Decision support systems for personalized and participative radiation oncology

Philippe Lambin; Jaap Zindler; Ben G L Vanneste; Lien Van De Voorde; Daniëlle Eekers; Inge Compter; Kranthi Marella Panth; Jurgen Peerlings; Ruben T H M Larue; Timo M Deist; Arthur Jochems; Tim Lustberg; Johan van Soest; Evelyn E C de Jong; Aniek J G Even; Bart Reymen; Nicolle Rekers; Marike van Gisbergen; Erik Roelofs; Sara Carvalho; Ralph T H Leijenaar; Catharina M L Zegers; Maria Jacobs; Janita van Timmeren; Patricia Brouwers; Jonathan A Lal; Ludwig Dubois; Ala Yaromina; Evert Jan Van Limbergen; Maaike Berbee; Wouter van Elmpt; Cary Oberije; Bram Ramaekers; Andre Dekker; Liesbeth J Boersma; Frank Hoebers; Kim M Smits; Adriana J Berlanga; Sean Walsh

doi:10.1016/j.addr.2016.01.006

Decision support systems for personalized and participative radiation oncology

Philippe Lambin, Jaap Zindler, Ben G L Vanneste, Lien Van De Voorde, Daniëlle Eekers, Inge Compter, Kranthi Marella Panth, Jurgen Peerlings, Ruben T H M Larue, Timo M Deist, Arthur Jochems, Tim Lustberg, Johan van Soest, Evelyn E C de Jong, Aniek J G Even, Bart Reymen, Nicolle Rekers, Marike van Gisbergen, Erik Roelofs, Sara CarvalhoRalph T H Leijenaar, Catharina M L Zegers, Maria Jacobs, Janita van Timmeren, Patricia Brouwers, Jonathan A Lal, Ludwig Dubois, Ala Yaromina, Evert Jan Van Limbergen, Maaike Berbee, Wouter van Elmpt, Cary Oberije, Bram Ramaekers, Andre Dekker, Liesbeth J Boersma, Frank Hoebers, Kim M Smits, Adriana J Berlanga, Sean Walsh

Research output: Contribution to journal › Review article › peer-review

Abstract

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Original language	English
Pages (from-to)	131-153
Journal	Advanced drug delivery reviews
Volume	109
DOIs	https://doi.org/10.1016/j.addr.2016.01.006
Publication status	Published - 15 Jan 2017
Externally published	Yes

Keywords

Decision Support Systems, Clinical
Humans
Neoplasms/diagnosis
Precision Medicine/methods
Radiation Oncology/methods
Treatment Outcome

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10.1016/j.addr.2016.01.006

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Lambin, P., Zindler, J., Vanneste, B. G. L., De Voorde, L. V., Eekers, D., Compter, I., Panth, K. M., Peerlings, J., Larue, R. T. H. M., Deist, T. M., Jochems, A., Lustberg, T., van Soest, J., de Jong, E. E. C., Even, A. J. G., Reymen, B., Rekers, N., van Gisbergen, M., Roelofs, E., ... Walsh, S. (2017). Decision support systems for personalized and participative radiation oncology. Advanced drug delivery reviews, 109, 131-153. https://doi.org/10.1016/j.addr.2016.01.006

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title = "Decision support systems for personalized and participative radiation oncology",

abstract = "A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.",

keywords = "Decision Support Systems, Clinical, Humans, Neoplasms/diagnosis, Precision Medicine/methods, Radiation Oncology/methods, Treatment Outcome",

