Radiomics based predication of radiation induced xerostomia for head and neck cancer patients

We proposed to develop a radiomics driven machine learning prediction model. The model is to predict radiation induced xerostomia in head and neck squamous cell carcinoma (HNSCC) patients receiving radiation therapy. The aim is to predict the xerostomia levels and help radiation oncologist to re-plan or revise the radiation treatment. We chose a dataset consist of 31 HNSCC patients who underwent highly conformal radiation therapy. Each patient had pre-, mid- and post-treatment scans with contoured planning structures. It also consists of clinical follow up measurements of patients. We divided the date-set into feature extraction, training, and validation categories. Radiomic features were extracted from parotids structure of each patient in all three CT images to quantify the changes occurred during different stages of radiation therapy. Six significant radiomic features i.e., NGLDM_Coarseness, GLRLM_LGRE, GLRLM_GLNU, NGLDM_Busyness, GLZLM_LGZE and GLRLM_SRE were found based on statistical correlation with clinical outcome. GLZLM_LGZE and GLRLM_SRE are found significant in both definitive and post-surgery cases. The selected radiomic features will be utilized during training phase of machine learning model as classifier.