Preditcting Treatment Outcome Using Interpretable Models for Patients with Head and Neck Cancer

Head and neck cancer accounts for around 3 % of cancers worldwide, resulting in many deaths each year. The increasing number of patients receiving a cancer diagnosis increases the demand for accurate diagnosis and effective treatment. Intra-tumor heterogeneity is said to be one of the issues in cancer therapy, an issue that needs to be solved. Radiomics pave the way for extracting features based on the shape, size, and texture of the entire tumor. Radiomics extracts features from tumors based on the gray levels in a medical image. The process of radiomics is intended to capture texture and heterogeneity in the tumor that would be impossible to deduce from a simple tumor biopsy. Feature extraction by radiomics has been proven to enrich clinical datasets with valuable features that positively impact the performance of predictive models. This thesis investigates the use of clinical and radiomics features for predicting treatment outcomes of head and neck cancer patients using interpretable models. The radiomics algorithm extracts first-order statistical, shape, and texture features from PET and CT images of each patient. The 139 patients in the training dataset were from Oslo University Hospital (OUS), whereas the 99 patients in the test set were from the MAASTRO clinic in the Netherlands. All the clinical features, together with the radiomics features, counted 388 features in total. Feature selection through the repeated elastic net technique (RENT) was performed to exclude irrelevant features from the dataset. Seven different tree-based machine learning algorithms were fitted to the data, and the performance was validated by the accuracy, ROC AUC, Matthews correlation coefficient, F1 score for class 1, and F1 score for class 0. The models were tested on the external MAASTRO dataset, and the overall best-performing models were interpreted. On the external dataset from the MAASTRO clinic, the highest-performing models obtained an MCC of 0.37 for OS prediction and 0.44 for DFS prediction. For both OS and DFS, the highest predictions were made on only the clinical data. Transparency in machine learning models greatly benefits decision-makers in clinical settings, as every prediction can be reasoned for. Predicting treatment outcomes for head and neck patients is highly possible with interpretable models. To determine if the methods used in this thesis are suited for predicting treatment outcomes for head and neck cancer patients, it is necessary to test the methods and models on more datasets