Background: The 5-years overall survival (OS) for OSCC is 60%. TNM staging is group-based and it is limited in tailoring personalized therapy. GF on primary tumor is a mature method to identify prognostic markers, but is limited by availability of surgi- cal specimen. RF, based on techniques being already part of the diagnostic phase, is fea- sible in all pts, but techniques are not yet defined. We recently defined a prognostic 48- genes signature (48-GS) in OSCC. Association and integration of GR and RF could pave the way to non-invasive assessment of prognostic models. We considered the ser- ies of OSCC pts with clinical stage III/IV from 5 European centers enrolled in the ongoing BD2Decide project. Methods: Primary tumors were analyzed for gene expression by ClariomD (Affymetrix). Several clinical and GF were tested and their prognostic value was com- pared to TNM. Magnetic resonance imaging (MRI) radiomics was evaluated on diffu- sion weighted images acquired at different b-values (0, 50, 100, 500, 1000) and on ADC maps. A total of 641 features were computed on tumor volume, including First Order Statistics (FOS), Textural and Shape-and-size RFs. A cohort of 87 pts with available data for both GF and MRI RF was analyzed. According to 48-GS, pts were divided in 2 classes: high and low risk. Statistical difference of RF was evaluated in the 2 classes using Mann-Whitney test with FDR correction. Results: The 48-GS separated the OSCC OS significantly better than TNM (p < 0.001). When challenged against available demographic and clinical factors, this model retained a significant independent association in multivariate Cox regression analysis. Five RF of the FOS group computed on fast ADC map were statistically different between the two 48-GS classes (p < 0.05). External validation of association between RF and GF is ongoing. Conclusions: We identified a prognostic model based on the 48-GS able to improve assessment of stage III/IV OSCC risk based on OS. Since significantly altered RFs mainly describe the variability of the intensity signals inside the tumor, their association with 48-GS could mirror biological differences. Clinical trial identification: NCT02832102. Legal entity responsible for the study: BD2Decide Consortium. Funding: AIRC (AIRC IG 18519); EU Horizon 2020 research/innovation program (689715). Disclosure: L. Licitra: Honoraria or advisory role: Eisai, MSD, Boehringer Ingelheim, Novartis, AstraZeneca, Roche, BMS, Merck-Serono, Bayer, Debiopharm, Sobi. All other authors have declared no conflicts of interest.

1053PDGenomics features (GF) and integration with MRI radiomics features (RF) to develop a prognostic model in oral cavity squamous cell carcinoma (OSCC) / Cavalieri, S; De Cecco, L; Calareso, G; Silva, M; Gazzani, S E; Bologna, M; Nauta, I; Wesseling, F; Lopez Perez, L; Shefi, R; Tountopoulos, V; Fico, G; Scheckenbach, K; Brakenhoff, R H; Hoebers, F; Canevari, S; Poli, T; Licitra, L; Mainardi, L. - In: ANNALS OF ONCOLOGY. - ISSN 0923-7534. - 29:suppl_8(2018). [10.1093/annonc/mdy287.009]

1053PDGenomics features (GF) and integration with MRI radiomics features (RF) to develop a prognostic model in oral cavity squamous cell carcinoma (OSCC)

CAVALIERI, SILVIA;Silva, M;Poli, T;
2018

Abstract

Background: The 5-years overall survival (OS) for OSCC is 60%. TNM staging is group-based and it is limited in tailoring personalized therapy. GF on primary tumor is a mature method to identify prognostic markers, but is limited by availability of surgi- cal specimen. RF, based on techniques being already part of the diagnostic phase, is fea- sible in all pts, but techniques are not yet defined. We recently defined a prognostic 48- genes signature (48-GS) in OSCC. Association and integration of GR and RF could pave the way to non-invasive assessment of prognostic models. We considered the ser- ies of OSCC pts with clinical stage III/IV from 5 European centers enrolled in the ongoing BD2Decide project. Methods: Primary tumors were analyzed for gene expression by ClariomD (Affymetrix). Several clinical and GF were tested and their prognostic value was com- pared to TNM. Magnetic resonance imaging (MRI) radiomics was evaluated on diffu- sion weighted images acquired at different b-values (0, 50, 100, 500, 1000) and on ADC maps. A total of 641 features were computed on tumor volume, including First Order Statistics (FOS), Textural and Shape-and-size RFs. A cohort of 87 pts with available data for both GF and MRI RF was analyzed. According to 48-GS, pts were divided in 2 classes: high and low risk. Statistical difference of RF was evaluated in the 2 classes using Mann-Whitney test with FDR correction. Results: The 48-GS separated the OSCC OS significantly better than TNM (p < 0.001). When challenged against available demographic and clinical factors, this model retained a significant independent association in multivariate Cox regression analysis. Five RF of the FOS group computed on fast ADC map were statistically different between the two 48-GS classes (p < 0.05). External validation of association between RF and GF is ongoing. Conclusions: We identified a prognostic model based on the 48-GS able to improve assessment of stage III/IV OSCC risk based on OS. Since significantly altered RFs mainly describe the variability of the intensity signals inside the tumor, their association with 48-GS could mirror biological differences. Clinical trial identification: NCT02832102. Legal entity responsible for the study: BD2Decide Consortium. Funding: AIRC (AIRC IG 18519); EU Horizon 2020 research/innovation program (689715). Disclosure: L. Licitra: Honoraria or advisory role: Eisai, MSD, Boehringer Ingelheim, Novartis, AstraZeneca, Roche, BMS, Merck-Serono, Bayer, Debiopharm, Sobi. All other authors have declared no conflicts of interest.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2852045
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