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  • [Clin Cancer Res.] Molecular Profile and FDG-PET/CT Total Metabolic Tumor Volume Improve Risk Classification at Diagnosis for Patients with Diffuse Large B-Cell Lymphoma.

    Henri Mondor Hospital / Anne-Segol  ene Cottereau*

  • 출처
    Clin Cancer Res.
  • 등재일
    2016 Aug 1
  • 저널이슈번호
    22(15):3801-9. doi: 10.1158/1078-0432.CCR-15-2825. Epub 2016 Mar 2.
  • 내용

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    Abstract

    PURPOSE:

    The prognostic impact of total metabolic tumor volume (TMTV) measured on pretreatment (18)F-FDG PET/CT and its added value to molecular characteristics was investigated in patients with diffuse large B-cell lymphoma (DLBCL).

     

    EXPERIMENTAL DESIGN:

    For 81 newly diagnosed patients with DLBCL treated with rituximab and CHOP/CHOP-like regimen, TMTV was computed using the 41% SUVmax thresholding method. According to the gene expression profile, determined using DASL (cDNA-mediated Annealing, Selection, Ligation and extension) technology, a subset of 57 patients was classified in germinal center B (GCB) or activated B-cell (ABC) subtypes and MYC or BCL2 overexpressed.

     

    RESULTS:

    Median follow-up was 64 months. Five-year progression-free survival (PFS) and overall survival (OS) were 60% and 63% in the whole population. Median pretherapy TMTV was 320 cm(3) (25th-75th percentiles 106-668 cm(3)). With a 300 cm(3) cutoff, patients with high TMTV (n = 43) had a 5-year PFS and OS of 43% and 46% compared with 76% and 78% for patients with a low TMTV (P = 0.0023, P = 0.0047). ABC status, MYC, or BCL2 overexpression and both overexpression ("dual expressor," DE) were significantly associated with a worse PFS and OS. TMTV combined with molecular data allowed a significant better risk substratification of ABC/GCB patients, on PFS and OS. High TMTV individualized in molecular-low-risk patients a group with a poor outcome (MYC, PFS=51%, OS=55% BCL2, PFS=49%, OS=49% or DE PFS=50%, OS=50%) and a group with a good outcome (MYC, PFS=93%, OS=93% BCL2, PFS=86%, OS=86%, or DE PFS=81%, OS=81%).


    CONCLUSIONS:

    The combination of molecular and imaging characteristics at diagnosis could lead to a more accurate selection of patients, to increase tailor therapy.  

     

    Author information

    Cottereau AS1, Lanic H2, Mareschal S3, Meignan M4, Vera P5, Tilly H2, Jardin F2, Becker S5.

    1Nuclear Medicine Department, Henri Becquerel Cancer Center and Rouen University Hospital, Rouen, France. QuantIF-LITIS (EA 4108-FR CNRS 3638), Faculty of Medicine, University of Rouen, Rouen, France. annesegolene.cottereau@aphp.fr.

    2Hematology Department, Centre Henri Becquerel, Rouen, France. UMR INSERM U918, Centre Henri Becquerel, Rouen, France.

    3UMR INSERM U918, Centre Henri Becquerel, Rouen, France. Bioinformatics, University of Rouen, Mont Saint-Aignan, France.

    4Nuclear Medicine Department, Hôpital Henri Mondor, Créteil, France.

    5Nuclear Medicine Department, Henri Becquerel Cancer Center and Rouen University Hospital, Rouen, France. QuantIF-LITIS (EA 4108-FR CNRS 3638), Faculty of Medicine, University of Rouen, Rouen, France.

     

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