A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Current state of knowledge on neuroendocrine small bowel tumours: non-systematic review of the literature based on one case.

More than 60% of neuroendocrine tumours, also called carcinoids, are localised within the gastrointestinal tract. Small bowel neuroendocrine tumours have been diagnosed with increasing frequency over the past 35 years, being the second most frequent tumours of the small intestine. Ileal neuroendocrine tumours diagnosis is late because patients have non-specific symptoms. We have proposed to illustrate as an example the case of a patient, and on its basis, to make a brief review of the literature on small bowel neuroendocrine tumours, resuming several recent changes in the field, concerning classification criteria of these tumours and new recommendations and current advances in diagnosis and treatment. This patient came to our emergency department with a complete bowel obstruction, along with a 2-year history of peristaltic abdominal pain, vomits and diarrhoea episodes. During emergency laparotomy, an ileal stricture was observed, that showed to be a neuroendocrine tumour of the small bowel.

Analysis of the association between chondrocalcinosis and osteoarthritis: a community based study

To analyse the association between chondrocalcinosis and osteoarthritis (OA) of the hands and knees in an unselected elderly rural population. METHODS--A community based cross sectional study was performed in individuals randomly selected from a previous epidemiological survey on the prevalence of chondrocalcinosis in people older than 60 years from Osona county, Catalonia, northeastern Spain. Radiological OA (grade 2 or more of Kellgren's classification) was evaluated in 26 individuals with chondrocalcinosis and in 104 controls. A total of 18 articular areas of both knees (medial and lateral tibiofemoral compartments) and hands (first, second and third metacarpophalangeal (MCP), first carpometacarpal, trapezium-scaphoid, radiocarpal and distal radioulnar joints) were studied. RESULTS--Radiological changes of OA in the knees were more common in subjects with chondrocalcinosis than in those without it, with an odds ratio adjusted for age and gender (aOR) of 4.3 (95% confidence interval (CI) 1.6 to 11.8, p = 0.005). OA was also more frequent in almost all areas of the hands in individuals with chondrocalcinosis, though the difference reached statistical significance only in the MCP joints (aOR 3.1; 95% CI 1.1 to 8.8; p = 0.033). However, taking into account the side and the different joint compartments analysed, the association between chondrocalcinosis and OA was significant only in the lateral tibiofemoral compartment and the left MCP joints. CONCLUSIONS--In an elderly population unselected for their rheumatic complaints, there was a real association between OA and chondrocalcinosis. This association was particularly relevant in the lateral tibiofemoral compartment of the knee and in the first three left MCP joints.

Serrated polyps of the colon: how reproducible is their classification?

For several years, the lack of consensus on definition, nomenclature, natural history, and biology of serrated polyps (SPs) of the colon has created considerable confusion among pathologists. According to the latest WHO classification, the family of SPs comprises hyperplastic polyps (HPs), sessile serrated adenomas/polyps (SSA/Ps), and traditional serrated adenomas (TSAs). The term SSA/P with dysplasia has replaced the category of mixed hyperplastic/adenomatous polyps (MPs). The present study aimed to evaluate the reproducibility of the diagnosis of SPs based on currently available diagnostic criteria and interactive consensus development. In an initial round, H&E slides of 70 cases of SPs were circulated among participating pathologists across Europe. This round was followed by a consensus discussion on diagnostic criteria. A second round was performed on the same 70 cases using the revised criteria and definitions according to the recent WHO classification. Data were evaluated for inter-observer agreement using Kappa statistics. In the initial round, for the total of 70 cases, a fair overall kappa value of 0.318 was reached, while in the second round overall kappa value improved to moderate (kappa = 0.557; p < 0.001). Overall kappa values for each diagnostic category also significantly improved in the final round, reaching 0.977 for HP, 0.912 for SSA/P, and 0.845 for TSA (p < 0.001). The diagnostic reproducibility of SPs improves when strictly defined, standardized diagnostic criteria adopted by consensus are applied.

First consumption ever of multiple substances: applying an expert-based taxonomy to a Swiss national sample of adolescents.

