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Background: Radiotherapy is one of the cornerstones of the treatment of Head and Neck Squamous Cell Carcinomas (HNSCC). However, radioresistance is associated with a high risk of recurrence. To propose strategies (such as combinations with drugs) that could over intrinsic radioresistance, it is crucial to predict the response to treatment. Patient-Derived Tumor Organoids (PDTO) are in vitro tridimensional microtumors obtained from patient' own cancer samples. They have been shown to serve as reliable surrogates of the tumor response in patients. Methods: The ORGAVADS study is a multicenter observational trial conducted to investigate the feasibility of generating and testing PDTO derived from HNSCC for the evaluation of sensitivity to treatments. PDTO are obtained after dissociation of resected tumors remaining from tissues necessary for the diagnosis. Embedding of tumor cells is then performed in extracellular matrix and culture in medium supplemented with growth factors and inhibitors. Histological and immunohistochemical characterizations are performed to validate the resemblance between PDTO and their original tumor. Response of PDTO to chemotherapy, radiotherapy and innovating combinations are assessed, as well as response to immunotherapy using co-cultures of PDTO with autologous immune cells collected from patient blood samples. Transcriptomic and genetic analyses of PDTO allow validation of the models compared to patients' own tumor and identification of potential predictive biomarkers. Discussion: This study is designed to develop PDTO models from HNSCC. It will allow comparing the response of PDTO to treatment and the clinical response of the patients from whom they are derived. Our aim is to study the PDTO ability to predict the clinical response to treatment for each patient in view of a personalized medicine as well as to establish a collection of HNSCC models that will be useful for future innovative strategies evaluation. Trial registration: NCT04261192, registered February 7, 2020, last amendment v4 accepted on June, 2021. [ABSTRACT FROM AUTHOR]