Advertisement

European Radiology

, Volume 22, Issue 8, pp 1687–1692 | Cite as

Assessment of the metabolic flow phenotype of primary colorectal cancer: correlations with microvessel density are influenced by the histological scoring method

  • Vicky GohEmail author
  • Manuel Rodriguez-Justo
  • Alec Engledow
  • Manu Shastry
  • Raymondo Endozo
  • Jacqui Peck
  • Marie Meagher
  • Stuart A. Taylor
  • Steve Halligan
  • Ashley M. Groves
Oncology

Abstract

Objectives

To investigate how the histological scoring of microvessel density affects correlations between integrated 18F-FDG-PET/perfusion CT parameters and CD105 microvessel density.

Methods

A total of 53 patients were enrolled from 2007 to 2010. Integrated 18F-FDG-PET/perfusion CT was successful in 45 patients, 35 of whom underwent surgery without intervening treatment. Tumour SUVmax, SUVmean and regional blood flow (BF) were derived. Immunohistochemical staining for CD105 expression and analysis were performed for two hot spots, four hot spots and the Chalkley method. Correlations between metabolic flow parameters and CD105 expression were assessed using Spearman’s rank correlation.

Results

Mean (SD) for tumour size was 38.5 (20.5) mm, for SUVmax, SUVmean and BF it was 19.1 (4.5), 11.6 (2.5) and 85.4 (40.3) mL/min/100 g tissue, and for CD105 microvessel density it was 71.4 (23.6), 66.8 (22.9) and 6.18 (2.07) for two hot spots, four hot spots and the Chalkley method, respectively. Positive correlation between BF and CD105 expression was modest but higher for Chalkley than for four hot spots analysis (r = 0.38, P = 0.03; r = 0.33, P = 0.05, respectively). There were no significant correlations between metabolic parameters (SUVmax or SUVmean) and CD105 expression (r = 0.08–0.22, P = 0.21–0.63).

Conclusions

The histological analysis method affects correlations between tumour CD105 expression and BF but not SUVmax or SUVmean.

Key Points

• FDG-PET/perfusion CT offers new surrogate biomarkers of angiogenesis.

• Microvessel density scoring influences histopathological correlations with CT blood flow.

• Highest correlations were found with the Chalkley analysis method.

• Correlations between SUV and CD105 are not affected by the scoring method.

Keywords

FDG-PET/CT Perfusion CT Colon Rectum Tumour 

References

  1. 1.
    Hurwitz H, Fehrenbacher L, Novotny W et al (2004) Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 350:2335–2342PubMedCrossRefGoogle Scholar
  2. 2.
    Peeters M, Price TJ, Cervantes A et al (2010) Randomized phase III study of panitumumab with fluorouracil, leucovorin, and irinotecan (FOLFIRI) compared with FOLFIRI alone as second line treatment in patients with metastatic colorectal cancer. J Clin Oncol 28:4706–4713PubMedCrossRefGoogle Scholar
  3. 3.
    Van Cutsem E, Köhne CH, Hitre E et al (2009) Cetuximab and chemotherapy as first line treatment for metastatic colorectal cancer. N Engl J Med 360:1408–1417PubMedCrossRefGoogle Scholar
  4. 4.
    Jonker DJ, O'Callaghan CJ, Karapetis CS et al (2007) Cetuximab for the treatment of colorectal cancer. N Engl J Med 357:2040–2048PubMedCrossRefGoogle Scholar
  5. 5.
    Kaira K, Okumura T, Ohde Y et al (2011) Correlation between 18F-FDG uptake on PET and molecular biology in metastatic pulmonary tumors. J Nucl Med 52:705–711PubMedCrossRefGoogle Scholar
  6. 6.
    Kaira K, Endo M, Abe M et al (2011) Biologic correlates of 18F-FDG uptake on PET in pulmonary pleomorphic carcinoma. Lung Cancer 71:144–150PubMedCrossRefGoogle Scholar
  7. 7.
    Kaira K, Endo M, Abe M et al (2010) Biologic correlation of 2-[18F]-fluoro-2-deoxy-d-glucose uptake on positron emission tomography in thymic epithelial tumors. J Clin Oncol 28:3746–3753PubMedCrossRefGoogle Scholar
  8. 8.
    Groves AM, Shastry M, Rodriguez-Justo M et al (2011) 18F-FDG PET and biomarkers for tumour angiogenesis in early breast cancer. Eur J Nucl Med Mol Imaging 38:46–52PubMedCrossRefGoogle Scholar
  9. 9.
    Li Y, Yang ZG, Chen TW et al (2008) Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer 61:44–53PubMedCrossRefGoogle Scholar
  10. 10.
    Goh V, Halligan S, Daley F et al (2008) Colorectal tumor vascularity: quantitative assessment with multidetector CT—do tumor perfusion measurements reflect angiogenesis? Radiology 249:510–517PubMedCrossRefGoogle Scholar
  11. 11.
    Chen Y, Zhang J, Dai J, Feng X, Lu H, Zhou C (2010) Angiogenesis of renal cell carcinoma: perfusion CT findings. Abdom Imaging 35:622–628PubMedCrossRefGoogle Scholar
  12. 12.
    Yang HF, Du Y, Ni JX et al (2010) Perfusion computed tomography evaluation of angiogenesis in liver cancer. Eur Radiol 20:1424–1430PubMedCrossRefGoogle Scholar
  13. 13.
    d'Assignies G, Couvelard A, Bahrami S et al (2009) Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors. Radiology 250:407–416PubMedCrossRefGoogle Scholar
  14. 14.
    Yao J, Yang ZG, Chen HJ, Chen TW, Huang J (2011) Gastric adenocarcinoma: can perfusion CT help to noninvasively evaluate tumor angiogenesis? Abdom Imaging 36:15–21PubMedCrossRefGoogle Scholar
  15. 15.
    Hansen S, Sorensen FB, Vach W et al (2003) Microvessel density compared with the Chalkley count in a prognostic study of angiogenesis in breast cancer patients. Histopathology 44:428–436CrossRefGoogle Scholar
  16. 16.
    Offersen BV, Borre M, Overgaard J (2003) Quantification of angiogenesis as a prognostic marker in human carcinomas: a critical evaluation of histopathological methods for estimation of vascular density. Eur J Cancer 39:881–890PubMedCrossRefGoogle Scholar
  17. 17.
    Weidner N (1995) Intratumour microvessel density as a prognostic factor in cancer. Am J Pathol 147:9–19PubMedGoogle Scholar
  18. 18.
    Bettencourt M-C, Bauer JJ, Sesterhenn IA et al (1998) CD34 immunohistochemical assessment of angiogenesis as a prognostic marker for prostatic cancer recurrence after radical prostatectomy. J Urol 160:459–465PubMedCrossRefGoogle Scholar
  19. 19.
    Dales JP, Garcia S, Andrac L et al (2004) Prognostic significance of angiogenesis evaluated by CD105 expression compared to CD31 in 905 breast carcinomas: correlation with long-term patient outcome. Int J Oncol 24:1197–1204PubMedGoogle Scholar
  20. 20.
    Tanaka F, Otake Y, Yanagihara K et al (2001) Evaluation of angiogenesis in non-small cell lung cancer: comparison between anti-CD34 antibody and Anti-CD105 antibody. Clin Cancer Res 7:3410–15PubMedGoogle Scholar
  21. 21.
    Middleton J, Americh L, Gayon R et al (2005) A comparative study of endothelial cell markers expressed in chronically inflamed human tissues: ECA-79, Duffy antigen receptor for chemokines, von Willebrand factor, CD31, CD34, CD105 and CD146. J Pathol 206:260–268PubMedCrossRefGoogle Scholar
  22. 22.
    Beliën JA, Somi S, de Jong JS et al (1999) Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer. J Clin Pathol 52:184–192PubMedCrossRefGoogle Scholar
  23. 23.
    Fox SB, Harris AL (2004) Histological quantitation of tumour angiogenesis. APMIS 112:413–430PubMedCrossRefGoogle Scholar
  24. 24.
    Hansen S, Grabau DA, Sørensen FB et al (2000) The prognostic value of angiogenesis by Chalkley counting in a confirmatory study design on 836 breast cancer patients. Clin Cancer Res 6:139–146PubMedGoogle Scholar
  25. 25.
    Fox SB, Leek RD, Weekes MP et al (1995) Quantification and prognostic value of breast cancer angiogenesis: comparison of microvessel density, Chalkley count and computer image analysis. J Pathol 177:275–283PubMedCrossRefGoogle Scholar
  26. 26.
    Vermeulen PB, Gasparini G, Fox SB et al (2002) Second international consensus on the methodology and criteria of evaluation of angiogenesis quantification in solid human tumours. Eur J Cancer 38:1564–1579PubMedCrossRefGoogle Scholar
  27. 27.
    Des Guetz G, Uzzan B, Nicolas P et al (2006) Microvessel density and VEGF expression are prognostic factors in colorectal cancer. meta-analysis of the literature. Br J Cancer 94:1823–1832PubMedCrossRefGoogle Scholar
  28. 28.
    Li ZP, Meng QF, Sun CH et al (2005) Tumor angiogenesis and dynamic CT in colorectal carcinoma: radiologic-pathologic correlation. World J Gastroenterol 11:1287–1291PubMedGoogle Scholar
  29. 29.
    Feng ST, Sun CH, Li ZP et al (2010) Evaluation of angiogenesis in colorectal carcinoma with multidetector-row CT multislice perfusion imaging. Eur J Radiol 75:191–196PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2012

Authors and Affiliations

  • Vicky Goh
    • 1
    Email author
  • Manuel Rodriguez-Justo
    • 2
  • Alec Engledow
    • 3
  • Manu Shastry
    • 4
  • Raymondo Endozo
    • 4
  • Jacqui Peck
    • 3
  • Marie Meagher
    • 4
  • Stuart A. Taylor
    • 5
  • Steve Halligan
    • 5
  • Ashley M. Groves
    • 4
  1. 1.Division of Imaging Sciences & Biomedical EngineeringKing’s College LondonLondonUK
  2. 2.Department of HistopathologyUniversity College HospitalLondonUK
  3. 3.Department of SurgeryUniversity College HospitalLondonUK
  4. 4.Institute of Nuclear MedicineUniversity College HospitalLondonUK
  5. 5.Specialist RadiologyUniversity College HospitalLondonUK

Personalised recommendations