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==Education and early career== |
==Education and early career== |
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Dekker pursued a Master of Science in [[Applied Physics]] at the [[University of Twente]] and obtained another |
Dekker pursued a Master of Science in [[Applied Physics]] at the [[University of Twente]] and obtained another master's degree in Technology Design from the [[Eindhoven University of Technology]] in 2000. He completed his PhD in medicine in 2003, and undertook a residency in Radiotherapy Medical Physics at Maastro Clinic from 2003 to 2005.<ref>{{cite web|url=https://www.brightlands.com/nieuws/toekomst-voorspellen-met-artificiele-intelligentie|title=Toekomst voorspellen met artificiële intelligentie}}</ref> |
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==Career== |
==Career== |
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==Media == |
==Media == |
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Dekker co-led the ProTRAIT project, focused on creating a unified database for evaluating clinical data from patients undergoing proton treatment, which was featured in Dutch media outlets.<ref>{{cite web|url=https://www.zorgvisie.nl/15-miljoen-euro-voor-protonentherapie/|title=1,5 miljoen euro voor protonentherapie}}</ref> |
Dekker co-led the ProTRAIT project, focused on creating a unified database for evaluating clinical data from patients undergoing proton treatment, which was featured in Dutch media outlets.<ref>{{cite web|url=https://www.zorgvisie.nl/15-miljoen-euro-voor-protonentherapie/|title=1,5 miljoen euro voor protonentherapie}}</ref><ref>{{cite web|url=https://icthealth.nl/nieuws/nieuw-onderzoeksinfrastructuur-voor-verbetering-van-protonentherapie/|title=Nieuw onderzoeksinfrastructuur voor verbetering van protonentherapie}}</ref> He emphasized AI's potential to enhance decision-making in radiation oncology by facilitating shared [[Shared decision-making in medicine|decision-making between physicians and patients]] in an ''[[Imaging Technology News]]'' piece,<ref>{{cite web|url=https://www.itnonline.com/article/web-exclusive-smart-machines-empower-oncology-docs-and-patients-say-astro-experts|title=WEB EXCLUSIVE: Smart Machines To Empower Oncology Docs and Patients, Say ASTRO Experts}}</ref> and discussed ethical AI solutions benefiting patients through academic, healthcare, and technology partnerships in an interview with ''Innovation Origins''.<ref>{{cite web|url=https://innovationorigins.com/en/predicting-the-future-with-ai/|title=Predicting the future with AI}}</ref> He was invited to the [[American Society for Radiation Oncology]] to share his vision on the future of AI in the radiation oncology field.<ref>{{cite web|url=https://www.auntminnie.com/clinical-news/radiation-oncology-therapy/article/15621487/astro-ais-rad-therapy-future-is-in-predicting-outcomes|title=ASTRO: AI's rad therapy future is in predicting outcomes}}</ref> In addition, he addressed researchers from [[National Institute of Technology Calicut|NITC]] and doctors from MVRCCRI, highlighting the significant potential of applying [[machine learning]] methods for early cancer detection and treatment.<ref>{{cite web|url=https://www.thehindu.com/news/national/kerala/nitc-mvrccri-to-work-with-maastricht-varsity/article65464273.ece|title=NITC, MVRCCRI to work with Maastricht varsity}}</ref> |
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==Research== |
==Research== |
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Dekker has focused his research on constructing global FAIR data-sharing infrastructures, employing AI to develop outcome prediction models from that data, and utilizing those models to enhance patient outcomes.<ref>{{cite web|url=https://www.biss-institute.com/team/andre-dekker/|title=Prof. André Dekker}}</ref> |
Dekker has focused his research on constructing global FAIR data-sharing infrastructures, employing AI to develop outcome prediction models from that data, and utilizing those models to enhance patient outcomes.<ref>{{cite web|url=https://www.biss-institute.com/team/andre-dekker/|title=Prof. André Dekker}}</ref> |
