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Research ArticleClinical Investigation
Open Access

18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients

Kibrom B. Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont and Irène Buvat
Journal of Nuclear Medicine December 2022, 63 (12) 1925-1932; DOI: https://doi.org/10.2967/jnumed.121.263501
Kibrom B. Girum
1LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;
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Louis Rebaud
1LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;
2Research and Clinical Collaborations, Siemens Medical Solutions, Knoxville, Tennessee;
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Anne-Ségolène Cottereau
1LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;
3Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France;
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Michel Meignan
4Lysa Imaging, Henri Mondor University Hospital, AP-HP, University Paris East, Créteil, France;
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Jérôme Clerc
3Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France;
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Laetitia Vercellino
5Department of Nuclear Medicine, Saint-Louis Hospital, AP-HP, Paris, France;
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Olivier Casasnovas
6Department of Hematology, University Hospital of Dijon, Dijon, France;
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Franck Morschhauser
7Department of Hematology, Claude Huriez Hospital, University Lille, EA 7365, Research Group on Injectable Forms and Associated Technologies, Lille, France; and
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Catherine Thieblemont
8Department of Hematology, Saint Louis Hospital, AP-HP, Paris, France
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Irène Buvat
1LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;
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  • FIGURE 1.
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    FIGURE 1.

    (A) Study flowchart. (B) Study design.

  • FIGURE 2.
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    FIGURE 2.

    Example of 18F-FDG PET MIP images (left) and associated lymphoma regions (right) based on expert delineation of the 3D 18F-FDG PET images.

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    FIGURE 3.

    18F-FDG PET MIP images and segmentation results (blue color overlapped over PET MIP images) by experts (MIP_masks) and by CNN for 4 patients: from REMARC cohort (A) and from LNH073B cohort (B).

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    FIGURE 4.

    Kaplan–Meier estimates of OS and PFS from REMARC cohort according to 3D 18F-FDG PET/CT image–based features TMTV (cm3) and Dmax (cm) (A and C), and according to PET MIP image–based features sTMTV (cm2) and sDmax (cm) estimated from AI (B and D).

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    FIGURE 5.

    Kaplan–Meier estimates of OS and PFS from LNH073B cohort according to 3D 18F-FDG PET/CT image–based features TMTV (cm3) and Dmax (cm) (A and C), and according to PET MIP image–based features sTMTV (cm2) and sDmax (cm) estimated from AI (B and D).

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    TABLE 1.

    Population Characteristics

    CharacteristicREMARCLNH073B
    No. of patients28795
    Sex
     No. of men165 (57.5%)42 (44%)
     No. of women122 (42.5%)53 (56%)
    Median age (y)68 (IQR, 64.0–73.0)46 (IQR, 33.25–55.0)
    Median weight (kg)72 (IQR, 63.0–84.2)68 (IQR, 58.0–80.0)
    Median height (cm)167.5 (IQR, 160.0–175.0) (1 case missed)173 (IQR, 140.0–193.0)
    Ann Arbor stage
     <I1 (0.4%)0 (0%)
     ≥II286 (99.6%)95 (100%)
    Performance status
     0115 (40%)0 (0%)
     1121 (42%)27 (28.4%)
     242 (14.6%)43 (45.3%)
     32 (0.7%)20 (21.1%)
     42 (0.7%)5 (5.3%)
     Missing5 (1.7%)NA
    • IQR = interquartile range (quartile 1 to quartile 3); NA = not applicable.

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    TABLE 2.

    Statistics for Surrogate TMTV and Surrogate Dmax

    CohortsTMTV/sDmaxMeanSDMinimumQ1 (25%)MedianQ3 (75%)Maximum
    REMARCsTMTV (cm2)252.27245.750.4877.04174.24350.561339.36
    sDmax (cm)100.1649.890.4066.2098.0135.0225.20
    LNH073BsTMTV (cm2)388.12249.9163.68224.48307.2450.081186.24
    sDmax (cm)121.8241.1043.2092.00116.40145.60222.40
    • Q1 = first quartile (25% percentile); Q3 = third quartile (75% percentile).

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    TABLE 3.

    Results of the Univariate Analyses for PFS and OS Using Time-Dependent AUC Analysis and Cox Models (HR)

    3D 18F-FDG PET/CT estimates2D PET MIP estimates
    DataPFS/OSMetricsTMTVDmaxsTMTVsDmax
    REMARCPFSAUC0.67 (0.60–0.73)0.65 (0.58–0.72)0.65 (0.58–0.72)0.68 (0.62–0.75)
    HR11.24 (2.10–46.20)9.0 (2.53–23.63)11.81 (3.29–31.77)12.49 (3.42–34.50)
    OSAUC0.67 (0.58–0.76)0.62 (0.53–0.71)0.67 (0.58–0.76)0.68 (0.59–0.76)
    HR16.43 (2.42–77.29)8.60 (1.47–28.33)22.14 (4.73–69.06)22.79 (3.80–79.21)
    LNH073BPFSAUC0.62 (0.49–0.75)0.56 (0.39–0.72)0.66 (0.53–0.80)0.58 (0.41–0.74)
    HR13.79 (0.45–86.80)32.83 (0.4–220.8)9.24 (0.95–37.94)16.79 (0.69–86.41)
    OSAUC0.65 (0.46–0.82)0.51 (0.31–0.72)0.64 (0.45–0.82)0.50 (0.29–0.72)
    HR64.30 (0.74–384.80)49.21 (0.07–258.3)14.17 (0.59–67.02)20.39 (0.08–93.66)

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Journal of Nuclear Medicine: 63 (12)
Journal of Nuclear Medicine
Vol. 63, Issue 12
December 1, 2022
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18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients
Kibrom B. Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat
Journal of Nuclear Medicine Dec 2022, 63 (12) 1925-1932; DOI: 10.2967/jnumed.121.263501

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18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients
Kibrom B. Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat
Journal of Nuclear Medicine Dec 2022, 63 (12) 1925-1932; DOI: 10.2967/jnumed.121.263501
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Keywords

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