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Research ArticleClinical Investigation
Open Access

Semiquantitative PET Parameters Refine Prognosis in CAR T–Treated Lymphoma After 1 and 3 Months: A Prospective Single-Center Study

Andrea Farolfi, Beatrice Casadei, Claudio Malizia, Riccardo Ussia, Veronica Rocchi, Andrea Paccagnella, Marianna Gentilini, Cristina Nanni, Lisa Argnani, Pier Luigi Zinzani and Stefano Fanti
Journal of Nuclear Medicine April 2025, jnumed.125.269670; DOI: https://doi.org/10.2967/jnumed.125.269670
Andrea Farolfi
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
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Beatrice Casadei
2IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seragnoli”, Bologna, Italy;
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Claudio Malizia
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
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Riccardo Ussia
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
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Veronica Rocchi
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
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Andrea Paccagnella
3Nuclear Medicine Unit, “M. Bufalini” Hospital, AUSL Romagna, Cesena, Italy; and
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Marianna Gentilini
2IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seragnoli”, Bologna, Italy;
4Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
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Cristina Nanni
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
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Lisa Argnani
2IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seragnoli”, Bologna, Italy;
4Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
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Pier Luigi Zinzani
2IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seragnoli”, Bologna, Italy;
4Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
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Stefano Fanti
1Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy;
4Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
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  • FIGURE 1.
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    FIGURE 1.

    Consort diagram for patient selection.

  • FIGURE 2.
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    FIGURE 2.

    Kaplan–Meier curves for OS, comparing groups dichotomized by DS, SUVmax, MTV, and TLG at 1 mo and 3 mo.

  • FIGURE 3.
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    FIGURE 3.

    Kaplan–Meier curves for PFS, comparing groups dichotomized by DS, SUVmax, MTV, and TLG at 1 mo and 3 mo.

  • FIGURE 4.
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    FIGURE 4.

    Kaplan–Meier curves for DoR, comparing groups dichotomized by DS, SUVmax, MTV, and TLG at 1 mo and 3 mo.

Tables

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    TABLE 1.

    Demographic and Clinical Characteristics of Patients

    CharacteristicPatients (n = 61)
    Age (y)59 (48–65)
    Sex
     Female18 (30)
     Male43 (70)
    Disease stage at study entry
     I1 (2)
     II19 (31)
     III8 (13)
     IV33 (54)
    Diagnosis on central histologic review
     DLBCL53 (87)
     PMBCL8 (13)
     High-grade B-cell lymphoma0
    Double- or triple-hit rearrangement: MYC plus BCL2, BCL6, or both (n = 53)6 (11)
    Cell of origin of cancer
     Germinal center B-cell type16 (26)
     Nongerminal center B-cell type28 (46)
     Activated B-cell2 (3)
     Missing data15 (25)
    Number of previous lines of antineoplastic therapy
     11 (2)
     233 (54)
     316 (26)
     4–811 (18)
    Relapse after last therapy6 (10)
    Refractory DLBCL55 (90)
    CAR-T product
     Axi-cel34 (56)
     Tisa-cel27 (44)
    • Data are number with percentage in parentheses. Continuous data are median with IQR in parentheses.

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    TABLE 2.

    PET Parameters for bPET, PET1m, and PET3m

    SUVmaxMTVTLG
    bPET18.3 (9.7–25.2)477.5 (30.3–399.9)3,205.5 (185.1–2,837.0)
    PET1m8.2 (0–13.6)153.5 (0–54.6)1,475.0 (0–391.0)
    PET3m5.9 (0–10.2)186.7 (0–12.1)1,301.1 (0–59.3)
    • Continuous data are median and IQR in parentheses.

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    TABLE 3.

    Metabolic PET/CT Parameter Cutoff Calculated Using Receiver Operating Characteristic (ROC) Curve Analysis Considering the Entire Cohort

    ROC curve for OSROC curve for PFSROC curve for DoR
    ParameterCutoffAUC (95% CI)PSensitivitySpecificityCutoffAUC (95% CI)PSensitivitySpecificityCutoffAUC (95% CI)PSensitivitySpecificity
    PET1m
     DS444
     SUVmax9.10.7360.000754%88%8.90.719<0.000147%91%3.30.5810.0750.5620.652
     MTV60.80.744<0.000150%94%48.30.748<0.000144%100%0.30.5810.160.50.696
     TLG97.00.7310.0006558%85%204.60.731<0.000147%96%0.50.5710.180.5620.609
    PET3m
     DS444
     SUVmax6.30.799<0.000171%87%11.40.734<0.000148%100%6.30.696<0.00010.4670.913
     MTV120.10.805<0.000159%97%120.10.722<0.000144%100%120.10.681<0.00010.3331
     TLG436.90.803<0.000159%97%67.80.725<0.000148%100%53.50.684<0.00010.40.956
    • Cutoff of 4 for DS is clinical choice.

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    TABLE 4.

    Cox Regression (Continuous PET Parameters)

    OSPFSDoR
    ParameterBetaHR (95% CI)Wald testPBetaHR (95% CI)Wald testPBetaHR (95% CI)Wald testP
    PET1m
     SUVmax0.0621.0644 (1.03–1.1)12.2<0.0010.111.1175 (1.07–1.16)30.3<0.0010.0461.0466 (0.947–1.16)0.80.372
     MTV0.00161.0016 (1–1)15.8<0.0010.00191.0019 (1–1)25.6<0.0010.00511.0051 (0.997–1.01)1.70.193
     TLG0.000121.00012 (1–1)11.3<0.0010.000141.0001 (1–1)16.5<0.001−0.000150.9998 (0.999–1)0.220.641
    PET3m
     SUVmax0.0811.0839 (1.04–1.13)13.6<0.0010.121.1268 (1.08–1.18)26.5<0.0010.161.1697 (1.09–1.26)18.7<0.001
     MTV0.00121.00119 (1–1)9.1<0.0010.000891.00099 (1–1)12.6<0.0010.00981.0099 (1–1.02)12.8<0.001
     TLG0.00021.0002 (1–1)12.0<0.0010.000151.00015 (1–1)14.3<0.0010.00221.0022 (1–1)16.9<0.001
    • View popup
    TABLE 5.

    Cox Regression with Cutoff (Dichotomized PET Parameters)

    OSPFSDoR
    ParameterBetaHR (95% CI)Wald testPBetaHR (95% CI)Wald testPBetaHR (95% CI)Wald testP
    PET1m
     SUVmax1.23.26 (1.22–8.75)5.51<0.051.23.23 (1.5–6.98)8.92<0.010.882.42 (0.89–6.55)3.00.08
     MTV1.23.18 (1.26–8.03)5.98<0.051.23.44 (1.63–7.24)10.5<0.010.72.01 (0.75–5.39)1.920.16
     TLG1.02.82 (1.05–7.56)4.23<0.051.23.33 (1.5–7.41)8.74<0.010.671.95 (0.72–5.3)1.720.19
     DS0.982.66 (1.06–6.72)4.3<0.051.13.0 (1.42–6.32)8.35<0.010.671.95 (0.73–5.24)1.760.18
    PET3m
     SUVmax2.18.15 (2.81–23.6)15<0.012.28.63 (3.68–20.2)24.5<0.012.07.15 (2.45–20.9)12.9<0.01
     MTV2.39.87 (3.65–26.7)20.4<0.013.533.7 (8.68–130.0)25.9<0.013.739.6 (7.29–215.0)18.2<0.01
     TLG2.07.44 (2.71–20.4)15.2<0.012.613.1 (5.4–31.8)32.4<0.012.411.0 (3.59–33.6)17.6<0.01
     DS1.75.53 (1.78–17.2)8.78<0.011.44.19 (1.82–9.64)11.4<0.011.33.67 (1.32–10.2)6.2<0.05
    • View popup
    TABLE 6.

    Multivariable Analysis of Clinical and PET-Derived Data

    OSPFSDoR
    ParameterHR95% CIPHR95% CIPHR95% CIP
    Age1.11.02–1.190.014——————
    BT10.911.08–110.380.0433.520.98–12.630.059.691.06–88.170.04
    LDH6.431.93–21.410.0023.101.31–7.350.01———
    Fibrinogen5.271.29–21.510.021——————
    Ferritin——————0.330.11–1.00.05
    SUVmax*11.032.79–43.57<0.00166.1713.07–335.04<0.00114.573.91–54.24<0.001
    • ↵* SUVmax at PET3m.

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Journal of Nuclear Medicine: 66 (5)
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Semiquantitative PET Parameters Refine Prognosis in CAR T–Treated Lymphoma After 1 and 3 Months: A Prospective Single-Center Study
Andrea Farolfi, Beatrice Casadei, Claudio Malizia, Riccardo Ussia, Veronica Rocchi, Andrea Paccagnella, Marianna Gentilini, Cristina Nanni, Lisa Argnani, Pier Luigi Zinzani, Stefano Fanti
Journal of Nuclear Medicine Apr 2025, jnumed.125.269670; DOI: 10.2967/jnumed.125.269670

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Semiquantitative PET Parameters Refine Prognosis in CAR T–Treated Lymphoma After 1 and 3 Months: A Prospective Single-Center Study
Andrea Farolfi, Beatrice Casadei, Claudio Malizia, Riccardo Ussia, Veronica Rocchi, Andrea Paccagnella, Marianna Gentilini, Cristina Nanni, Lisa Argnani, Pier Luigi Zinzani, Stefano Fanti
Journal of Nuclear Medicine Apr 2025, jnumed.125.269670; DOI: 10.2967/jnumed.125.269670
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