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Dekker, alongside Pieter Kubben and [[Michel Dumontier]], co-authored ''Fundamentals of Clinical Data Science'' which delved into topics such as [[personalized medicine]] offering insights in a healthcare-optimized style without requiring mathematical or coding expertise.<ref>{{cite web|url=https://www.ncbi.nlm.nih.gov/books/NBK543527/|title=Fundamentals of Clinical Data Science}}</ref> His research on the increasing significance of [[Clinical decision support system|clinical decision-support systems]] in radiation oncology discussed the multistage process involved in developing robust prediction models, emphasizing the critical role of predictive models in optimizing treatment outcomes.<ref>{{cite web|url=https://www.nature.com/articles/nrclinonc.2012.196|title=Predicting outcomes in radiation oncology—multifactorial decision support systems}}</ref> In a collaborative study, he proposed a system for characterizing and classifying oligometastatic disease.<ref>{{cite web|url=https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(19)30718-1/abstract|title=Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation}}</ref> |
Dekker, alongside Pieter Kubben and [[Michel Dumontier]], co-authored ''Fundamentals of Clinical Data Science'' which delved into topics such as [[personalized medicine]] offering insights in a healthcare-optimized style without requiring mathematical or coding expertise.<ref>{{cite web|url=https://www.ncbi.nlm.nih.gov/books/NBK543527/|title=Fundamentals of Clinical Data Science}}</ref> His research on the increasing significance of [[Clinical decision support system|clinical decision-support systems]] in radiation oncology discussed the multistage process involved in developing robust prediction models, emphasizing the critical role of predictive models in optimizing treatment outcomes.<ref>{{cite web|url=https://www.nature.com/articles/nrclinonc.2012.196|title=Predicting outcomes in radiation oncology—multifactorial decision support systems}}</ref> In a collaborative study, he proposed a system for characterizing and classifying oligometastatic disease.<ref>{{cite web|url=https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(19)30718-1/abstract|title=Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation}}</ref> |
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Dekker explored [[radiomics]], particularly in [[ |
Dekker explored [[radiomics]], particularly in [[non-small-cell lung cancer]] imaging, addressing the challenges of extracting quantitative features from [[Medical imaging|medical images]] to develop diagnostic, [[Prognostics|prognostic]], or predictive models integrating biological and medical data.<ref>{{cite web|url=https://www.sciencedirect.com/science/article/abs/pii/S0730725X12002202|title=Radiomics: the process and the challenges}}</ref> Collaborating with a team of researchers, he conducted a radiomic analysis of 1,019 patients with [[Lung cancer|lung]] or [[Head and neck cancer|head-and-neck cancer]] using [[CT scan|computed tomography]] (CT) imaging data, identifying prognostic radiomic features that showed significant power in independent datasets of the cancer patients.<ref>{{cite web|url=https://www.nature.com/articles/ncomms5006|title=Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach}}</ref> Additionally, in describing the concept and potential of radiomics in cancer research, he and his collaborators proposed guidelines to enhance the scientific integrity and clinical relevance of radiomics investigations.<ref>{{cite web|url=https://www.nature.com/articles/nrclinonc.2017.141|title=Radiomics: the bridge between medical imaging and personalized medicine}}</ref> Furthermore, his systematic review assessed the reproducibility of radiomic features used in cancer imaging for clinical decision making, noting that feature stability is influenced by factors such as image acquisition settings, [[Tomographic reconstruction|reconstruction algorithms]], and [[Data preprocessing|preprocessing methods]].<ref>{{cite web|url=https://www.sciencedirect.com/science/article/pii/S0360301618309052|title=Repeatability and Reproducibility of Radiomic Features: A Systematic Review}}</ref> |
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Dekker introduced a method for analyzing plethysmographic signals to identify [[heart rate variability]] parameters linked to [[respiration rate]], leading to an improved [[Pulse oximetry|pulse oximeter]] functionality for noninvasive respiration rate monitoring.<ref>{{cite web|url=https://patents.google.com/patent/US6702752B2/en|title=Monitoring respiration based on plethysmographic heart rate signal}}</ref> His development of an apparatus resulted in an approved patent for monitoring secondary physiological processes by analyzing optical signal variations.<ref>{{cite web|url=https://patents.google.com/patent/US6709402B2/en|title=Apparatus and method for monitoring respiration with a pulse oximeter}}</ref> He then presented a method utilizing [[Photoplethysmogram|photoplethysmography]] to gather physiological parameters, involving filtering and analyzing pleth signals to identify components of interest for determining respiratory parameters.<ref>{{cite web|url=https://patents.google.com/patent/US7001337B2/en|title=Monitoring physiological parameters based on variations in a photoplethysmographic signal}}</ref> |
Dekker introduced a method for analyzing plethysmographic signals to identify [[heart rate variability]] parameters linked to [[respiration rate]], leading to an improved [[Pulse oximetry|pulse oximeter]] functionality for noninvasive respiration rate monitoring.<ref>{{cite web|url=https://patents.google.com/patent/US6702752B2/en|title=Monitoring respiration based on plethysmographic heart rate signal}}</ref> His development of an apparatus resulted in an approved patent for monitoring secondary physiological processes by analyzing optical signal variations.<ref>{{cite web|url=https://patents.google.com/patent/US6709402B2/en|title=Apparatus and method for monitoring respiration with a pulse oximeter}}</ref> He then presented a method utilizing [[Photoplethysmogram|photoplethysmography]] to gather physiological parameters, involving filtering and analyzing pleth signals to identify components of interest for determining respiratory parameters.<ref>{{cite web|url=https://patents.google.com/patent/US7001337B2/en|title=Monitoring physiological parameters based on variations in a photoplethysmographic signal}}</ref> |
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==Bibliography== |
==Bibliography== |
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===Books=== |
===Books=== |
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*''Fundamentals of Clinical Data Science'' (2019) ISBN 978-3319997124 |
*''Fundamentals of Clinical Data Science'' (2019) ISBN 978-3319997124 |
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===Selected articles=== |
===Selected articles=== |
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*Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R. G., Granton, P., ... & Aerts, H. J. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer, 48(4), |
*Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R. G., Granton, P., ... & Aerts, H. J. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer, 48(4), 441–446. |
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*Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., ... & Gillies, R. J. (2012). Radiomics: the process and the challenges. Magnetic resonance imaging, 30(9), |
*Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., ... & Gillies, R. J. (2012). Radiomics: the process and the challenges. Magnetic resonance imaging, 30(9), 1234–1248. |
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*Leijenaar, R. T., Carvalho, S., Velazquez, E. R., Van Elmpt, W. J., Parmar, C., Hoekstra, O. S., ... & Lambin, P. (2013). Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta oncologica, 52(7), |
*Leijenaar, R. T., Carvalho, S., Velazquez, E. R., Van Elmpt, W. J., Parmar, C., Hoekstra, O. S., ... & Lambin, P. (2013). Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta oncologica, 52(7), 1391–1397. |
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*Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., ... & Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications, 5(1), 4006. |
*Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., ... & Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications, 5(1), 4006. |
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*Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., ... & Walsh, S. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), |
*Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., ... & Walsh, S. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), 749–762. |
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*Lustberg, T., van Soest, J., Gooding, M., Peressutti, D., Aljabar, P., van der Stoep, J., ... & Dekker, A. (2018). Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiotherapy and Oncology, 126(2), |
*Lustberg, T., van Soest, J., Gooding, M., Peressutti, D., Aljabar, P., van der Stoep, J., ... & Dekker, A. (2018). Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiotherapy and Oncology, 126(2), 312–317. |
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*Guckenberger, M., Lievens, Y., Bouma, A. B., Collette, L., Dekker, A., Desouza, N. M., ... & Ost, P. (2020). Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation. THE LANCET ONCOLOGY, 21(1), |
*Guckenberger, M., Lievens, Y., Bouma, A. B., Collette, L., Dekker, A., Desouza, N. M., ... & Ost, P. (2020). Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation. THE LANCET ONCOLOGY, 21(1), 18–28. |
