Place du pathologiste dans la prise en charge néoadjuvante des cancers du sein [Neoadjuvant treatment of breast cancer: implications for the pathologist].

These past few years, neoadjuvant strategy has taken an increasing place in the management of breast cancer patients. This strategy is mainly indicated to obtain a tumour bulk regression allowing a breast conserving surgery in patients that otherwise would have undergone mastectomy. Of note, development of new chemotherapy agents and targeted therapies has critically helped in the progress of neoadjuvant strategy as it is currently associated with better pathological response rates. In this context, the pathologist is at the crossroad of this multidisciplinary process. First, he provides on the initial core needle biopsy the tumour pathological characteristics that are critical for the choice of treatment strategy, i.e. histological type, histological grade, proliferative activity (mitotic count and Ki67/MIB1 index labeling), hormone receptor status (oestrogen receptor and progesterone receptor) and HER2 status. Secondly, the pathologist evaluates the pathological response and the status of surgical margins with regards to the residual tumour on the surgical specimen after neoadjuvant treatment. These parameters are important for the management of the patient, since it has been shown that complete pathological response is associated with improved disease free survival. Several grading systems are used to assess the pathological response in breast and axillary lymph nodes. The most frequently used in France are currently the systems described by Sataloff et al. and Chevallier et al. In this review, we detail the different steps involving the pathologist in neoadjuvant setting, with special regards to the quality process and future perspectives such as emerging predictive biomarkers.

Predictive accuracy of original and recalibrated Framingham risk score in the Swiss population.

OBJECTIVE: To compare the predictive accuracy of the original and recalibrated Framingham risk function on current morbidity from coronary heart disease (CHD) and mortality data from the Swiss population. METHODS: Data from the CoLaus study, a cross-sectional, population-based study conducted between 2003 and 2006 on 5,773 participants aged 35-74 without CHD were used to recalibrate the Framingham risk function. The predicted number of events from each risk function were compared with those issued from local MONICA incidence rates and official mortality data from Switzerland. RESULTS: With the original risk function, 57.3%, 21.2%, 16.4% and 5.1% of men and 94.9%, 3.8%, 1.2% and 0.1% of women were at very low (<6%), low (6-10%), intermediate (10-20%) and high (>20%) risk, respectively. With the recalibrated risk function, the corresponding values were 84.7%, 10.3%, 4.3% and 0.6% in men and 99.5%, 0.4%, 0.0% and 0.1% in women, respectively. The number of CHD events over 10 years predicted by the original Framingham risk function was 2-3 fold higher than predicted by mortality+case fatality or by MONICA incidence rates (men: 191 vs. 92 and 51 events, respectively). The recalibrated risk function provided more reasonable estimates, albeit slightly overestimated (92 events, 5-95th percentile: 26-223 events); sensitivity analyses showed that the magnitude of the overestimation was between 0.4 and 2.2 in men, and 0.7 and 3.3 in women. CONCLUSION: The recalibrated Framingham risk function provides a reasonable alternative to assess CHD risk in men, but not in women.

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

Multimodal Strategies in Development.Predictive Value of early simultaneosgesture-speech comination

The present study investigates the predictive value of the early appearance of simultaneous pointing-speech combinations. An experimental task was used to obtain a communicative productive sample from nineteen children at 1;0 and 1;3. Infant’s communicative productions, in combination with gaze joint engagement patterns, were analyzed in relation to different social conditions. The results show a significant effect of age and social condition on infants’ communicative productions. Gesture-speech combinations seem to work as a strong communicative resource to attract the adult’s attention in social demanding communicative contexts. Gaze joint engagement was used in combination with simultaneous pointing-speech combinations to attract adults’ attention during social demanding conditions. Finally, the use of simultaneous pointing-speech combinations at 1;0 in demanding conditions predicted greater expressive vocabulary acquisition at 1;3 and 1;6. These results indicate that the use of gesture-speech combinations may be considered a significant step towards the early integration of language components.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Predictive value of the MGMT promoter methylation status in metastatic melanoma patients receiving first-line temozolomide plus bevacizumab in the trial SAKK 50/07.

The O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is a predictive parameter for the response of malignant gliomas to alkylating agents such as temozolomide. First clinical trials with temozolomide plus bevacizumab therapy in metastatic melanoma patients are ongoing, although the predictive value of the MGMT promoter methylation status in this setting remains unclear. We assessed MGMT promoter methylation in formalin-fixed, primary tumor tissue of metastatic melanoma patients treated with first-line temozolomide and bevacizumab from the trial SAKK 50/07 by methylation-specific polymerase chain reaction. In addition, the MGMT expression levels were also analyzed by MGMT immunohistochemistry. Eleven of 42 primary melanomas (26%) revealed a methylated MGMT promoter. Promoter methylation was significantly associated with response rates CR + PR versus SD + PD according to RECIST (response evaluation criteria in solid tumors) (p<0.05) with a trend to prolonged median progression-free survival (8.1 versus 3.4 months, p>0.05). Immunohistochemically different protein expression patterns with heterogeneous and homogeneous nuclear MGMT expression were identified. Negative MGMT expression levels were associated with overall disease stabilization CR + PR + SD versus PD (p=0.05). There was only a poor correlation between MGMT methylation and lack of MGMT expression. A significant proportion of melanomas have a methylated MGMT promoter. The MGMT promoter methylation status may be a promising predictive marker for temozolomide therapy in metastatic melanoma patients. Larger sample sizes may help to validate significant differences in survival type endpoints.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

A new early and automated MRI-based predictor of motor improvement after stroke.

