Literature DB >> 27754905

Baseline Total Metabolic Tumor Volume Measured with Fixed or Different Adaptive Thresholding Methods Equally Predicts Outcome in Peripheral T Cell Lymphoma.

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.   

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.
© 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  PTCL; adaptive thresholds; lymphoma; metabolic tumor volume

Mesh:

Substances:

Year:  2016        PMID: 27754905     DOI: 10.2967/jnumed.116.180406

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  22 in total

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-02-17       Impact factor: 9.236

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Authors:  Charline Lasnon; Blandine Enilorac; Nicolas Aide
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-02-23       Impact factor: 9.236

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Authors:  Anne-Ségolène Cottereau; Irene Buvat; Salim Kanoun; Annibale Versari; Olivier Casasnovas; Stephane Chauvie; Jérôme Clerc; Andrea Gallamini; Michel Meignan
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-04-12       Impact factor: 9.236

5.  Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden.

Authors:  Sally F Barrington; Michel Meignan
Journal:  J Nucl Med       Date:  2019-04-06       Impact factor: 10.057

6.  A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies.

Authors:  Fanny Orlhac; Jakoba J Eertink; Anne-Ségolène Cottereau; Josée M Zijlstra; Catherine Thieblemont; Michel Meignan; Ronald Boellaard; Irène Buvat
Journal:  J Nucl Med       Date:  2021-09-16       Impact factor: 10.057

7.  Prognostic value of pre-transplantation total metabolic tumor volume on 18fluoro-2-deoxy-D-glucose positron emission tomography-computed tomography in relapsed and refractory aggressive lymphoma.

Authors:  Takeshi Sugio; Shingo Baba; Yasuo Mori; Goichi Yoshimoto; Kenjiro Kamesaki; Shuichiro Takashima; Shingo Urata; Takahiro Shima; Kohta Miyawaki; Yoshikane Kikushige; Yuya Kunisaki; Akihiko Numata; Katsuto Takenaka; Hiromi Iawasaki; Toshihiro Miyamoto; Kousei Ishigami; Koichi Akashi; Koji Kato
Journal:  Int J Hematol       Date:  2022-06-14       Impact factor: 2.319

8.  SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model.

Authors:  Luca Ceriani; Giuseppe Gritti; Luciano Cascione; Maria Cristina Pirosa; Angela Polino; Teresa Ruberto; Anastasios Stathis; Andrea Bruno; Alden A Moccia; Luca Giovanella; Stefanie Hayoz; Sämi Schär; Stefan Dirnhofer; Alessandro Rambaldi; Giovanni Martinelli; Christoph Mamot; Emanuele Zucca
Journal:  Blood Adv       Date:  2020-03-24

9.  Metabolic Tumour Volume for Response Prediction in Advanced-Stage Hodgkin Lymphoma.

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

10.  Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma.

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
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