Artificial Intelligence for Locally Advanced Head Neck Cancer Treated With Multi-modality Adaptive RadioTherapy: Machine Learning-based Radiomic Prediction of Outcome and Toxicity (RadiomicART)

Current clinical management algorithms for squamous cell carcinoma of head and neck (HNSCC) involve the use of surgery and / or radiotherapy (RT) depending on the stage of the disease at diagnosis. Radical RT, exclusive or in combination with systemic therapy, represents an effective therapeutic option according to the international guidelines. Despite the recent technological advancements in the field of RT, about 30-50% of patients will develop locoregional failure after primary treatment . Moreover, although the development of Intensity modulated radiation therapy (IMRT) and Volumetric modulated arc therapy (VMAT) techniques allowed a greater sparing of dose on healthy tissues, radiation-induced toxicity still represents a relevant concern, impacting on quality of life. The continuous effort of personalized medicine has the goal of improving patient's outcome, in terms of both disease's control and pattern of toxicity. Advanced imaging modalities appear to play an essential role in the customization of the radiation treatment as shown through the use of Adaptive Radiotherapy (ART) and radiomic. With ART we mean the adaptation of tumor volumes and surrounding organs at risk (OARs) to the shrinkage and patient emaciation during RT treatment. Adaptive radiotherapy (ART) includes techniques that allow knowledge of patient-specific anatomical variations informed by Image-guided radiotherapies (IGRTs) to feedback into the plan and dose-delivery optimization during the treatment course. Radiomic is the extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity. Radiomic features extracted from medical images can be used as input features to create a machine learning model able to predict survival, and to guide treatment thanks to its predictive value in view of therapy personalization.

The combination of both ART and radiomic analysis could potentially be considered a further advance in the personalization of oncological treatments, and in particular for radiation treatments. For this reason, the investigators designed the present research project with the aim to prospectively evaluate a machine learning-based radiomic approach to predict outcome and toxicity of HNSCC patients treated with ART by mean of CT, MRI and PET-scan.