The use of multiple legal and illegal substances by adolescents is a growing concern in all countries, but since no consensus about a taxonomy did emerge yet, it is difficult to understand the different patterns of consumption and to implement tailored prevention and treatment programs directed towards specific subgroups of the adolescent population. Using data from a Swiss survey on adolescent health, we analyzed the age at which ten legal and illegal substances were consumed for the first time ever by applying a method combining the strength of both automatic clustering and use of substance experts. Results were then compared to 30 socio-economic factors to establish the usefulness of and to validate our taxonomy. We also analyzed the succession of substance first use for each group. The final taxonomy consists of eight groups ranging from non-consumers to heavy drug addicts. All but four socio-economic factors were significantly associated with the taxonomy, the strongest associations being observed with health, behavior, and sexuality factors. Numerous factors influence adolescents in their decision to first try substances or to use them on a regular basis, and no factor alone can be considered as an absolute marker of problematic behavior regarding substance use. Different processes of experimentation with substances are associated with different behaviors, therefore focusing on only one substance or only one factor is not efficient. Prevention and treatment programs can then be tailored to address specific issues related to different youth subgroups.

Zonation for 3D aquifer characterization based on joint inversions of multimethod crosshole geophysical data

Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petro-physical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine the parameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petro-physical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease.

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

GIS-based debris flow source and runout susceptibility assessment from DEM data - a case study in NW Nicaragua

In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Tire traces: discrimination and classification of pyrolysis-GC/MS profiles

Tire traces can be observed on several crime scenes as vehicles are often used by criminals. The tread abrasion on the road, while braking or skidding, leads to the production of small rubber particles which can be collected for comparison purposes. This research focused on the statistical comparison of Py-GC/MS profiles of tire traces and tire treads. The optimisation of the analytical method was carried out using experimental designs. The aim was to determine the best pyrolysis parameters regarding the repeatability of the results. Thus, the pyrolysis factor effect could also be calculated. The pyrolysis temperature was found to be five time more important than time. Finally, a pyrolysis at 650 °C during 15 s was selected. Ten tires of different manufacturers and models were used for this study. Several samples were collected on each tire, and several replicates were carried out to study the variability within each tire (intravariability). More than eighty compounds were integrated for each analysis and the variability study showed that more than 75% presented a relative standard deviation (RSD) below 5% for the ten tires, thus supporting a low intravariability. The variability between the ten tires (intervariability) presented higher values and the ten most variant compounds had a RSD value above 13%, supporting their high potential of discrimination between the tires tested. Principal Component Analysis (PCA) was able to fully discriminate the ten tires with the help of the first three principal components. The ten tires were finally used to perform braking tests on a racetrack with a vehicle equipped with an anti-lock braking system. The resulting tire traces were adequately collected using sheets of white gelatine. As for tires, the intravariability for the traces was found to be lower than the intervariability. Clustering methods were carried out and the Ward's method based on the squared Euclidean distance was able to correctly group all of the tire traces replicates in the same cluster than the replicates of their corresponding tire. Blind tests on traces were performed and were correctly assigned to their tire source. These results support the hypothesis that the tested tires, of different manufacturers and models, can be discriminated by a statistical comparison of their chemical profiles. The traces were found to be not differentiable from their source but differentiable from all the other tires present in the subset. The results are promising and will be extended on a larger sample set.

Treatment implications of the emerging molecular classification system for melanoma.

Melanoma is an aggressive disease with few standard treatment options. The conventional classification system for this disease is based on histological growth patterns, with division into four subtypes: superficial spreading, lentigo maligna, nodular, and acral lentiginous. Major limitations of this classification system are absence of prognostic importance and little correlation with treatment outcomes. Recent preclinical and clinical findings support the notion that melanoma is not one malignant disorder but rather a family of distinct molecular diseases. Incorporation of genetic signatures into the conventional histopathological classification of melanoma has great implications for development of new and effective treatments. Genes of the mitogen-associated protein kinase (MAPK) pathway harbour alterations sometimes identified in people with melanoma. The mutation Val600Glu in the BRAF oncogene (designated BRAF(V600E)) has been associated with sensitivity in vitro and in vivo to agents that inhibit BRAF(V600E) or MEK (a kinase in the MAPK pathway). Melanomas arising from mucosal, acral, chronically sun-damaged surfaces sometimes have oncogenic mutations in KIT, against which several inhibitors have shown clinical efficacy. Some uveal melanomas have activating mutations in GNAQ and GNA11, rendering them potentially susceptible to MEK inhibition. These findings suggest that prospective genotyping of patients with melanoma should be used increasingly as we work to develop new and effective treatments for this disease.

What's so special about conversion disorder? A problem and a proposal for diagnostic classification.

Conversion disorder presents a problem for the revisions of DSM-IV and ICD-10, for reasons that are informative about the difficulties of psychiatric classification more generally. Giving up criteria based on psychological aetiology may be a painful sacrifice but it is still the right thing to do.