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  • Review Article
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Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

Nature Reviews Clinical Oncology volume 20, pages 640–657 (2023)Cite this article

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Abstract

The use of functional quantitative biomarkers extracted from routine PET–CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET–CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Key points

  • [18F]-Fluorodeoxyglucose (FDG)-PET–CT scans are routinely used for initial staging and assessment of treatment response in patients with lymphoma; however, the development of novel effective therapies has challenged the predictive power of routine PET–CT scans, underscoring the need for improved PET-based biomarkers.

  • Metabolic tumour volume is a quantitative PET-based biomarker that enables assessment of total disease burden (FDG-avid areas) and has prognostic value across several lymphoma subtypes independently of the assessment method, thus having potential for adoption in various clinical decision-making scenarios.

  • The deployment of deep learning-driven segmentation tools in molecular imaging enables rapid processing and normalization of data and can therefore improve risk stratification and enable scaling up of the workflows for PET-based biomarker assessment.

  • Radiomics is a rapidly evolving tool that involves high-throughput analysis of computer-extracted quantitative imaging data with the aim of capturing the whole tumour phenotype in a non-invasive manner; radiomics studies focused on PET-based biomarkers are ongoing, with promising results.

  • The identification of predictive biomarkers remains an unmet need in lymphoma research; imaging biomarkers have shown promise in pretreatment risk assessment and can potentially be used in risk-adapted clinical trials.

  • After much-needed standardization, we expect PET-based biomarkers to be incorporated in clinical trials and subsequently into decision-making models to enable the selection of the patients who will derive the greatest benefit from specific therapies.

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Fig. 1: MTV and TLG in a patient with advanced-stage diffuse large B cell lymphoma.
Fig. 2: Potential applications of deep learning-driven artificial intelligence in the clinical management of lymphoma.
Fig. 3: Workflow of radiomics analyses in lymphoma.
Fig. 4: Quantitative PET-based imaging biomarkers.
Fig. 5: Schematic overview of a factorial design for a potential PET-based risk-adapted clinical trial involving patients with Hodgkin lymphoma.

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Acknowledgements

The research of J.P.A., R.A.K., F.Y. and C.H.M. is supported by the National Cancer Institute core grant for the Sylvester Comprehensive Cancer Center P30CA240139. The work of J.P.A. is also supported by the Dwoskin Family Foundation, the Peykoff Initiative from the Lymphoma Research Foundation and the U.S. Department of Defense (grant CA220385).

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Authors and Affiliations

  1. Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA

    Juan Pablo Alderuccio & Craig H. Moskowitz

  2. Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL, USA

    Russ A. Kuker

  3. Department of Radiation Oncology, Division of Medical Physics, University of Miami Miller School of Medicine, Miami, FL, USA

    Fei Yang

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J.P.A. wrote the manuscript. All authors researched data, made substantial contributions to the discussion of content, and reviewed and/or edited the manuscript before submission.

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Correspondence to Juan Pablo Alderuccio.

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Competing interests

J.P.A. acts as a consultant for ADC Therapeutics and Genentech, receives research funding from ADC Therapeutics, and has an immediate family member who has served on the advisory boards of Agios Pharmaceuticals, Forma Therapeutics, Foundation Medicine, Inovio Pharmaceuticals, and Puma Biotechnology. C.H.M. is a member of the scientific advisory board for ADC Therapeutics, Incyte, and Kite and receives research funding from ADC Therapeutics, Beigene, Incyte, Merck, and Seattle Genetics. R.A.K. and F.Y. declare no competing interests.

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Alderuccio, J.P., Kuker, R.A., Yang, F. et al. Quantitative PET-based biomarkers in lymphoma: getting ready for primetime. Nat Rev Clin Oncol 20, 640–657 (2023). https://doi.org/10.1038/s41571-023-00799-2

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