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.
Highlights
One hurdle to elucidating if a potential biomarker has utility in characterizing dose-response in terms of radiation esophagitis, is the manner in which we quantify the toxicity
While this has great practical importance in terms of clinical symptom management, the use of grading criteria is suboptimal for the use of outcome assessment via predictive models, as well as for investigating pre-treatment biomarkers. This is because grading criteria assign a nominal score for toxicity severity based on the patient’s perceived symptom severity and physician chosen interventions, which are subjective in nature and are non-continuous quantifications[11]. This concern highlights the need for objective endpoint measures of toxicity severity, as well as endpoints for outcome assessment that directly relate to the individual patient’s radiation-response in the esophagus
The goals of this study were: (i) to quantify the inter-patient variability of esophageal response, referred to as the normal tissue toxicity in this study, by utilizing esophageal expansion along with the corresponding radiation dose to quantify individual patient’s dose-response; (ii) to determine if patient subgroups of radiation sensitivity can be identified in a mathematically reproducible manner using K-Means clustering; and (iii) to determine if the patient radiation sensitivity subgroup information can be used in the predictive modelling process to improve toxicity prediction models, thereby showing feasibility for this methodology as a validation procedure for pre-treatment biomarkers of radiation sensitivity
Summary
One hurdle to elucidating if a potential biomarker has utility in characterizing dose-response in terms of radiation esophagitis, is the manner in which we quantify the toxicity. Some studies have shown certain single nucleotide polymorphisms (SNPs) as potential pre-treatment biomarkers of radiation sensitivity using the methods previously described[10, 16,17,18,19,20,21] Grading criteria such as the common terminology criteria for adverse events (CTCAE) has been a typical clinical method of quantifying toxicity[22]. While this has great practical importance in terms of clinical symptom management, the use of grading criteria is suboptimal for the use of outcome assessment via predictive models, as well as for investigating pre-treatment biomarkers This is because grading criteria assign a nominal score for toxicity severity based on the patient’s perceived symptom severity and physician chosen interventions, which are subjective in nature and are non-continuous quantifications[11]. The goals of this study were: (i) to quantify the inter-patient variability of esophageal response, referred to as the normal tissue toxicity in this study, by utilizing esophageal expansion along with the corresponding radiation dose to quantify individual patient’s dose-response; (ii) to determine if patient subgroups of radiation sensitivity can be identified in a mathematically reproducible manner using K-Means clustering; and (iii) to determine if the patient radiation sensitivity subgroup information can be used in the predictive modelling process to improve toxicity prediction models, thereby showing feasibility for this methodology as a validation procedure for pre-treatment biomarkers of radiation sensitivity
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