ArticleScientific reports2017
A Novel Methodology using CT Imaging Biomarkers to Quantify Radiation Sensitivity in the Esophagus with Application to Clinical Trials.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Review
- Multiomic random forest toxicity modeling of radiation esophagitis.Physics and imaging in radiation oncology · 2025Article
- Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity.Advances in radiation oncology · 2025Article
- Artificial intelligence to predict outcomes of head and neck radiotherapy.Clinical and translational radiation oncology · 2023Article
- Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy.International journal of radiation oncology, biology, physics · 2021Article
- Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.Physics in medicine and biology · 2020Article
- A Nomogram for the Prediction of Progression and Overall Survival in Childhood Acute Lymphoblastic Leukemia.Frontiers in oncology · 2020Article
- Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer.Physics and imaging in radiation oncology · 2020Article
- Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.Frontiers in oncology · 2020Review
- Increased evidence for the prognostic value of FDG uptake on late-treatment PET in non-tumour-affected oesophagus in irradiated patients with oesophageal carcinoma.European journal of nuclear medicine and molecular imaging · 2018Article
- Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer.Translational lung cancer research · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Personalized cancer therapy seeks to tailor treatment to an individual patient's biology. Therefore, a means to characterize radiosensitivity is necessary. In this study, we investigated radiosensitivity in the normal esophagus using an imaging biomarker of radiation-response and esophageal toxicity, esophageal expansion, as a method to quantify radiosensitivity in 134 non-small-cell lung cancer patients, by using K-Means clustering to group patients based on esophageal radiosensitivity. Patients within the cluster of higher response and lower dose were labelled as radiosensitive. This information was used as a variable in toxicity prediction modelling (lasso logistic regression). The resultant model performance was quantified and compared to toxicity prediction modelling without utilizing radiosensitivity information. The esophageal expansion-response was highly variable between patients, even for similar radiation doses. K-Means clustering was able to identify three patient subgroups of radiosensitivity: radiosensitive, radio-normal, and radioresistant groups. Inclusion of the radiosensitive variable improved lasso logistic regression models compared to model performance without radiosensitivity information. Esophageal radiosensitivity can be quantified using esophageal expansion and K-Means clustering to improve toxicity prediction modelling. Finally, this methodology may be applied in clinical trials to validate pre-treatment biomarkers of esophageal toxicity.
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