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Preserve

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AI for new signatures and models for tailored organ preservation approaches in laryngeal and hypopharyngeal cancer

Locally advanced laryngeal (LAR) and hypopharyngeal (HYPO) squamous cell carcinoma may be treated with induction chemotherapy (IC) followed (in case of response > 50%) by radiotherapy (RT) for larynx preservation (LP) as an alternative to total laryngectomy (TL). However, not all patients (pts) benefit from LP strategy and up to 30-40% have a TL; Preserve will personalize pts management to increase the rate of LP in LAR/HYPO cancer, by maximizing the probability of response to induction treatment. Main objectives are to assess a multimodal signature predictive of response to IC and to define alternative pathways to be tackled in pts non-responding to IC. Preserve will collect and integrate a large series of clinically annotated data from LAR/HYPO cancer pts treated with IC followed by RT, to assess a multi-omic signature of response to IC and to define alternative pathways. Transcriptomic analysis, molecular data on cell lines and radiomic evaluation will be main components of this signature. A cost-utility analysis of Preserve’s personalized treatment in LAR/HYPO cancer integrating QoL measures will assess sustainability of PM in clinical setting. Preserve has involved patients’ associations for QoL measurement, evaluation of its approach and wide dissemination of study results.

Preserve is a collaboration between 6 partners from 5 countries: ASST Spedali Civili and University of Brescia – ASSTBS (Italy) that is coordinating the project, Istituto Europeo di Oncologia – IEO (Italy), Bellvitge Biomedical Research Institute – IDIBELL (Spain), Leipzig University – U Leipzig (Germany), University of Oslo – UiO (Norway) and Athens Technology Center S.A. – ATC (Greece).

Starting from the data management system already validated in H2020 BD2Decide project, a data collection and data visualization tool will be implemented, by ATC, enabling a formal representation of prediction, and analysis of factors on which the predictive signatures are built. Thus, ATC will create dynamic visualization tools in order to support physicians and researchers and will facilitate the interpretation and assessment of the generated knowledge or the generation of new knowledge. Findable, Accessible, Interoperable and Re-usable (FAIR) principles will be followed to support the management and processing of multisource and multiscale data.

The project started in June 2021 and has a duration of 36 months.

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