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European Journal of Nuclear Medicine and Molecular Imaging

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01-02-2022 | Diffuse Large B-Cell Lymphoma | Original Article

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

Authors: 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

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 3/2022

Abstract

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 SUV_peak and the maximal distance between the largest lesion and any other lesion (Dmax_bulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUV_peak and Dmax_bulk) 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.

Trial registration number and date

EudraCT: 2006–005,174-42, 01–08-2008.

Available only for authorised users

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Title: 18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma
Authors: 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
Publication date: 01-02-2022
Publisher: Springer Berlin Heidelberg
Keywords: Diffuse Large B-Cell Lymphoma
Diffuse Large B-Cell Lymphoma
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 3/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05480-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

Trial registration number and date

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Trial registration number and date

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