INVESTIGATION OF RADIATION INJURY IN THE ESOPHAGUS FROM DEFINITIVE CHEMORADIATION THERAPY USING NOVEL IMAGING BIOMARKERS

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing these subjective grading scores as complication endpoints. Not only does this schema of toxicity analysis leads to a lack of consistency between models from different study populations, and thereby radiation therapy planning recommendations for the esophagus, but inherent patient radiosensitivity is not considered, leading to suboptimal treatment regimens. The purpose of this work was to investigate radiation injury in the esophagus by first developing in-vivo imaging biomarkers of radiation-response in the esophagus using 4-dimensional computed tomography (4DCT) and 18fluorodeoxyglucose positron emission tomography (FDG-PET), separately. These imaging biomarkers were then compare with radiation esophagitis grade, using traditional and machine learning techniques, and shown to objectively quantify esophageal radiation toxicity. Metrics describing the esophageal radiation response from either imaging modality were strong classifiers of radiation esophagitis grade. Multivariate models to predict maximum esophagitis treatment grade (4DCT), and esophagitis symptom progression (FDG-PET) were developed and had strong performance for both scenarios. These imaging biomarkers were then used to comprehensively investigate the influence of dose-geometry and radiation type (photon or proton) on esophageal response. Using these radiation-response biomarkers in esophageal dose-response analysis, dose metrics with spatial information of esophageal dose coverage, (e.g. dose to a subregion of the esophagus with specific percent cross-sectional area coverage), as well as without spatial information, (traditional dose-volume histogram), was analyzed separately using machine learning methods. No detectable difference in response was observed when comparing dose metrics with and without spatial information. Statistical analysis showed no significant difference (p Inherent patient radiation sensitivity was investigated using esophageal expansion and delivered dose to the corresponding esophageal subregion. Cluster analysis was used to group patient patients based on their maximum expansion and delivered dose to the analyzed subregion of the esophagus. Patients clustered with proportionally higher expansion per delivered dose were considered radiosensitive. These results were then applied to NTCP toxicity modelling by using patient radiosensitivity cluster membership as a predictor variable. Models with the radiosensitive predictor outperformed models not including the cluster membership variable for prediction of grade 3 esophagitis

Automated contouring and statistical process control for plan quality in a breast clinical trial

Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial