Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment.

18F-FDG PET/CT Deauville Score SUVmax classical Hodgkin lymphoma early response assessment total metabolic tumor volume

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
26 Sep 2022
Historique:
received: 20 06 2022
revised: 24 08 2022
accepted: 22 09 2022
entrez: 27 10 2022
pubmed: 28 10 2022
medline: 28 10 2022
Statut: epublish

Résumé

(1) This study aimed to investigate whether baseline clinical and Positron Emission Tomography/Computed Tomography (bPET)-derived parameters could help predicting early response to the first two cycles of chemotherapy (Deauville Score at interim PET, DS at iPET) in patients with classical Hodgkin lymphoma (cHL) to identify poor responders (DS ≥ 4) who could benefit from first-line treatment intensification at an earlier time point. (2) cHL patients with a bPET and an iPET imaging study in our Centre’s records (2013−2019), no synchronous/metachronous tumors, no major surgical resection of disease prior to bPET, and treated with two cycles of ABVD chemotherapy before iPET were retrospectively included. Baseline International Prognostic Score for HL (IPS) parameters were collected. Each patient’s bPET total metabolic tumor volume (TMTV) and highest tumoral SUVmax were collected. ROC curves and Youden’s index were used to derive the optimal thresholds of TMTV and SUVmax with regard to the DS (≥4). Chi-square or Fisher’s exact test were used for the univariate analysis. A multivariate analysis was then performed using logistic regression. The type I error rate in the hypothesis testing was set to 5%. (3) A total of 146 patients were included. The optimal threshold to predict a DS ≥ 4 was >177 mL for TMTV and >14.7 for SUVmax (AUC of 0.65 and 0.58, respectively). The univariate analysis showed that only TMTV, SUVmax, advanced disease stage, and age were significantly associated with a DS ≥ 4. A multivariate model was finally derived from TMTV, SUVmax, and age, with an AUC of 0.77. (4) A multivariate model with bPET parameters and age at diagnosis was satisfactorily predictive of poor response at iPET after ABVD induction chemotherapy in cHL patients. More studies are needed to validate these results and further implement DS-predictive factors at baseline in order to prevent poor response and intensify therapeutic strategies a-priori when needed.

Identifiants

pubmed: 36292014
pii: diagnostics12102325
doi: 10.3390/diagnostics12102325
pmc: PMC9600607
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Italian Ministry of Health
ID : GR-2019-12370372

Références

Med Phys. 2017 Sep;44(9):4608-4619
pubmed: 28513853
Leuk Lymphoma. 2009 Aug;50(8):1257-60
pubmed: 19544140
J Clin Oncol. 2014 Sep 20;32(27):3059-68
pubmed: 25113753
Sci Rep. 2019 Feb 4;9(1):1322
pubmed: 30718585
Chin Med J (Engl). 2018 Aug 05;131(15):1776-1779
pubmed: 30058573
Eur J Nucl Med Mol Imaging. 2014 Sep;41(9):1735-43
pubmed: 24811577
J Leukoc Biol. 2022 Sep;112(3):539-545
pubmed: 35060170
Eur J Nucl Med Mol Imaging. 2015 Feb;42(2):328-54
pubmed: 25452219
BMC Cancer. 2018 May 3;18(1):521
pubmed: 29724189
Int J Radiat Oncol Biol Phys. 2017 Feb 1;97(2):333-338
pubmed: 28068241
Eur J Nucl Med Mol Imaging. 2021 Sep;48(10):3198-3220
pubmed: 33604689
Blood Adv. 2020 Jul 28;4(14):3268-3276
pubmed: 32702097
Metabolites. 2022 Mar 24;12(4):
pubmed: 35448472
Br J Radiol. 2021 Nov 1;94(1127):20210470
pubmed: 34415777
Oncol Lett. 2020 Oct;20(4):47
pubmed: 32788936
J Clin Oncol. 2012 Sep 20;30(27):3383-8
pubmed: 22869887
Ann Hematol. 2020 Feb;99(2):293-299
pubmed: 31897678
J Clin Oncol. 2014 Sep 20;32(27):3048-58
pubmed: 25113771
Ann Hematol. 2020 Jun;99(6):1321-1330
pubmed: 32333153
Blood Cancer J. 2021 Jul 9;11(7):126
pubmed: 34244478
J Clin Med. 2021 Sep 26;10(19):
pubmed: 34640418
Ann Hematol. 2020 Feb;99(2):277-282
pubmed: 31872362
N Engl J Med. 1998 Nov 19;339(21):1506-14
pubmed: 9819449

Auteurs

Elizabeth Katherine Anna Triumbari (EKA)

Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.

David Morland (D)

Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France.
Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France.
CReSTIC (Centre de Recherche en Sciences et Technologies de l'Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France.

Annarosa Cuccaro (A)

Hematology Unit, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Hematology Unit, Center for Translational Medicine, Azienda USL Toscana NordOvest, 55100 Livorno, Italy.

Elena Maiolo (E)

Hematology Unit, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.

Stefan Hohaus (S)

Hematology Unit, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Hematology Section, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.

Salvatore Annunziata (S)

Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.

Classifications MeSH