Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project. Issue 2 (27th October 2020)

• Record Type: Journal Article
• Authors: Cavalieri, Stefano; De Cecco, Loris; Brakenhoff, Ruud H.; Serafini, Mara Serena; Canevari, Silvana; Rossi, Silvia; Lanfranco, Davide; Hoebers, Frank J. P.; Wesseling, Frederik W. R.; Keek, Simon; Scheckenbach, Kathrin; Mattavelli, Davide; Hoffmann, Thomas; López Pérez, Laura; Fico, Giuseppe; Bologna, Marco; Nauta, Irene; Leemans, C. René; Trama, Annalisa; Klausch, Thomas; Berkhof, Johannes Hans; Tountopoulos, Vasilis; Shefi, Ron; Mainardi, Luca; Mercalli, Franco; Poli, Tito; Licitra, Lisa
• Is Part Of: Head & neck. Volume 43:Issue 2(2021)
• Page Start: 601
• Page End: 612
• Publication Date: 2020-10-27
• Subjects: big data -- head and neck cancer -- prognostic models -- radiomics -- transcriptomics; Head -- Diseases -- Periodicals; Neck -- Diseases -- Periodicals; Head -- Periodicals; Neck -- Periodicals; Face -- Periodicals; 617.51059
• DOI: 10.1002/hed.26515
• Languages: English
• ISSNs: 1043-3074

Abstract

Abstract: Background: 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. Methods: 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. Results: 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. Conclusions: 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.