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==References== |
==References== |
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{{reflist}} |
{{reflist}} |
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[[Category:Maastricht University alumni]] |
[[Category:Maastricht University alumni]] |
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[[Category:Living people]] |
[[Category:Living people]] |
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[[Category:1974 births]] |
Revision as of 10:28, 15 May 2024
André Dekker | |
---|---|
Born | June 20, 1974 |
Nationality | Dutch |
Education | Master of Science in Applied Physics Master of Science in Technology Design Doctor of Philosophy in Medicine |
Alma mater | Ludger College University of Twente Eindhoven University of Technology University Hospital Maastricht |
Occupation(s) | Medical physicist, author and academic |
Scientific career | |
Institutions | Maastro Clinic Maastricht University Maastricht UMC+ Medical Data Works |
Thesis | Pressure-Volume Loops in Cardiac Surgery (2003) |
André Dekker is a Dutch medical physicist, author, and academic who is a Professor[1] and Head of Clinical Data Science at Maastricht University (UM), Maastricht UMC+ and Maastro Clinic. He also holds the position of Chief Scientific Officer at Medical Data Works.[2]
Dekker's research centers on federated FAIR data infrastructures, AI for health outcome prediction models, and applying AI to enhance patient and citizen health.[3] He has written of 250 articles and is the co-author of the book Fundamentals of Clinical Data Science.[4]
Education and early career
Dekker pursued a Master of Science in Applied Physics at the University of Twente and obtained another master's degree in Technology Design from the Eindhoven University of Technology in 2000. He completed his PhD in medicine in 2003, and undertook a residency in Radiotherapy Medical Physics at Maastro Clinic from 2003 to 2005.[5]
Career
Dekker served as a Radiotherapy Medical Physics Resident at Maastro Clinic from 2003 to 2005, later assuming the role of Medical Physics Head and Member of the Management Team until 2010. He was the Head of Information & Services, Member of the Management Team at Maastro Clinic from 2010 to 2015, and Scientific Director at Maastro Innovations from 2010 to 2018. He has served on advisory boards of organizations, including the European Society for Radiotherapy & Oncology, Hanarth Fund,[6] MD Anderson, Novo Nordisk Foundation, Luxemburg National Research Fund and Peter Munk Cardiac Centre.[7] He serves as the Head of Clinical Data Science at UM's Medical Center, while concurrently holding the positions of Full Professor of Clinical Data Science,[8] Chief Scientific Officer at Medical Data Works, and Medical Physicist at Maastro Clinic.[9]
Media
Dekker co-led the ProTRAIT project, focused on creating a unified database for evaluating clinical data from patients undergoing proton treatment, which was featured in Dutch media outlets.[10][11] He emphasized AI's potential to enhance decision-making in radiation oncology by facilitating shared decision-making between physicians and patients in an Imaging Technology News piece,[12] and discussed ethical AI solutions benefiting patients through academic, healthcare, and technology partnerships in an interview with Innovation Origins.[13] He was invited to the American Society for Radiation Oncology to share his vision on the future of AI in the radiation oncology field.[14] In addition, he addressed researchers from NITC and doctors from MVRCCRI, highlighting the significant potential of applying machine learning methods for early cancer detection and treatment.[15]
Research
Dekker has focused his research on constructing global FAIR data-sharing infrastructures, employing AI to develop outcome prediction models from that data, and utilizing those models to enhance patient outcomes.[16]
Dekker, alongside Pieter Kubben and Michel Dumontier, co-authored Fundamentals of Clinical Data Science which delved into topics such as personalized medicine offering insights in a healthcare-optimized style without requiring mathematical or coding expertise.[17] His research on the increasing significance of clinical decision-support systems in radiation oncology discussed the multistage process involved in developing robust prediction models, emphasizing the critical role of predictive models in optimizing treatment outcomes.[18] In a collaborative study, he proposed a system for characterizing and classifying oligometastatic disease.[19]
