Literature DB >> 28864629

Predictive Value of PET Response Combined with Baseline Metabolic Tumor Volume in Peripheral T-Cell Lymphoma Patients.

Anne-Ségolène Cottereau1, Tarec Christoffer El-Galaly2, Stéphanie Becker3,4, Florence Broussais5, Lars Jelstrup Petersen6, Christophe Bonnet7, John O Prior8, Hervé Tilly9, Martin Hutchings10, Olivier Casasnovas11, Michel Meignan12.   

Abstract

Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of aggressive non-Hodgkin lymphomas with poor outcomes on current therapy. We investigated whether response assessed with PET/CT combined with baseline total metabolic tumor volume (TMTV) could detect early relapse or refractory disease.
Methods: From 7 European centers, 140 patients with nodal PTCL who underwent baseline PET/CT were selected. Forty-three had interim PET (iPET) performed after 2 cycles (iPET2), 95 had iPET performed after 3 or 4 cycles (iPET3/4), and 96 had end-of-treatment PET (eotPET). Baseline TMTV was computed with a 41% SUVmax threshold, and PET response was reported using the Deauville 5-point scale.
Results: With a median of 43 mo of follow-up, the 2-y progression-free survival (PFS) and overall survival (OS) were 51% and 67%, respectively. iPET2-positive patients (Deauville score ≥ 4) had a significantly worse outcome than iPET2-negative patients (P < 0.0001, hazard ratio of 6.8 for PFS; P < 0.0001, hazard ratio of 6.6 for OS). The value of iPET3/4 was also confirmed for PFS (P < 0.0001) and OS (P < 0.0001). The 2-y PFS and OS for iPET3/4-positive (n = 28) and iPET3/4-negative (n = 67) patients were 16% and 32% versus 75% and 85%, respectively. The eotPET results also reflected patient outcome. A model combining TMTV and iPET3/4 stratified the population into distinct risk groups (TMTV ≤ 230 cm3 and iPET3/4-negative [2-y PFS/OS, 79%/85%]; TMTV > 230 cm3 and iPET3/4-negative [59%/84%]; TMTV ≤ 230 cm3 and iPET3/4-positive [42%/50%]; TMTV > 230 cm3 and iPET3/4-positive [0%/18%]).
Conclusion: iPET response is predictive of outcome and allows early detection of high-risk PTCL patients. Combining iPET with TMTV improves risk stratification in individual patients.
© 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  PET/CT; PTCLs; interim PET; lymphoma; metabolic tumor volume

Mesh:

Year:  2017        PMID: 28864629     DOI: 10.2967/jnumed.117.193946

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


  20 in total

1.  A risk stratification model for nodal peripheral T-cell lymphomas based on the NCCN-IPI and posttreatment Deauville score.

Authors:  Ho-Young Yhim; Yong Park; Yeon-Hee Han; Sungeun Kim; Sae-Ryung Kang; Joon-Ho Moon; Ju Hye Jeong; Ho-Jin Shin; Keunyoung Kim; Yoon Seok Choi; Kunho Kim; Min Kyoung Kim; Eunjung Kong; Dae Sik Kim; Jae Seon Eo; Ji Hyun Lee; Do-Young Kang; Won Sik Lee; Seok Mo Lee; Young Rok Do; Jun Soo Ham; Seok Jin Kim; Won Seog Kim; Joon Young Choi; Deok-Hwan Yang; Jae-Yong Kwak
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-07-28       Impact factor: 9.236

2.  Baseline and interim functional imaging with PET effectively risk stratifies patients with peripheral T-cell lymphoma.

Authors:  Neha Mehta-Shah; Kimiteru Ito; Kurt Bantilan; Alison J Moskowitz; Craig Sauter; Steven M Horwitz; Heiko Schöder
Journal:  Blood Adv       Date:  2019-01-22

3.  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

Review 4.  Current Role of Functional Imaging in the Management of Lymphoma.

Authors:  Bruce D Cheson; Michel Meignan
Journal:  Curr Oncol Rep       Date:  2021-11-04       Impact factor: 5.075

Review 5.  [Hybrid imaging in lymphoma].

Authors:  Marius E Mayerhöfer; Alexander Haug
Journal:  Radiologe       Date:  2020-05       Impact factor: 0.635

6.  Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.

Authors:  Paul Blanc-Durand; Simon Jégou; Salim Kanoun; Alina Berriolo-Riedinger; Caroline Bodet-Milin; Françoise Kraeber-Bodéré; Thomas Carlier; Steven Le Gouill; René-Olivier Casasnovas; Michel Meignan; Emmanuel Itti
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-10-24       Impact factor: 9.236

Review 7.  How to Sequence Therapies in Peripheral T Cell Lymphoma.

Authors:  Kitsada Wudhikarn; N Nora Bennani
Journal:  Curr Treat Options Oncol       Date:  2021-07-02

8.  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

9.  Baseline Total Metabolic Tumor Volume and Total Lesion Glycolysis Measured on 18F-FDG PET-CT Predict Outcomes in T-Cell Lymphoblastic Lymphoma.

Authors:  Xiaoyan Feng; Xin Wen; Ling Li; Zhenchang Sun; Xin Li; Lei Zhang; Jingjing Wu; Xiaorui Fu; Xinhua Wang; Hui Yu; Xinran Ma; Xudong Zhang; Xinli Xie; Xingmin Han; Mingzhi Zhang
Journal:  Cancer Res Treat       Date:  2020-12-02       Impact factor: 4.679

Review 10.  RESISTing the Need to Quantify: Putting Qualitative FDG-PET/CT Tumor Response Assessment Criteria into Daily Practice.

Authors:  J G Peacock; C T Christensen; K P Banks
Journal:  AJNR Am J Neuroradiol       Date:  2019-11-28       Impact factor: 4.966

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