Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

Ruysscher, Dirk De; Defraene, Gilles; Ramaekers, Bram; Lambin, Philippe; Briers, Erik; Stobart, Hilary; Ward, Tim; Bentzen, Søren M.; Staa, Tjeerd van; Azria, D.; Rosenstein, Barry S.; Kerns, Sarah L.; West, Catharine M L

doi:10.1016/j.radonc.2016.11.003

Cited by 15 publications

(12 citation statements)

References 33 publications

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“…However, the low dose difference between planning strategies (V 5 from 64% to 61% on average for Level 5) was very small. For D 50 , it will be crucial to find a reproducible biomarker of lung tissue radiosensitivity [32] (e.g., through the REQUITE observational study [33]) allowing optimal (top-level lung radiosensitivity) patient selection for our selective avoidance planning. It should be noted that assuming another HU increase threshold for severe damage than the current 20 HU, would alter the absolute values of lung mass spared from severe damage (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small–cell lung cancer: a simulation study

2019

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Background: Selective avoidance aims at sparing functional lung regions. Here, we preferentially direct radiation to irreversibly nonfunctional lung areas based on planning CT imaging to reduce functional lung damage. Materials and methods: For 12 stage I-IV NSCLC patients, 5 lung substructures were segmented on the planning CT, combining voxels <-900HU,-900HU to-801HU,-800HU to-701HU,-700HU to-601HU and !-600HU (Level 1 to 5). Two VMAT plans were optimized: a reference plan blinded from substructures and a selective avoidance plan (AV) imposing gradually stricter constraints on Level 1-5, based on previously validated associations between lung subvolume baseline density and density increase (DHU) after treatment. Characteristics of treatment plans were evaluated, including subvolumes, dose, and predicted DHU (with reported 95% CI reflecting prediction model uncertainty). Results: Segmented substructures were on average 477 cc, 1157 cc, 484 cc, 69 cc, and 123 cc (Level 1-5). AV plans could spare Level 3-5, e.g., mean dose decrease of 3.5 Gy (range 0.6 Gy; 6.0 Gy) for Level 5, p<.001. This significantly reduced the average lung mass with predicted DHU>20HU by 12.5 g (95% CI: 5.4-16.9) and 27.1 g (95% CI: 10.2-32.9) for a median and upper 10th percentile patient susceptibility for damage simulation, respectively. Conclusions: Lung damage avoidance based on CT density is feasible and easy to implement. A biomarker providing a reliable selection of patients with high susceptibility for lung damage will be crucial to show the clinical relevance of this avoidance planning strategy.

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Section: Discussionmentioning

confidence: 99%

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small–cell lung cancer: a simulation study

2019

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“…Dyspnea scores are subjective, which hamper detailed quantitative analyses, including biomarker identification. The goal is the development of risk models to stratify patients according to their genetic risk for radiotherapy-induced damage and hence to more optimal personalized radiotherapy schedules (109). The single most important risk factor to develop severe radiation pneumonitis is interstitial lung disease (ILD) which can be quantified by the uptake of FDG in the lungs.…”

Section: Risk Factors For Rilimentioning

confidence: 99%

Radiation-Induced Lung Injury (RILI)

et al. 2019

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Radiation pneumonitis (RP) and radiation fibrosis (RF) are two dose-limiting toxicities of radiotherapy (RT), especially for lung, and esophageal cancer. It occurs in 5–20% of patients and limits the maximum dose that can be delivered, reducing tumor control probability (TCP) and may lead to dyspnea, lung fibrosis, and impaired quality of life. Both physical and biological factors determine the normal tissue complication probability (NTCP) by Radiotherapy. A better understanding of the pathophysiological sequence of radiation-induced lung injury (RILI) and the intrinsic, environmental and treatment-related factors may aid in the prevention, and better management of radiation-induced lung damage. In this review, we summarize our current understanding of the pathological and molecular consequences of lung exposure to ionizing radiation, and pharmaceutical interventions that may be beneficial in the prevention or curtailment of RILI, and therefore enable a more durable therapeutic tumor response.

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“…Now that substantial progress has been achieved in radiogenomics to identify biomarkers associated with development of adverse effects resulting from radiotherapy and advances have been realized towards model building, efforts are being focused on optimal design and patient selection for interventional trials using radiogenomic biomarkers 74 . One important point to consider in the design of clinical trials is that unlike disease susceptibility, the risk for radiation-induced toxicities is continuous for development of toxicities with an increasing incidence of complications with larger radiation doses and/or volumes irradiated.…”

Section: Design Of Clinical Trialsmentioning

confidence: 99%

Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity

Rosenstein

2017

Seminars in Radiation Oncology

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The overall goal of radiogenomics is the identification of genomic markers that are predictive for the development of adverse effects resulting from cancer treatment with radiation. The principal rationale for a focus on toxicity in radiogenomics is that for many patients treated with radiation, especially individuals diagnosed with early stage cancers, the survival rates are high and therefore a substantial number of people will live for a significant period of time beyond treatment. However, many of these patients could suffer from debilitating complications resulting from radiotherapy. Work in radiogenomics has greatly benefited from creation of the Radiogenomics Consortium (RGC), which includes investigators at multiple institutions located in a variety of countries. The common goal of the RGC membership is to share biospecimens and data so as to achieve large scale studies with increased statistical power to enable identification of relevant genomic markers. A principal aim of research in radiogenomics is the development of a predictive instrument to enable identification of people who are at greatest risk for adverse effects resulting from cancer treatment using radiation. It is anticipated that creation of a predictive assay characterized by a high level of sensitivity and specificity will improve precision radiotherapy and assist patients and their physicians to select the optimal treatment for each individual.

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Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

Cited by 15 publications

References 33 publications

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small–cell lung cancer: a simulation study

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small–cell lung cancer: a simulation study

Radiation-Induced Lung Injury (RILI)

Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity

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