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  • Review Article
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Predicting outcomes in radiation oncology—multifactorial decision support systems

Nature Reviews Clinical Oncology volume 10, pages 27–40 (2013)Cite this article

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Abstract

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome—including survival, recurrence patterns and toxicity—in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Key Points

  • Many prediction models that consider factors related to disease and treatment are available, but lack standardized assessments of their robustness, reproducibility or clinical utility

  • The complete cycle of model development for decision making in radiotherapy involves several stages, including selection of data, performance measure, classification and external validation

  • Clinical decision-support systems (CDSSs) based on validated predictors will be crucial to implement personalized radiation oncology

  • Tolerance of normal tissue is the dose-limiting factor for the administration of radiotherapy, therefore, any CDSS should be based on predictors of tumour control and the probability of complications

  • Rapid-learning healthcare will enable the increasingly rapid validation of CDSSs, which, in turn, will enable the next major advances in shared decision making

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Figure 1: Schematic overview of methodological processes in clinical decision-support system development, describing model development, assessment of clinical usefulness and what ideally to publish.
Figure 2: The importance of considering measured dose for outcome prediction for a patient with prostate cancer.
Figure 3: Axial FDG–PET and CT images of two different patients with NSCLC.
Figure 4: A published nomogram for local control in patients with cancer of the larynx treated with radiotherapy.
Figure 5: Knowledge-driven health-care principles using a clinical decision-support system in conjunction with standard evidence and regulations to choose the optimal treatment.
Figure 6: A simplified schematic representation of systems biology applied to radiotherapy.

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Acknowledgements

We acknowledge financial support from the Center for Translational Molecular Medicine framework (AIR FORCE), European Union sixth and seventh framework programme (ARTFORCE and METOXIA), INTERREG (www.eurocat.info), QuIC-ConCePT (funded by the Innovative Medicine Initiative Joint Undertaking) and the Dutch Cancer Society (KWF UM 2011-5020 and KWF UM 2009-4454).

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Authors and Affiliations

  1. Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands

    Philippe Lambin, Ruud G. P. M. van Stiphout, Maud H. W. Starmans, Emmanuel Rios-Velazquez, Georgi Nalbantov, Erik Roelofs, Wouter van Elmpt, Dirk De Ruysscher & Andre Dekker

  2. Departments of Radiation Oncology and Biostatistics and Computational Biology, Dana–Farber Cancer Institute, Harvard University, Boston, 02215, MA, USA

    Hugo J. W. L. Aerts

  3. Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, M5G 0A3, ON, Canada

    Paul C. Boutros

  4. Department of Surgery, Catholic University of the Sacred Heart, Policlinico Universitario Agostino Gemelli, Rome, 00168, Italy

    Pierluigi Granone

  5. Department of Radiotherapy, Catholic University of the Sacred Heart, Policlinico Universitario Agostino Gemelli, Rome, 00168, Italy

    Vincenzo Valentini

  6. Division of Biological Stress Response, H3, The Netherlands Cancer Institute, Amsterdam, 1066 CX, The Netherlands

    Adrian C. Begg

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  1. Philippe Lambin
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P. Lambin and R. G. P. M. van Stiphout contributed equally to this manuscript, leading the research efforts for the data, discussion of the article content and writing of the manuscript. M. H. W. Starmans, E. Rios-Velazquez, E. Roelofs, W. van Elmpt, A. C. Begg, and D. De Ruysscher contributed to the discussion of the article's content and helped to write the manuscript. G. Nalbantov and H. J. W. L. Aerts helped to write the manuscript. P. C. Boutros and A. Dekker researched the data, contributed to the discussion of the article's content and helped to write the manuscript. P. Granone and V. Valentini contributed to the discussion of the article's content. All authors edited the manuscript before submission.

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Correspondence to Philippe Lambin.

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Lambin, P., van Stiphout, R., Starmans, M. et al. Predicting outcomes in radiation oncology—multifactorial decision support systems. Nat Rev Clin Oncol 10, 27–40 (2013). https://doi.org/10.1038/nrclinonc.2012.196

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