The prognostic value of pretherapeutic diffusion-weighted MRI in oropharyngeal carcinoma treated with (chemo-)radiotherapy

Lambrecht, Maarten; Vandecaveye, Vincent; Limbergen, Evert Van; Hauben, Esther; Dok, Rüveyda; Roebben, Ilse; Keyzer, Frederik De; Hermans, Bob; Nuyts, Sandra

doi:10.1102/1470-7330.2011.9056

“…Multiple studies have shown that diffusion-weighted MRI parameters, such as the apparent diffusion coefficient (ADC), which is a measure of water mobility in tissues, can accurately predict response and survival in multiple tumor sites. [120][121][122][123][124] However, lack of reproducibility of ADC measurements, due to lack of standardization of instruments between vendors and to lack of internationally accepted calibration protocols, remains a bottleneck in these types of studies. 125 Evaluations of different time points in dynamic contrast-enhanced MRI have also been used to describe tumor perfusion.…”

Section: 119mentioning

confidence: 99%

Predictive modeling of treatment outcome in rectal cancer

Stiphout¹

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“…118,119 Multiple studies have shown that diffusion-weighted MRI parameters, such as the apparent diffusion coefficient (ADC), which is a measure of water mobility in tissues, can accurately predict response and survival in multiple tumour sites. [120][121][122][123][124] However, lack of reproducibility of ADC measurements-due to lack of standardization of instruments be-tween vendors and of internationally accepted calibration protocols-remains a bottleneck in these types of studies. 125 Evaluations of different time points in dynamic contrastenhanced MRI have also been used to describe tumour perfusion.…”

Section: Imaging Featuresmentioning

confidence: 99%

Multifactorial decision support systems in radiation oncology : clinical predictors and radiomics

Velazquez

¹

0

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Predicting outcomes in radiation oncology—multifactorial decision support systems

Lambin

¹

,

Stiphout

²

,

Starmans

³

et al. 2012

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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.

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The prognostic value of pretherapeutic diffusion-weighted MRI in oropharyngeal carcinoma treated with (chemo-)radiotherapy

Cited by 3 publications

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Predictive modeling of treatment outcome in rectal cancer

Predictive modeling of treatment outcome in rectal cancer

Multifactorial decision support systems in radiation oncology : clinical predictors and radiomics

Predicting outcomes in radiation oncology—multifactorial decision support systems

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