Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Introduction: The selection of patients with metastatic clear cell renal cell carcinoma (ccRCC) who may benefit from targeted tyrosine kinase inhibitors has been a challenge, even more so now with the advent of new therapies. Hilar fat infiltration (HFI) is a validated prognostic factor in nonmetastatic ccRCC (TNM 2009 staging system) but has never been studied in metastatic patients. We aimed to assess its phenotype and prognostic effect in patients with metastatic ccRCC treated with first-line sunitinib.Materials and methods: In a multicentric study, we retrospectively included 90 patients and studied the corresponding ccRCC at the pathological, immunohistochemical, and molecular levels. Patient and tumor characteristics were compared using univariate and multivariate analysis. All the features were then studied by Cox models for prognostic effect.Results: HFI was found in 42 patients (46.7%), who had worse prognosis (Heng criteria) (P = 0.003), liver metastases (P = 0.036), and progressive diseases at first radiological evaluation (P = 0.024). The corresponding ccRCC was associated with poor pathological prognostic factors that are well known in nonmetastatic ccRCC. For these patients, median progression-free survival was 4 months vs. 13 months (P = 0.02), and median overall survival was 14 months vs. 29 months (P = 0.006). In a multivariate Cox model integrating all the variables, only poor prognosis, according to the Heng criteria and HFI, remained independently associated with both progression-free survival and overall survival.Conclusion: HFI was demonstrated for the first time to be an independent poor prognostic factor. Its potential role in predicting resistance to antiangiogenic therapy warrants further investigation.
ISSN: 1078-1439

hal-01632553
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01632553
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01632553/document
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01632553/file/Hilar%20fat%20infiltration_accepted.pdf
DOI : 10.1016/j.urolonc.2017.05.015

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Introduction In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. Methods The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. Results Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. Conclusions The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
International Journal for Equity in Health

hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028/document
DOI : 10.1186/1475-9276-12-21

Éditeur(s) : HAL CCSD
Résumé : National audience
Afin de prendre en compte les inégalités sociales de santé, il est fréquent d'utiliser des indices socio-économique qui synthétisent différents aspects du statut socio-économique (SES) à l'échelle de l'individu, du voisinage ou de la région. De nombreux indices existent déjà mais nombre d'entre eux utilisent un faible nombre de variables, des méthodes d'agrégation simples et/ou sélectionnent les variables uniquement depuis la littérature. Nous avons développé une nouvelle procédure de création d'indices SES visant à sélectionner à partir d'un grand nombre de variables celles qui composeront l'indice et à les agréger à l'aide de techniques d'analyses de données. Cette procédure est plus complexe à mettre en œuvre pour des non statisticiens que d'autres approches existantes, c'est pourquoi nous l'avons implémentée dans un package R nommé SesIndexCreatoR. Ce package vise à donner des outils aussi simples d'emploi que possible pour effectuer la procédure de création et exploiter ses résultats, tout en conservant les différentes possibilités et la flexibilité qu'elle offre.
Deuxièmes rencontres R
Lyon, France

Droits : info:eu-repo/semantics/OpenAccess
hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087/document
https://hal.archives-ouvertes.fr/hal-01433087/file/SesIndexCreatoR_20130328.pdf

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
INTRODUCTION: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. METHODS: The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. RESULTS: Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. CONCLUSIONS: The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
ISSN: 1475-9276

inserm-00809310
http://www.hal.inserm.fr/inserm-00809310
http://www.hal.inserm.fr/inserm-00809310/document
http://www.hal.inserm.fr/inserm-00809310/file/1475-9276-12-21.pdf
DOI : 10.1186/1475-9276-12-21