Luca Ceriani1,2, Lisa Milan3, Peter W M Johnson4, Maurizio Martelli5, Stefano Presilla6, Luca Giovanella3, Emanuele Zucca7,8,9. 1. Nuclear Medicine and PET-CT Centre, Oncology Institute of Southern Switzerland, Via Ospedale 12, 6500, Bellinzona, Switzerland. luca.ceriani@eoc.ch. 2. Institute of Oncology Research, Bellinzona, Switzerland. luca.ceriani@eoc.ch. 3. Nuclear Medicine and PET-CT Centre, Oncology Institute of Southern Switzerland, Via Ospedale 12, 6500, Bellinzona, Switzerland. 4. Cancer Research UK Centre, Southampton General Hospital, Southampton, UK. 5. Department of Cellular Biotechnologies and Hematology, Sapienza University, Rome, Italy. 6. Medical Physics Unit, Ente Ospedaliero Cantonale, Bellinzona, Switzerland. 7. Institute of Oncology Research, Bellinzona, Switzerland. 8. Division of Medical Oncology, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland. 9. Department of Medical Oncology, Inselspital / Bern University Hospital, Bern, Switzerland.
Abstract
PURPOSE: This study assessed the performance of four different methods for the estimation of metabolic tumour volume (MTV) in primary mediastinal B cell lymphoma (PMBCL). METHOD: MTV was estimated using either a region growing automatic software program (RG) or a fixed threshold (FT) segmentation algorithm with the three most common cut-offs proposed in the literature (i.e., 25% and 41% of the SUVmax and SUV value ≥2.5). We compared these four methods using phantoms that simulated different set-ups of the main imaging characteristics of PMBCL (volume, shape, 18-FDG uptake and intra-lesion distribution) and assessed their performance in 103 PMBCL patients enrolled in the International Extranodal Lymphoma Study Group-26 (IELSG-26) study. RESULTS: There was good correlation between MTV values estimated in vitro and in vivo using the different methods. The 25% FT cut-off (FT25%) provided the most accurate MTV evaluation in the phantoms. The cut-off at SUV 2.5 (FT2.5) resulted in MTV overestimation that particularly increased with high SUV values. The 41% cut-off (FT41%) showed MTV underestimation that was more evident when there were high levels of heterogeneity in tracer distribution. Shape of the lesion did not affect MTV computation. The RG algorithm provided a systematic slight MTV underestimation without significant changes due to lesion characteristics. We observed analogous trends for the MTV estimation in patients, with very different derived thresholds for the four methods. Optimal cut-offs for predicting progression-free survival (PFS) ranged from 213 to 831 ml. All methods predicted PFS with similar negative predictive values (94-95%) but different positive predictive values (23-45%). CONCLUSIONS: The different methods result in significantly different MTV cut-off values. All allow risk stratification in PMBCL, but FT25% showed the best capacity to predict disease progression in the patient cohort and provided the best accuracy in the phantom model.
PURPOSE: This study assessed the performance of four different methods for the estimation of metabolic tumour volume (MTV) in primary mediastinal B cell lymphoma (PMBCL). METHOD:MTV was estimated using either a region growing automatic software program (RG) or a fixed threshold (FT) segmentation algorithm with the three most common cut-offs proposed in the literature (i.e., 25% and 41% of the SUVmax and SUV value ≥2.5). We compared these four methods using phantoms that simulated different set-ups of the main imaging characteristics of PMBCL (volume, shape, 18-FDG uptake and intra-lesion distribution) and assessed their performance in 103 PMBCLpatients enrolled in the International Extranodal Lymphoma Study Group-26 (IELSG-26) study. RESULTS: There was good correlation between MTV values estimated in vitro and in vivo using the different methods. The 25% FT cut-off (FT25%) provided the most accurate MTV evaluation in the phantoms. The cut-off at SUV 2.5 (FT2.5) resulted in MTV overestimation that particularly increased with high SUV values. The 41% cut-off (FT41%) showed MTV underestimation that was more evident when there were high levels of heterogeneity in tracer distribution. Shape of the lesion did not affect MTV computation. The RG algorithm provided a systematic slight MTV underestimation without significant changes due to lesion characteristics. We observed analogous trends for the MTV estimation in patients, with very different derived thresholds for the four methods. Optimal cut-offs for predicting progression-free survival (PFS) ranged from 213 to 831 ml. All methods predicted PFS with similar negative predictive values (94-95%) but different positive predictive values (23-45%). CONCLUSIONS: The different methods result in significantly different MTV cut-off values. All allow risk stratification in PMBCL, but FT25% showed the best capacity to predict disease progression in the patient cohort and provided the best accuracy in the phantom model.
Authors: Bruce D Cheson; Beate Pfistner; Malik E Juweid; Randy D Gascoyne; Lena Specht; Sandra J Horning; Bertrand Coiffier; Richard I Fisher; Anton Hagenbeek; Emanuele Zucca; Steven T Rosen; Sigrid Stroobants; T Andrew Lister; Richard T Hoppe; Martin Dreyling; Kensei Tobinai; Julie M Vose; Joseph M Connors; Massimo Federico; Volker Diehl Journal: J Clin Oncol Date: 2007-01-22 Impact factor: 44.544
Authors: Kerry J Savage; Stefano Monti; Jeffery L Kutok; Giorgio Cattoretti; Donna Neuberg; Laurence De Leval; Paul Kurtin; Paola Dal Cin; Christine Ladd; Friedrich Feuerhake; Ricardo C T Aguiar; Sigui Li; Gilles Salles; Francoise Berger; Wen Jing; Geraldine S Pinkus; Thomas Habermann; Riccardo Dalla-Favera; Nancy Lee Harris; Jon C Aster; Todd R Golub; Margaret A Shipp Journal: Blood Date: 2003-08-21 Impact factor: 22.113
Authors: Andreas Rosenwald; George Wright; Karen Leroy; Xin Yu; Philippe Gaulard; Randy D Gascoyne; Wing C Chan; Tong Zhao; Corinne Haioun; Timothy C Greiner; Dennis D Weisenburger; James C Lynch; Julie Vose; James O Armitage; Erlend B Smeland; Stein Kvaloy; Harald Holte; Jan Delabie; Elias Campo; Emili Montserrat; Armando Lopez-Guillermo; German Ott; H Konrad Muller-Hermelink; Joseph M Connors; Rita Braziel; Thomas M Grogan; Richard I Fisher; Thomas P Miller; Michael LeBlanc; Michael Chiorazzi; Hong Zhao; Liming Yang; John Powell; Wyndham H Wilson; Elaine S Jaffe; Richard Simon; Richard D Klausner; Louis M Staudt Journal: J Exp Med Date: 2003-09-15 Impact factor: 14.307
Authors: G Todeschini; S Secchi; E Morra; U Vitolo; E Orlandi; F Pasini; E Gallo; A Ambrosetti; C Tecchio; C Tarella; A Gabbas; A Gallamini; L Gargantini; M Pizzuti; G Fioritoni; L Gottin; G Rossi; M Lazzarino; F Menestrina; M Paulli; M Palestro; M G Cabras; F Di Vito; G Pizzolo Journal: Br J Cancer Date: 2004-01-26 Impact factor: 7.640