S. Cavalieri, L. De Cecco, R. H. Brakenhoff, M. Serena Serafini, S. Canevari, S. Rossi, D. Lanfranco, F. J. P. Hoebers, F. W. R. Wesseling, S. Keek, K. Scheckenbach, D. Mattavelli, T. Hoffmann, L. López Pérez, G. Fico, M. Bologna, I. Nauta, C. René Leeman

Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project

Medical Imaging Analysis and Diagnostics Machine Learning

Despite advances in treatments, 30% to 50% of stage III‐IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.
Stage III‐IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively.
The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow‐up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both.
This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE