18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma

Jakoba J. Eertink, Tim van de Brug, Sanne E. Wiegers, Gerben J.C. Zwezerijnen, Elisabeth A.G. Pfaehler, Pieternella J. Lugtenburg, Bronno van der Holt, Henrica C.W. de Vet, Otto S. Hoekstra, Ronald Boellaard, Josée M. Zijlstra*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Purpose: Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. Methods: Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. Results: The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). Conclusion: Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. 

Original languageEnglish
Pages (from-to)932-942
Number of pages11
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Issue number3
Early online date18 Aug 2021
Publication statusPublished - Feb 2022

Bibliographical note

Funding Information:
This work is financially supported by the Dutch Cancer Society (# VU 2018–11648).

Funding Information:
J.J.E., T.v.d.B., S.E.W., G.J.C.Z., E.A.G.P., B.v.d.H., H.C.W.d.V., O.S.H., and R.B. declare no competing financial interests. P.J.L. received research funding from Takeda, Servier, and Roche and received honoraria for advisory boards from Takeda, Servier, Genentech, Genmab, Celgene, and Incyte. J.M.Z. received research funding from Roche and received honoraria for advisory boards from Takeda, Gilead, and Roche.

Publisher Copyright:
© 2021, The Author(s).


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