Dekker explored radiomics, particularly in non-small-cell lung cancer imaging, addressing the challenges of extracting quantitative features from medical images to develop diagnostic, prognostic, or predictive models integrating biological and medical data.[20] Collaborating with a team of researchers, he conducted a radiomic analysis of 1,019 patients with lung or head-and-neck cancer using computed tomography (CT) imaging data, identifying prognostic radiomic features that showed significant power in independent datasets of the cancer patients.[21] Additionally, in describing the concept and potential of radiomics in cancer research, he and his collaborators proposed guidelines to enhance the scientific integrity and clinical relevance of radiomics investigations.[22] Furthermore, his systematic review assessed the reproducibility of radiomic features used in cancer imaging for clinical decision making, noting that feature stability is influenced by factors such as image acquisition settings, reconstruction algorithms, and preprocessing methods.[23]
Dekker introduced a method for analyzing plethysmographic signals to identify heart rate variability parameters linked to respiration rate, leading to an improved pulse oximeter functionality for noninvasive respiration rate monitoring.[24] His development of an apparatus resulted in an approved patent for monitoring secondary physiological processes by analyzing optical signal variations.[25] He then presented a method utilizing photoplethysmography to gather physiological parameters, involving filtering and analyzing pleth signals to identify components of interest for determining respiratory parameters.[26]
Awards and honors
- 2019 – Sir Godfrey Hounsfield Award, British Institute of Radiology[27]
- 2020 – Health Care Award, Computable
Bibliography
Books
- Fundamentals of Clinical Data Science (2019) ISBN 978-3319997124
Selected articles
- Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R. G., Granton, P., ... & Aerts, H. J. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer, 48(4), 441–446.
- Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., ... & Gillies, R. J. (2012). Radiomics: the process and the challenges. Magnetic resonance imaging, 30(9), 1234–1248.
- Leijenaar, R. T., Carvalho, S., Velazquez, E. R., Van Elmpt, W. J., Parmar, C., Hoekstra, O. S., ... & Lambin, P. (2013). Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta oncologica, 52(7), 1391–1397.
- Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., ... & Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications, 5(1), 4006.
- Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., ... & Walsh, S. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), 749–762.
- Lustberg, T., van Soest, J., Gooding, M., Peressutti, D., Aljabar, P., van der Stoep, J., ... & Dekker, A. (2018). Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiotherapy and Oncology, 126(2), 312–317.
- Guckenberger, M., Lievens, Y., Bouma, A. B., Collette, L., Dekker, A., Desouza, N. M., ... & Ost, P. (2020). Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation. THE LANCET ONCOLOGY, 21(1), 18–28.
References
- ^ "Professor André Dekker".
- ^ "Andre Dekker (A.L.A.J.)".
- ^ "Andre Dekker".
- ^ "Andre Dekker Maastro Clinic" (PDF).
- ^ "Toekomst voorspellen met artificiële intelligentie".
- ^ "Organisation".
- ^ "Peter Munk Cardiac Centre strategic plan" (PDF).
- ^ "Professor of Clinical Data Science, Maastricht UMC+".
- ^ "Andre Dekker MSc, PhD".
- ^ "1,5 miljoen euro voor protonentherapie".
- ^ "Nieuw onderzoeksinfrastructuur voor verbetering van protonentherapie".
- ^ "WEB EXCLUSIVE: Smart Machines To Empower Oncology Docs and Patients, Say ASTRO Experts".
- ^ "Predicting the future with AI".
- ^ "ASTRO: AI's rad therapy future is in predicting outcomes".
- ^ "NITC, MVRCCRI to work with Maastricht varsity".
- ^ "Prof. André Dekker".
- ^ "Fundamentals of Clinical Data Science".
- ^ "Predicting outcomes in radiation oncology—multifactorial decision support systems".
- ^ "Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation".
- ^ "Radiomics: the process and the challenges".
- ^ "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach".
- ^ "Radiomics: the bridge between medical imaging and personalized medicine".
- ^ "Repeatability and Reproducibility of Radiomic Features: A Systematic Review".
- ^ "Monitoring respiration based on plethysmographic heart rate signal".
- ^ "Apparatus and method for monitoring respiration with a pulse oximeter".
- ^ "Monitoring physiological parameters based on variations in a photoplethysmographic signal".
- ^ "Professor Dekker receives the Sir Godfrey Hounsfield award".