18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia

Dijk, Lisanne V. van; Noordzij, Walter; Brouwer, Charlotte L.; Boellaard, Ronald; Burgerhof, Johannes G. M.; Langendijk, Johannes A.; Sijtsema, Nanna M.; Steenbakkers, Roel J.H.M.

doi:10.1016/j.radonc.2017.08.024

Cited by 61 publications

(51 citation statements)

References 41 publications

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“…Reported data suggest that combining CT and 18 F-FDG PET features from the lung might be able to predict for radiation pneumonitis in patients receiving radiation therapy for esophageal cancer (64) or that second-order features from 18 F-FDG PET might predict radiation lung injury after SBRT for stage 1 NSCLC (65) . Similarly, high levels of parotid 18 F-FDG uptake and the texture feature of long run high gray level emphasis in patients with head and neck cancer, when added to a reference model (radiation dose and baseline xerostomia score), improved the prediction of radiation-induced xerostomia (66) .…”

Section: Radiomics and Texture Analysis In Radiation Therapymentioning

confidence: 97%

Challenges and Promises of PET Radiomics

Cook

Azad

Owczarczyk

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

136

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PurposeRadiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.Methods and MaterialsRadiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.ResultsIn relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.ConclusionsAlthough at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

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Section: Radiomics and Texture Analysis In Radiation Therapymentioning

confidence: 97%

Challenges and Promises of PET Radiomics

Cook

Azad

Owczarczyk

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

136

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“…The authors concluded that the rate of CBCT-measured parotid gland image feature changes improved NTCP modeling over dose alone for late xerostomia prediction (AUC 0.77). In the context of late xerostomia prediction, baseline evaluation of changes in magnetic resonance (MR) and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET-CT)-based parotid gland features was also shown to be a promising field of application (39)(40)(41)(42)(43). In particular, parotid glands with low metabolic activity and a low fat-to-functional parenchymal ratio were matched by more heterogeneous intensity and texture imaging features: overall, these hypothesisgenerating studies showed that pre-treatment radiomics-based prediction outperformed conventional NTCP models.…”

Section: Head and Neck Radiotherapy: Parotid Glandsmentioning

confidence: 99%

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

et al. 2020

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Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday's clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organsat-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.

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“…If using a reference constraint(s) (e.g. QUANTEC (94)(95)(96)(97)(98)(99)(100) or other extant models(3, 71,[101][102][103][104][105][106][107][108][109], note prior reference/model. (45,99,110) If constraints were based on a biological model, which one(s)?…”

Section: Processmentioning

confidence: 99%

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Heukelom

Fuller

2019

Seminars in Radiation Oncology

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For nearly two decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye towards improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing Elbert Hubbard, "Art is not a thing, it is a way." "When an artist uses a conceptual form of art, it means that all of the planning and decisions are made beforehand and the execution is a perfunctory affair. The idea becomes a machine that makes the art."-Sol LeWitt (1) The concept of "adaptive radiation therapy" (ART) has been widely praised, serially modeled in silico, and heavily discussed, but to date, at a practical level, remains rarely implemented in vivo outside the research setting. We aim to discuss the "state of the ART" in head and neck therapy, with an emphasis on specification of the intent with which ART is performed; the terms of ART used, or a disambiguation of the nomenclature; technical

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18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia

Cited by 61 publications

References 41 publications

Challenges and Promises of PET Radiomics

Challenges and Promises of PET Radiomics

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

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