Coastal zone management in the fisheries sector program

International audienceThis paper presents an analysis of censored survival data for breast cancer specific mortality and disease-free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit of using the neural network framework especially for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing with the mortality model also compared with two other prognostic models called TNG and the NPI rule model. Further verification was carried out by comparing marginal estimates of the predicted and actual cumulative hazards. It was also observed that doctors seem to treat mortality and disease-free models as equivalent, so a further analysis was performed to observe if this was the case. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model. This paper extends the existing OSRE methodology to data sets that include continuous-valued variables

Why don’t cancer patients enter clinical trials? A review

Despite wide agreement with the usefulness of clinical trials, the proportion of patients who are enrolled in such trials is often considered as too low. We performed a comprehensive search of the literature from 1980 to the present to review the current data on barriers and facilitators to the development of multicenter clinical trials. Of 364 articles initially identified, we selected 35 articles and one book which main purpose was to assess the reasons of physicians’ and/or patients’ participation in clinical trials. Our review underlines that physicians play a key-role in the development and non-development of clinical trials. More studies – in particular outside the United States – are needed to better understand physicians’ attitudes towards clinical trials. They should combine multivariate analyses and comparative approaches in order to associate physicians’ behaviours with their individual characteristics, with the organisational context of their working environment and with the health care system

The medical treatment of metastatic renal cell cancer in the elderly: position paper of a SIOG Taskforce.

Treatments currently recommended for metastatic renal cell cancer (mRCC) have not been evaluated specifically in elderly patients. Here we consider what may be learned by analysing according to age the efficacy and toxicity data from key phase III trials of the targeted agents sorafenib (Nexavar), sunitinib (Sutent), temsirolimus (Torisel), and bevacizumab (Avastin), and from a study of expanded access to sunitinib and sorafenib. This paper represents the first systematic review of the role of targeted agents specifically in the elderly population. Retrospective subgroup analyses of clinical trial data cannot be considered definitive. However, they suggest in general that the progression-free and overall survival benefits seen in mRCC patients aged 65 years and over are similar to those in the younger age group. The frequency of major toxicities in elderly patients treated with targeted agents is no greater than in younger patients, although such toxicities may have greater impact on the quality of life. That said, no meaningful data are available for patients over 85 years. To confirm and extend these conclusions, prospective studies should be undertaken in the elderly to determine whether recommendations made for the wider mRCC population apply equally to this group of patients in whom comorbidities, comedication and the greater impact of low-grade toxicity may influence the efficacy and tolerability of treatment. Such studies are increasingly needed, given the growing number of elderly people and their rising life expectancy. Meanwhile, when considering the most appropriate drug to use in a particular patient, the toxicity profiles of the individual targeted agents--and any implications for specific comorbid conditions--should be taken into account.Journal ArticleResearch Support, Non-U.S. Gov'tReviewinfo:eu-repo/semantics/publishe

Breast cancer predictions bu neural networks analysis : a comparison with logistic regression

National audienceThis paper presents an exploratory fixed time study to identify the most significant covariates as a precursor to a longitudinal study of specific mortality and disease free survival. The data comprise 2535 consecutive patients diagnosed with primary breast cancer and entered into the study between 1996 and 2004, at a single French clinical center, Centre Léon Bérard, Lyon, France, where they received standard treatment. The patients were selected with T0-T4, N0-N1, M0 following the TNM staging methodology

Breast Cancer Predictions by Neural Networks Analysis : a Comparison with Logistic Regression

International audienceThis paper presents an exploratory fixed time study to identify the most significant covariates as a precursor to a longitudinal study of specific mortality, disease free survival and disease recurrences. The data comprise consecutive patients diagnosed with primary breast cancer and entered into the study from 1996 at a single French clinical center, Centre Léon Bérard, based in Lyons, where they received standard treatment. The methodology was to compare and contrast multi-layer perceptron neural networks (NN) with logistic regression (LR), to identify key covariates and their interactions and to compare the selected variables with those routinely used in clinical severity of illness indices for breast cancer. The Logistic regression in this work was chosen as an accepted standard for prediction by biostatisticians in order to evaluate the neural network. Only covariates available at the time of diagnosis and immediately following surgery were used. We used for comparison classification performance indices: AUROC (AREA Under Receiver-Operating Characteristics) curves, sensitivity, specificity, accuracy and positive predictive value for the two following events of interest: Specific Mortality, and Disease Free Survival

Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes

The aim of this study was to compare multilayer perceptron neural networks (NNs) with standard logistic regression (LR) to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS), and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC) in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort

Individualized dosing with axitinib: rationale and practical guidance

Axitinib is a potent, selective, vascular endothelial growth factor receptor inhibitor with demonstrated efficacy as second-line treatment for metastatic renal cell carcinoma. Analyses of axitinib drug exposures have demonstrated high interpatient variability in patients receiving the 5 mg twice-daily (b.i.d.) starting dose. Clinical criteria can be used to assess whether individual patients may benefit further from dose modifications, based on their safety and tolerability data. This review provides practical guidance on the 'flexible dosing' method, to help physicians identify who would benefit from dose escalations, dose reductions or continuation with manageable toxicity at the 5 mg b.i.d. dose. This flexible approach allows patients to achieve the best possible outcomes without compromising safety

Activity of cabozantinib in radioresistant brain metastases from renal cell carcinoma: two case reports

Abstract Background Renal cell carcinoma represents 3–5% of adult malignant tumors. Metastases are found in 30–40% of patients and brain metastases occurred in more than 10% of them. Despite significant progress in medical treatment, patients with brain metastases still have a limited survival. Cabozantinib, a tyrosine kinase inhibitor directed against vascular endothelial growth factor receptors, was recently registered for the treatment of metastatic renal cell carcinoma. Almost no data are, however, available on patients with brain metastases. Case presentation Case 1 is a 51-year-old man of North African origin; Case 2 is a 55-year-old European man. Case 1 and Case 2 had metastases of renal carcinoma at initial diagnosis and were treated with vascular endothelial growth factor receptors tyrosine kinase inhibitors. Case 1 had clear cell renal carcinoma and underwent nephrectomy; he then received several lines of tyrosine kinase inhibitor directed against vascular endothelial growth factor receptors and the mTor complex. During the second treatment a brain metastasis was diagnosed and treated with radiosurgery with rapid efficacy. Two years later he received nivolumab, an antibody directed against the programmed death-1 and programmed death-ligand 1 complex, but disease progression was observed with the reappearance of the brain metastasis together with neurologic symptoms. Cabozantinib was administered and induced a rapid clinical improvement as well as tumor regression in all sites including his brain. Sequencing of his tumor evidenced a mutation of the MET gene. Case 2 had a papillary renal carcinoma with brain metastases at time of diagnosis. After radiation of the brain tumors, a vascular endothelial growth factor receptor tyrosine kinase inhibitor was administered for 3 years. The disease was under control in all sites except in his brain; several new brain metastases requiring new radiation treatments developed. The disease finally progressed at all metastatic sites including his brain and he had several neurological symptoms. Cabozantinib was administered and rapidly induced a clinical improvement; a further computed tomography scan and brain magnetic resonance imaging showed significant tumor regressions. No MET gene mutation or amplification was observed in the tumor analysis. Conclusions These case reports indicate that cabozantinib was able, first, to reach brain tumors and second, to induce significant regressions in renal carcinoma brain metastases that were resistant to radiation as well as to previous systemic vascular endothelial growth factor receptor tyrosine kinase inhibitors