In this work we present the approach adopted to stratify patients at high vs. low risk for reoccurrence of Oral Squamous Cell Carcinoma (OSCC) and to model the disease progression after remission. For this purpose we developed a multiscale and multilevel model, which integrates thousands of heterogeneous data including genomics, collected by means of innovative technologies such as Point-of-Care (PoC) Real Time PCR and lab-on-chip and advanced image fusion techniques. The realized predictive model produced a bio-signature of high-risk patients and identified a set of biomarkers from tumor tissues and blood cells, indicative of potential disease reoccurrence. The NeoMark predictive model was trained and initially validated in a multicentre pilot study (three European clinical centers involved in Italy and in Spain) on a cohort of 86 patients affected by OSCC with a minimum follow up of 12 months. We discuss how the disease bio-profile identified by NeoMark was considered extremely useful by the clinicians to evaluate the risk of disease reoccurrence of a patient at the time of diagnosis and to provide a 'tailored therapy' to each case. © 2014 IEEE.
Multilevel and multiscale modeling approach for VPH-based prediction of oral cancer reoccurrences. Results of the FP7 NeoMark project / Martinelli, E.; Poli, T.; Exarchos, K.; Steger, S.. - (2014), pp. 781-784. (Intervento presentato al convegno 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014 tenutosi a Valencia, esp nel 2014) [10.1109/BHI.2014.6864480].
Multilevel and multiscale modeling approach for VPH-based prediction of oral cancer reoccurrences. Results of the FP7 NeoMark project
Poli T.
;
2014-01-01
Abstract
In this work we present the approach adopted to stratify patients at high vs. low risk for reoccurrence of Oral Squamous Cell Carcinoma (OSCC) and to model the disease progression after remission. For this purpose we developed a multiscale and multilevel model, which integrates thousands of heterogeneous data including genomics, collected by means of innovative technologies such as Point-of-Care (PoC) Real Time PCR and lab-on-chip and advanced image fusion techniques. The realized predictive model produced a bio-signature of high-risk patients and identified a set of biomarkers from tumor tissues and blood cells, indicative of potential disease reoccurrence. The NeoMark predictive model was trained and initially validated in a multicentre pilot study (three European clinical centers involved in Italy and in Spain) on a cohort of 86 patients affected by OSCC with a minimum follow up of 12 months. We discuss how the disease bio-profile identified by NeoMark was considered extremely useful by the clinicians to evaluate the risk of disease reoccurrence of a patient at the time of diagnosis and to provide a 'tailored therapy' to each case. © 2014 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
