OBJECTIVES: To investigate how the histological scoring of microvessel density affects correlations between integrated (18)F-FDG-PET/perfusion CT parameters and CD105 microvessel density. METHODS: A total of 53 patients were enrolled from 2007 to 2010. Integrated (18)F-FDG-PET/perfusion CT was successful in 45 patients, 35 of whom underwent surgery without intervening treatment. Tumour SUV(max), SUV(mean) 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 SUV(max), SUV(mean) 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 (SUV(max) or SUV(mean)) 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 SUV(max) or SUV(mean). 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.
OBJECTIVES: To investigate how the histological scoring of microvessel density affects correlations between integrated (18)F-FDG-PET/perfusion CT parameters and CD105 microvessel density. METHODS: A total of 53 patients were enrolled from 2007 to 2010. Integrated (18)F-FDG-PET/perfusion CT was successful in 45 patients, 35 of whom underwent surgery without intervening treatment. Tumour SUV(max), SUV(mean) 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 SUV(max), SUV(mean) 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 (SUV(max) or SUV(mean)) 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 SUV(max) or SUV(mean). 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.
Authors: Marc Peeters; Timothy Jay Price; Andrés Cervantes; Alberto F Sobrero; Michel Ducreux; Yevhen Hotko; Thierry André; Emily Chan; Florian Lordick; Cornelis J A Punt; Andrew H Strickland; Gregory Wilson; Tudor-Eliade Ciuleanu; Laslo Roman; Eric Van Cutsem; Valentina Tzekova; Simon Collins; Kelly S Oliner; Alan Rong; Jennifer Gansert Journal: J Clin Oncol Date: 2010-10-04 Impact factor: 44.544
Authors: P B Vermeulen; G Gasparini; S B Fox; C Colpaert; L P Marson; M Gion; J A M Beliën; R M W de Waal; E Van Marck; E Magnani; N Weidner; A L Harris; L Y Dirix Journal: Eur J Cancer Date: 2002-08 Impact factor: 9.162
Authors: Jim Middleton; Laure Americh; Regis Gayon; Denis Julien; Michel Mansat; Pierre Mansat; Philippe Anract; Alain Cantagrel; Pierre Cattan; Jean-Marie Reimund; Luc Aguilar; Francois Amalric; Jean-Philippe Girard Journal: J Pathol Date: 2005-07 Impact factor: 7.996
Authors: F Tanaka; Y Otake; K Yanagihara; Y Kawano; R Miyahara; M Li; T Yamada; N Hanaoka; K Inui; H Wada Journal: Clin Cancer Res Date: 2001-11 Impact factor: 12.531
Authors: Vicky Goh; Steve Halligan; Frances Daley; David M Wellsted; Thomas Guenther; Clive I Bartram Journal: Radiology Date: 2008-09-23 Impact factor: 11.105
Authors: Herbert Hurwitz; Louis Fehrenbacher; William Novotny; Thomas Cartwright; John Hainsworth; William Heim; Jordan Berlin; Ari Baron; Susan Griffing; Eric Holmgren; Napoleone Ferrara; Gwen Fyfe; Beth Rogers; Robert Ross; Fairooz Kabbinavar Journal: N Engl J Med Date: 2004-06-03 Impact factor: 91.245
Authors: Michael A Fischer; Bart Vrugt; Hatem Alkadhi; Dieter Hahnloser; Thomas F Hany; Patrick Veit-Haibach Journal: Eur J Nucl Med Mol Imaging Date: 2014-04-24 Impact factor: 9.236
Authors: Martin W Huellner; Timothy D Collen; Philipp Gut; Ralph Winterhalder; Chantal Pauli; Joachim Diebold; Burkhardt Seifert; Klaus Strobel; Patrick Veit-Haibach Journal: EJNMMI Res Date: 2014-01-22 Impact factor: 3.138