OBJECTIVES: In this study, we investigated the structural plasticity of the contralesional motor network in ischemic stroke patients using diffusion magnetic resonance imaging (MRI) and explored a model that combines a MRI-based metric of contralesional network integrity and clinical data to predict functional outcome at 6 months after stroke. METHODS: MRI and clinical examinations were performed in 12 patients in the acute phase, at 1 and 6 months after stroke. Twelve age- and gender-matched controls underwent 2 MRIs 1 month apart. Structural remodeling after stroke was assessed using diffusion MRI with an automated measurement of generalized fractional anisotropy (GFA), which was calculated along connections between contralesional cortical motor areas. The predictive model of poststroke functional outcome was computed using a linear regression of acute GFA measures and the clinical assessment. RESULTS: GFA changes in the contralesional motor tracts were found in all patients and differed significantly from controls (0.001 ≤ p < 0.05). GFA changes in intrahemispheric and interhemispheric motor tracts correlated with age (p ≤ 0.01); those in intrahemispheric motor tracts correlated strongly with clinical scores and stroke sizes (p ≤ 0.001). GFA measured in the acute phase together with a routine motor score and age were a strong predictor of motor outcome at 6 months (r(2) = 0.96, p = 0.0002). CONCLUSION: These findings represent a proof of principle that contralesional diffusion MRI measures may provide reliable information for personalized rehabilitation planning after ischemic motor stroke. Neurology® 2012;79:39-46.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Suspicion d'hémorragie sous-arachnoïdienne: place d'un score prédictif dans la stratégie diagnostique actuelle [Usefulness of a predictive score in subarachnoid hemorrhage diagnosis].

Usefulness of a predictive score in subarachnoid hemorrhage diagnosis Nearly half of the patients with non-traumatic subarachnoid hemorrhage (SAH) present with no neurological signs, inducing clinical underestimation of the gravity of their affection. As the outcome of aneurismal SAH is highly dependant on the initial neurological status and the recurrence of untreated hemorrhagic events, these neurologically intact patients stand to suffer the most from delayed diagnosis. Although there is currently no validated predictive score that reliably identifies SAH-induced headache, a combination of clinical criteria derived from a cohort of sudden-onset headache patients should allow risk stratification and identification of those patients requiring further investigation.

Early predictors of outcome in comatose survivors of ventricular fibrillation and non-ventricular fibrillation cardiac arrest treated with hypothermia: a prospective study

OBJECTIVES: Current indications for therapeutic hypothermia (TH) are restricted to comatose patients with cardiac arrest (CA) due to ventricular fibrillation (VF) and without circulatory shock. Additional studies are needed to evaluate the benefit of this treatment in more heterogeneous groups of patients, including those with non-VF rhythms and/or shock and to identify early predictors of outcome in this setting. DESIGN: Prospective study, from December 2004 to October 2006. SETTING: 32-bed medico-surgical intensive care unit, university hospital. PATIENTS: Comatose patients with out-of-hospital CA. INTERVENTIONS: TH to 33 +/- 1 degrees C (external cooling, 24 hrs) was administered to patients resuscitated from CA due to VF and non-VF (including asystole or pulseless electrical activity), independently from the presence of shock. MEASUREMENTS AND MAIN RESULTS: We hypothesized that simple clinical criteria available on hospital admission (initial arrest rhythm, duration of CA, and presence of shock) might help to identify patients who eventually survive and might most benefit from TH. For this purpose, outcome was related to these predefined variables. Seventy-four patients (VF 38, non-VF 36) were included; 46% had circulatory shock. Median duration of CA (time from collapse to return of spontaneous circulation [ROSC]) was 25 mins. Overall survival was 39.2%. However, only 3.1% of patients with time to ROSC > 25 mins survived, as compared to 65.7% with time to ROSC < or = 25 mins. Using a logistic regression analysis, time from collapse to ROSC, but not initial arrest rhythm or presence of shock, independently predicted survival at hospital discharge. CONCLUSIONS: Time from collapse to ROSC is strongly associated with outcome following VF and non-VF cardiac arrest treated with therapeutic hypothermia and could therefore be helpful to identify patients who benefit most from active induced cooling.