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Lisanne van Dijk via Scopus - Elsevier Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy Advances in Radiation Oncology 2023 | Journal article DOI: 10.1016/j.adro.2022.101163 EID: 2-s2.0-85147366664 Part of ISSN: 24521094
Lisanne VAN DIJK PostDoc Position Doctor of Medicine University of Texas MD Anderson
Lisanne V. van Dijk's 7 research works with 1 citations and 104 reads, including: Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability.
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Lisanne V. van Dijk is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Medicine & Head and neck cancer. The author has an hindex of 14, co-authored 45 publications receiving 1365 citations.
Lisanne van Dijk is 29 jaar, getrouwd en moeder van drie dochters. Sinds vier jaar is ze bewust
Lisanne V van Dijk. Assistant Professor at UMCG. Verified email at umcg.nl. Radiomics NTCP TCP modeling Image Biomarkers Proton therapy multi-modality imaging. Articles Cited by Public access.. W Schaake, A van der Schaaf, LV van Dijk, ACM van den Bergh,. PLoS One 13 (7), e0197757, 2018. 33: 2018:
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Lisanne V. van Dijk. Department of Radiation Oncology, The University of Texas MD Anderson Cancer, 77030, Houston, TX, USA, Clifton D. Fuller. Department of Radiation Oncology, The University of Texas MD Anderson Cancer, 77030, Houston, TX, USA
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Lisanne V van Dijk 1 , Maria Thor 2 , Roel J H M Steenbakkers 3 , Aditya Apte 2 , Tian-Tian Zhai 3 , Ronald Borra 4 , Walter Noordzij 5 , Cherry Estilo 6 , Nancy Lee 7 , Johannes A Langendijk 3 , Joseph O Deasy 2 , Nanna M Sijtsema 3
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Lisanne Van Dijk's 7 research works with 8 citations and 258 reads, including: DASS Good: Explainable Data Mining of Spatial Cohort Data
Lisanne van Dijk Universiteit Maastricht Utrecht, Utrecht, Nederland LinkedIn
Lisanne van Dijk dr.. https://orcid.org/0000-0002-9515-5616 Overview Network Research output (74) Activities (8) Prizes (4) If you made any changes in Pure these will be visible here soon. Collaborations and top research areas from the last five years Recent external collaboration on country/territory level.
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PMCID: PMC8906058 DOI: 10.1016/j.ijrobp.2021.04.042 Abstract Purpose: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist.
Lisanne Van Dijk Leeuwarden, Friesland, Nederland professioneel profiel LinkedIn
Lisanne V van Dijk 1 , Charlotte L Brouwer 2 , Arjen van der Schaaf 2 , Johannes G M Burgerhof 3 , Roelof J Beukinga 2 , Johannes A Langendijk 2 , Nanna M Sijtsema 2 , Roel J H M Steenbakkers 2 Affiliations 1 Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.
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Lisanne van Dijk PhD, University Medical Center Groningen (UMCG) Many head and neck cancer patients suffer from persistent severe toxicities following radiotherapy. As survival rates increase, toxicity reduction has become more pivotal. This project uses Artificial Intelligence techniques to predict toxicity trajectories, which can facilitate.
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PMID: 31653573 DOI: 10.1016/j.radonc.2019.09.022 Abstract Introduction: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects.
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In short, Lisanne van Dijk's research entails the following: Deep learning to Enhance Therapeutic Outcomes and toXicity for LUNG cancer patients (DETOX-LUNG) Radiotherapy is an important treatment for lung cancer patients, yet they are often faced with severe toxicities due to radiation damage. These toxicities do not only have a big impact on.
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Dr. van Dijk's formal education as a Technical Physician gives uniquely positions her to translate technical innovations to clinic-usable applications. Her PhD is in radiomics and machine learning approaches to predict of radiation-induced toxicities and tumor outcome with multi-modality imaging in head and neck cancer patient.
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Background: The goal of this study was to comprehensively investigate the association of chemotherapy with trajectories of acute symptom development and late symptom recovery in patients with oropharyngeal cancer (OPC) by comparing symptom burden between induction chemotherapy followed by concurrent chemoradiotherapy (ICRT), concurrent chemo-rad.
