Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach

SIMPLE SUMMARY: Early prediction of significant alterations of head and neck (HN) cancer volume due to radiation therapy (RT) could provide an indication for necessary planning adaptations of the RT dose, tumor and organs at risk anatomy. However, the irregularities of the underlying cancer tissue and the patient-specific responses to RT render the prognostics for the tumor’s behavior exceedingly complex. In this study, a data-driven machine learning approach is proposed that incorporates the radiomic features of the low-dosage cone beam CT (CBCT) images clinically used in image-guided radiotherapy treatments in a feature selection and classification framework. The proposed model achieved high prediction performance, while able to identify indicative image characteristics for early prediction, further investigated in terms of their implications in HN cancer treated with RT. ABSTRACT: Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.