Triplet chemotherapy with or without bevacizumab as first line treatment for metastatic colorectal cancer: An AGEO multicenter real-world study.

BACKGROUND: Prior trials validated triplet chemotherapy (Tri-CT) with bevacizumab as first line treatment for metastatic colorectal cancer (mCRC) but real-world data are scarce and practices remain heterogeneous. AIMS: To evaluate Tri-CT +/- bevacizumab efficacy and safety, and to identify factors influencing treatment decisions. METHODS: The COLOTRIP retrospective study enrolled mCRC patients treated from 2014 to 2019 in 14 French centers. RESULTS: Of 299 patients (81% PS 0-1, 58% RAS-mutated and 19% BRAF-mutated), 51% received Tri-CT and 49% Tri-CT + bevacizumab. Metastatic disease was classified as resectable (6.5%), potentially resectable (40%), and unresectable (54%). Bevacizumab use was associated with primary tumor location, mutational status and number of metastases. Median overall survival was 33.5 months in the Tri-CT group and 23.9 months in the Tri-CT + bevacizumab group, with median progression-free survival being 14.5 and 11.4 months. After adjusting for initial characteristics, no difference in survival was noted. Around 30% of patients experienced grade ≥3 adverse events. CONCLUSIONS: This study highlights several factors influencing Tri-CT use +/- bevacizumab decision and confirms the real-world good oncological outcomes and tolerability of these regimens in mCRC patients. Our results suggest that Tri-CT alone may by an appropriate option for specific subgroups of patients.

[Last year of residency for radiation oncologists in France: Overview and perspectives]. / Phase de consolidation (docteur junior) en oncologie­radiothérapie en France : état des lieux et perspectives.

PURPOSE: The last year of the radiotherapy oncology internship in France has become a phase of empowerment, called "junior doctor", allowing interns to validate acts previously reserved only for senior doctors. This study focused on the responsibilities given to the first promotion of junior doctors in France and their feelings on this new status. MATERIAL AND METHODS: A cross-sectional survey was carried out by the French associations of interns and young doctors in oncology, Aerio and SFjRO. A questionnaire was sent to the class referents of each city for transmission to the junior doctors of the year 2021-2022 from September 1st to November 30th, 2022. The questions concerned training, the modalities of this year and the feelings. Responses were analyzed anonymously using R. 4.3.1 software. RESULTS: For radiation oncology, 33 responses were obtained from 21 cities. For most junior doctors, three to four localizations (51%) were performed with an average of five new patients per week. The contours were reviewed either systematically (51%) or only at the beginning (32%). Dosimetry was reported as never countersigned in 19%; 80% of junior doctors described having been the only radiation oncologist during multidisciplinary staff meetings. The two main areas of improvement were theoretical training (45%) and legal frameworks/contracts (42%). CONCLUSION: These results relating to the first class of junior doctors showed an overall agreement with the recommendations of the Cnec. Feedback from interns was positive. The supervision of brachytherapy and dosimetry activities, the presence alone in multidisciplinary panel remained points of vigilance.

Multiple correspondence analysis in predictive logistic modelling: application to a living-donor kidney transplantation data.

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors. These models were compared to two other models, ordinarily built by backward and forward stepwise selection, which yielded, respectively, five and two predictors. After internal validation, the models performance statistics showed similar results. Likelihood ratio tests suggested that backward approach achieved a better fit than the forward modelling in both methods and the Vuong's non-nested test between backward-built models suggested that these were undistinguishable. We conclude that the tolerance distance, in combination with MCA, could be a feasible method for variable selection in logistic modelling, when there are several categorical predictors.

A heuristic index for selecting similar categories in multiple correspondence analysis applied to living donor kidney transplantation.

This work introduces a heuristic index (the "tolerance distance") to define the "closeness" of two variable categories in multiple correspondence analysis (MCA). This index is a weighted Euclidean distance where weightings are based on the "importance" of each MCA axis, and variable categories were considered to be associated when their distances were below the tolerance distance. This approach was applied to a renal transplantation data. The analysed variables were allograft survival and 13 of its putative predictors. A bootstrap-based stability analysis was employed for assessing result reliability. The method identified previously detected associations within the database, such as that between race of donors and recipients, and that between HLA match and Cyclosporine use. A hierarchical clustering algorithm was also applied to the same data, allowing for interpretations similar to those based on MCA. The defined tolerance distance could thus be used as an index of "closeness" in MCA, hence decreasing the subjectivity of interpreting MCA results.

Artificial neural networks for infant mortality modelling.

This work aims to investigate a simple to use and easy to interpret methodology for assessing the relative importance of input variables in artificial neural networks (ANNs) applied to epidemiological modelling. The independent variables were 43 variables of the social, economic, environmental and health sector of 59 Brazilian municipalities, and the outcomes were infant mortality rates from these municipalities. Two assays were developed for the ANN modelling. On the first, all 43 variables were taken as input; and on the second, input variables were chosen with the help of factor analysis (FA). The relative importance of the input variables was investigated by means of bootstrap replications of the ANN model on the second assay. Further, multiple linear regression models (LRMs) were developed with the same data set and compared to the ANN models. The FA analysis allowed the selection of eight variables for the second assay. The percent of explained variance R(2) on the ANNs was in the range 0.74-0.80, while linear models had R(2)=0.4-0.5. These findings were validated by the bootstrap replications, in which the ANN models remained with higher R(2) and lower mean square error than the LRMs. The analysis of the best (second) ANN model indicated the highest ranking of importance for the variables literacy, agricultural and livestock sector jobs, number of commercial establishments and telephones. The approach presented here successfully integrated a data-oriented model with expert knowledge, indicating the potentiality of ANN modelling in the prediction, planning and assessment of public health actions.

Overweight and risk of overweight in schoolchildren in the city of Rio de Janeiro, Brazil: prevalence and characteristics.

The association of overweight and risk of overweight with socio-economic and demographic variables was investigated among schoolchildren in the city of Rio de Janeiro, Brazil between 1995 and 1996. Data were obtained by direct interview and physical examination of boys and girls aged between 6 and 11 years in a two-stage, random sample, population-based survey. Univariate analysis was performed on the association between socio-economic and demographic predictors of overweight/risk of overweight and the dependent variables, i.e. body mass index (BMI), triceps skinfold and subscapular skinfold. Based on this analysis, logistic regression models were developed. The prevalence of overweight and risk of overweight was 37.8% in girls and 36.4% in boys. For the BMI model, the variables retained were age and number of persons per household; for the triceps skinfold model, the variables were age, gender and area of residence; and age, gender and persons/household were the variables for the subscapular skinfold model. The results suggest that both BMI and skinfold indicators should be used to assess overweight/risk of overweight and that public health programmes for schoolchildren should be developed to combat the alarming increase in obesity.