Predicting salivary gland dysfunction with image biomarkers in head and neck cancer patients

This thesis is the first to show that medical images can be used to improve prediction models for radiation-induced xerostomia, the syndrome of dry mouth, in head and neck cancer patients. Better life expectancy of HNC survivors has led to an increased demand for prediction, prevention and understanding of the development treatment-induced side effects. In addition, more advanced treatment have become available, such as proton therapy, that have great potential to spare normal tissues, giving rise to multiple treatment options . The image characteristics that are extracted represent patient-specific tissue characteristics that are quantified in tangible values, allowing for quantitative analysis of three-dimensional clinical image information. We developed dedicated software to extract image characteristics from clinical images. Xerostomia prediction was improved by the addition of normal tissue image characteristics, which were either extracted before, during or after radiotherapy, to reference prediction models that were based on radiation dose parameters and baseline side effects scores only. By optimizing side effect prediction, this thesis contributes to the next step in personalized treatment approaches. Furthermore, it generated hypotheses for the patient-specific reaction to radiation dose, hereby advancing towards a better understanding of the development of late treatment-induced toxicities

Predicting Patient-Reported Outcomes for Oropharyngeal Cancer Patients Treated With Radiotherapy: Evaluating the Efficacy of AUCsymptom

https://openworks.mdanderson.org/sumexp21/1095/thumbnail.jp

Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy

PURPOSE: Deep-learning (DL) techniques have been successful in disease-prediction tasks and could improve the prediction of mandible osteoradionecrosis (ORN) resulting from head and neck cancer (HNC) radiation therapy. In this study, we retrospectively compared the performance of DL algorithms and traditional machine-learning (ML) techniques to predict mandible ORN binary outcome in an extensive cohort of patients with HNC.METHODS AND MATERIALS: Patients who received HNC radiation therapy at the University of Texas MD Anderson Cancer Center from 2005 to 2015 were identified for the ML (n = 1259) and DL (n = 1236) studies. The subjects were followed for ORN development for at least 12 months, with 173 developing ORN and 1086 having no evidence of ORN. The ML models used dose-volume histogram parameters to predict ORN development. These models included logistic regression, random forest, support vector machine, and a random classifier reference. The DL models were based on ResNet, DenseNet, and autoencoder-based architectures. The DL models used each participant's dose cropped to the mandible. The effect of increasing the amount of available training data on the DL models' prediction performance was evaluated by training the DL models using increasing ratios of the original training data.RESULTS: The F1 score for the logistic regression model, the best-performing ML model, was 0.3. The best-performing ResNet, DenseNet, and autoencoder-based models had F1 scores of 0.07, 0.14, and 0.23, respectively, whereas the random classifier's F1 score was 0.17. No performance increase was apparent when we increased the amount of training data available for DL model training.CONCLUSIONS: The ML models had superior performance to their DL counterparts. The lack of improvement in DL performance with increased training data suggests that either more data are needed for appropriate DL model construction or that the image features used in DL models are not suitable for this task.</p

Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients:a literature review, implementation and multi-threshold evaluation

In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter