Anne-Ségolène Cottereau1, Sebastien Hapdey2,3, Loic Chartier4, Romain Modzelewski2,3, Olivier Casasnovas5, Emmanuel Itti6, Herve Tilly7, Pierre Vera2,3, Michel A Meignan6, Stéphanie Becker2,3. 1. Nuclear Medicine Department, Hôpital Henri Mondor, University Paris-Est Créteil, Créteil, France annesegolene.cottereau@aphp.fr. 2. Nuclear Medicine Department, Henri Becquerel Cancer Center and Rouen University Hospital, Rouen, France. 3. QuantIF-LITIS (EA [Equipe d'Accueil] 4108), Faculty of Medicine, University of Rouen, Rouen, France. 4. Department of Biostatistics (LYSARC), Centre Hospitalier Lyon Sud, Pierre Bénite, France. 5. Hematology Department, Hopital Le Bocage-CHU Dijon, France; and. 6. Nuclear Medicine Department, Hôpital Henri Mondor, University Paris-Est Créteil, Créteil, France. 7. Hematology Department, UMR918, Henri Becquerel Cancer Center and Rouen University Hospital, Rouen, France.
Abstract
The purpose of this study was to compare in a large series of peripheral T cell lymphoma, as a model of diffuse disease, the prognostic value of baseline total metabolic tumor volume (TMTV) measured on 18F-FDG PET/CT with adaptive thresholding methods with TMTV measured with a fixed 41% SUVmax threshold method. METHODS: One hundred six patients with peripheral T cell lymphoma, staged with PET/CT, were enrolled from 5 Lymphoma Study Association centers. In this series, TMTV computed with the 41% SUVmax threshold is a strong predictor of outcome. On a dedicated workstation, we measured the TMTV with 4 adaptive thresholding methods based on characteristic image parameters: Daisne (Da) modified, based on signal-to-background ratio; Nestle (Ns), based on tumor and background intensities; Fit, including a 3-dimensional geometric model based on spatial resolution (Fit); and Black (Bl), based on mean SUVmax The TMTV values obtained with each adaptive method were compared with those obtained with the 41% SUVmax method. Their respective prognostic impacts on outcome prediction were compared using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves. RESULTS: The median value of TMTV41%, TMTVDa, TMTVNs, TMTVFit, and TMTVBl were, respectively, 231 cm3 (range, 5-3,824), 175 cm3 (range, 8-3,510), 198 cm3 (range, 3-3,934), 175 cm3 (range, 8-3,512), and 333 cm3 (range, 3-5,113). The intraclass correlation coefficients were excellent, from 0.972 to 0.988, for TMTVDa, TMTVFit, and TMTVNs, and less good for TMTVBl (0.856). The mean differences obtained from the Bland-Altman plots were 48.5, 47.2, 19.5, and -253.3 cm3, respectively. Except for Black, there was no significant difference within the methods between the ROC curves (P > 0.4) for progression-free survival and overall survival. Survival curves with the ROC optimal cutoff for each method separated the same groups of low-risk (volume ≤ cutoff) from high-risk patients (volume > cutoff), with similar 2-y progression-free survival (range, 66%-72% vs. 26%-29%; hazard ratio, 3.7-4.1) and 2-y overall survival (79%-83% vs. 50%-53%; hazard ratio, 3.0-3.5). CONCLUSION: The prognostic value of TMTV remained quite similar whatever the methods, adaptive or 41% SUVmax, supporting its use as a strong prognosticator in lymphoma. However, for implementation of TMTV in clinical trials 1 single method easily applicable in a multicentric PET review must be selected and kept all along the trial.
The purpose of this study was to compare in a large series of peripheral T cell lymphoma, as a model of diffuse disease, the prognostic value of baseline total metabolic tumor volume (TMTV) measured on 18F-FDG PET/CT with adaptive thresholding methods with TMTV measured with a fixed 41% SUVmax threshold method. METHODS: One hundred six patients with peripheral T cell lymphoma, staged with PET/CT, were enrolled from 5 Lymphoma Study Association centers. In this series, TMTV computed with the 41% SUVmax threshold is a strong predictor of outcome. On a dedicated workstation, we measured the TMTV with 4 adaptive thresholding methods based on characteristic image parameters: Daisne (Da) modified, based on signal-to-background ratio; Nestle (Ns), based on tumor and background intensities; Fit, including a 3-dimensional geometric model based on spatial resolution (Fit); and Black (Bl), based on mean SUVmax The TMTV values obtained with each adaptive method were compared with those obtained with the 41% SUVmax method. Their respective prognostic impacts on outcome prediction were compared using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves. RESULTS: The median value of TMTV41%, TMTVDa, TMTVNs, TMTVFit, and TMTVBl were, respectively, 231 cm3 (range, 5-3,824), 175 cm3 (range, 8-3,510), 198 cm3 (range, 3-3,934), 175 cm3 (range, 8-3,512), and 333 cm3 (range, 3-5,113). The intraclass correlation coefficients were excellent, from 0.972 to 0.988, for TMTVDa, TMTVFit, and TMTVNs, and less good for TMTVBl (0.856). The mean differences obtained from the Bland-Altman plots were 48.5, 47.2, 19.5, and -253.3 cm3, respectively. Except for Black, there was no significant difference within the methods between the ROC curves (P > 0.4) for progression-free survival and overall survival. Survival curves with the ROC optimal cutoff for each method separated the same groups of low-risk (volume ≤ cutoff) from high-risk patients (volume > cutoff), with similar 2-y progression-free survival (range, 66%-72% vs. 26%-29%; hazard ratio, 3.7-4.1) and 2-y overall survival (79%-83% vs. 50%-53%; hazard ratio, 3.0-3.5). CONCLUSION: The prognostic value of TMTV remained quite similar whatever the methods, adaptive or 41% SUVmax, supporting its use as a strong prognosticator in lymphoma. However, for implementation of TMTV in clinical trials 1 single method easily applicable in a multicentric PET review must be selected and kept all along the trial.
Authors: Jasmin Mettler; Horst Müller; Conrad-Amadeus Voltin; Christian Baues; Bernd Klaeser; Alden Moccia; Peter Borchmann; Andreas Engert; Georg Kuhnert; Alexander E Drzezga; Markus Dietlein; Carsten Kobe Journal: J Nucl Med Date: 2018-06-07 Impact factor: 10.057
Authors: Amy J Weisman; Minnie W Kieler; Scott B Perlman; Martin Hutchings; Robert Jeraj; Lale Kostakoglu; Tyler J Bradshaw Journal: Radiol Artif Intell Date: 2020-09-02