author = "Philippe Lambin and Jaap Zindler and Vanneste, {Ben G L} and {De Voorde}, {Lien Van} and Dani{\"e}lle Eekers and Inge Compter and Panth, {Kranthi Marella} and Jurgen Peerlings and Larue, {Ruben T H M} and Deist, {Timo M} and Arthur Jochems and Tim Lustberg and {van Soest}, Johan and {de Jong}, {Evelyn E C} and Even, {Aniek J G} and Bart Reymen and Nicolle Rekers and {van Gisbergen}, Marike and Erik Roelofs and Sara Carvalho and Leijenaar, {Ralph T H} and Zegers, {Catharina M L} and Maria Jacobs and {van Timmeren}, Janita and Patricia Brouwers and Lal, {Jonathan A} and Ludwig Dubois and Ala Yaromina and {Van Limbergen}, {Evert Jan} and Maaike Berbee and {van Elmpt}, Wouter and Cary Oberije and Bram Ramaekers and Andre Dekker and Boersma, {Liesbeth J} and Frank Hoebers and Smits, {Kim M} and Berlanga, {Adriana J} and Sean Walsh",

note = "Copyright {\textcopyright} 2016. Published by Elsevier B.V.",

year = "2017",

month = jan,

day = "15",

doi = "10.1016/j.addr.2016.01.006",

language = "English",

volume = "109",

pages = "131--153",

journal = "Advanced drug delivery reviews",

issn = "0169-409X",

publisher = "Elsevier B.V.",

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Lambin, P, Zindler, J, Vanneste, BGL, De Voorde, LV, Eekers, D, Compter, I, Panth, KM, Peerlings, J, Larue, RTHM, Deist, TM, Jochems, A, Lustberg, T, van Soest, J, de Jong, EEC, Even, AJG, Reymen, B, Rekers, N, van Gisbergen, M, Roelofs, E, Carvalho, S, Leijenaar, RTH, Zegers, CML, Jacobs, M, van Timmeren, J, Brouwers, P, Lal, JA, Dubois, L, Yaromina, A, Van Limbergen, EJ, Berbee, M, van Elmpt, W, Oberije, C, Ramaekers, B, Dekker, A, Boersma, LJ, Hoebers, F, Smits, KM, Berlanga, AJ & Walsh, S 2017, 'Decision support systems for personalized and participative radiation oncology', Advanced drug delivery reviews, vol. 109, pp. 131-153. https://doi.org/10.1016/j.addr.2016.01.006

TY - JOUR

T1 - Decision support systems for personalized and participative radiation oncology

AU - Lambin, Philippe

AU - Zindler, Jaap

AU - Vanneste, Ben G L

AU - De Voorde, Lien Van

AU - Eekers, Daniëlle

AU - Compter, Inge

AU - Panth, Kranthi Marella

AU - Peerlings, Jurgen

AU - Larue, Ruben T H M

AU - Deist, Timo M

AU - Jochems, Arthur

AU - Lustberg, Tim

AU - van Soest, Johan

AU - de Jong, Evelyn E C

AU - Even, Aniek J G

AU - Reymen, Bart

AU - Rekers, Nicolle

AU - van Gisbergen, Marike

AU - Roelofs, Erik

AU - Carvalho, Sara

AU - Leijenaar, Ralph T H

AU - Zegers, Catharina M L

AU - Jacobs, Maria

AU - van Timmeren, Janita

AU - Brouwers, Patricia

AU - Lal, Jonathan A

AU - Dubois, Ludwig

AU - Yaromina, Ala

AU - Van Limbergen, Evert Jan

AU - Berbee, Maaike

AU - van Elmpt, Wouter

AU - Oberije, Cary

AU - Ramaekers, Bram

AU - Dekker, Andre

AU - Boersma, Liesbeth J

AU - Hoebers, Frank

AU - Smits, Kim M

AU - Berlanga, Adriana J

AU - Walsh, Sean

PY - 2017/1/15

Y1 - 2017/1/15

N2 - A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

AB - A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

KW - Decision Support Systems, Clinical

KW - Humans

KW - Neoplasms/diagnosis

KW - Precision Medicine/methods

KW - Radiation Oncology/methods

KW - Treatment Outcome

U2 - 10.1016/j.addr.2016.01.006

DO - 10.1016/j.addr.2016.01.006

M3 - Review article

C2 - 26774327

SN - 0169-409X

VL - 109

SP - 131

EP - 153

JO - Advanced drug delivery reviews

JF - Advanced drug delivery reviews

ER -

Decision support systems for personalized and participative radiation oncology

Abstract

Keywords

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