:908–917. doi: 10.1097/MNM.0b013e32832ee93b PubMedCrossRefGoogle Scholar
  121. 121.
    Thoeny HC, De Keyzer F, Vandecaveye V et al (2005) Effect of vascular targeting agent in rat tumor model: dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology 237:492–499. doi: 10.1148/radiol.2372041638 PubMedCrossRefGoogle Scholar
  122. 122.
    Weber WA (2010) Monitoring tumor response to therapy with 18F-FLT PET. J Nucl Med Off Publ Soc Nucl Med 51:841–844. doi: 10.2967/jnumed.109.071217 Google Scholar
  123. 123.
    Tran L-B-A, Bol A, Labar D et al (2016) DW-MRI and (18) F-FLT PET for early assessment of response to radiation therapy associated with hypoxia-driven interventions. Preclinical studies using manipulation of oxygenation and/or dose escalation. Contrast Media Mol Imaging 11:115–121. doi: 10.1002/cmmi.1670 PubMedCrossRefGoogle Scholar
  124. 124.
    Li Z, Herrmann K, Pirsig S et al (2013) Molecular imaging for early prediction of response to Sorafenib treatment in sarcoma. Am J Nucl Med Mol Imaging 4:70–79PubMedPubMedCentralGoogle Scholar
  125. 125.
    Leyton J, Latigo JR, Perumal M et al (2005) Early detection of tumor response to chemotherapy by 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography: the effect of cisplatin on a fibrosarcoma tumor model in vivo. Cancer Res 65:4202–4210. doi: 10.1158/0008-5472.CAN-04-4008 PubMedCrossRefGoogle Scholar
  126. 126.
    Sohn H-J, Yang Y-J, Ryu J-S et al (2008) [18F]Fluorothymidine positron emission tomography before and 7 days after gefitinib treatment predicts response in patients with advanced adenocarcinoma of the lung. Clin Cancer Res Off J Am Assoc Cancer Res 14:7423–7429. doi: 10.1158/1078-0432.CCR-08-0312 CrossRefGoogle Scholar
  127. 127.
    Eary JF, Link JM, Muzi M et al (2011) Multiagent PET for risk characterization in sarcoma. J Nucl Med Off Publ Soc Nucl Med 52:541–546. doi: 10.2967/jnumed.110.083717 Google Scholar
  128. 128.
    Yang Y, Cao M, Kamrava M et al (2016) WE-FG-202-11: longitudinal diffusion MRI for treatment assessment of sarcoma patients with pre-operative radiation therapy. Med Phys 43:3829. doi: 10.1118/1.4957923 CrossRefGoogle Scholar
  129. 129.
    Huang W, Beckett BR, Tudorica A et al (2016) Evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy using dynamic contrast-enhanced magnetic resonance imaging. Tomogr J Imaging Res 2:308–316. doi: 10.18383/j.tom.2016.00202 Google Scholar
  130. 130.
    Uhl M, Saueressig U, van Buiren M et al (2006) Osteosarcoma: preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion- and perfusion-weighted magnetic resonance imaging. Invest Radiol 41:618–623. doi: 10.1097/01.rli.0000225398.17315.68 PubMedCrossRefGoogle Scholar
  131. 131.
    Raylman RR, Majewski S, Velan SS et al (2007) Simultaneous acquisition of magnetic resonance spectroscopy (MRS) data and positron emission tomography (PET) images with a prototype MR-compatible, small animal PET imager. J Magn Reson San Diego Calif 1997 186:305–310. doi: 10.1016/j.jmr.2007.03.012 Google Scholar
  132. 132.
    Catalano OA, Rosen BR, Sahani DV et al (2013) Clinical impact of PET/MR imaging in patients with cancer undergoing same-day PET/CT: initial experience in 134 patients—a Hypothesis-generating Exploratory Study. Radiology 269:857–869. doi: 10.1148/radiol.13131306 PubMedCrossRefGoogle Scholar
  133. 133.
    Benz MR, Czernin J, Tap WD et al (2010) FDG-PET/CT imaging predicts histopathologic treatment responses after neoadjuvant therapy in adult primary bone sarcomas. Sarcoma 2010:1–7. doi: 10.1155/2010/143540 CrossRefGoogle Scholar

Copyright information

© Italian Association of Nuclear Medicine and Molecular Imaging 2017

Authors and Affiliations

  1. 1.Department of Radiology, Perelman School of MedicineHospital of the University of PennsylvaniaPhiladelphiaUSA

Personalised recommendations