Prognosis in Duke's B colorectal carcinoma: the Jass classification revisited.

OBJECTIVE: To assess whether Jass staging enhances prognostic prediction in Dukes' B colorectal carcinoma. DESIGN: A historical cohort observational study. SETTING: A university tertiary care centre, Switzerland. SUBJECTS: 108 consecutive patients. INTERVENTIONS: Curative resection of Dukes' B colorectal carcinoma between January 1985 and December 1988, Patients with familial adenomatous polyposis; hereditary non-polyposis colorectal cancer; Crohns' disease; ulcerative colitis and synchronous and recurrent tumours were excluded. A comparable group of 155 consecutive patients with Dukes' C carcinoma were included for reference purposes. MAIN OUTCOME MEASURES: Disease free and overall survival for Dukes' B and overall survival for Dukes' C tumours. RESULTS: Dukes' B tumours in Jass group III or with an infiltrated margin had a significantly worse disease-free survival (p = 0.001 and 0.0001, respectively) and those with infiltrated margins had a significantly worse overall survival (p = 0.002). Overall survival among those with Dukes' B Jass III and Dukes' B with infiltrated margins was no better than overall survival among all patients with Dukes' C tumours. CONCLUSION: Jass staging and the nature of the margin of invasion allow patients undergoing curative surgery for Dukes' B colorectal carcinoma to be separated into prognostic groups. A group of patients with Dukes' B tumours whose prognosis is inseparable from those with Dukes' C tumours can be identified, the nature of the margin of invasion being used to classify a larger number of patients.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Rapid marine recovery after the end-Permian mass-extinction event in the absence of marine anoxia

A new Early Triassic marine fauna is described from the Central Oman Mountains. The fauna is Griesbachian in age, on the basis of abundant conodonts and ammonoids, and was deposited in an oxygenated seamount setting off the Arabian platform margin. It is the first Griesbachian assemblage from a well-oxygenated marine setting and thus provides a test for the hypothesis that widespread anoxia prevented rapid recovery. The earliest Griesbachian (parvus zone) contains a low-diversity benthic fauna dominated by the bivalves Promyalina and Claraia. A similar level of recovery characterizes the immediate postextinction interval worldwide. However, the middle upper Griesbachian sedimentary rocks (isarcica and catinata zones) contain an incredibly diverse benthic fauna of bivalves, gastropods, articulate brachiopods, a new undescribed crinoid, echinoids, and ostracods. This fauna is more diverse and ecologically complex than the typical middle to late Griesbachian faunas described from oxygen-restricted settings worldwide. The level of postextinction recovery observed in the Oman fauna is not recorded elsewhere until the Spathian. These data support the hypothesis that the apparent delay in recovery after the end-Permian extinction event was due to widespread and prolonged benthic oxygen restriction: in the absence of anoxia, marine recovery is much faster.

Sediment budget monitoring of a debris-flow torrent (French Prealps)

The Manival near Grenoble (French Prealps) is a very active debris-flow torrent equipped with a large sediment trap (25 000 m3) protecting an urbanized alluvial fan from debris-flows. We began monitoring the sediment budget of the catchment controlled by the trap in Spring 2009. Terrestrial laser scanner is used for monitoring topographic changes in a small gully, the main channel, and the sediment trap. In the main channel, 39 cross-sections are surveyed after every event. Three periods of intense geomorphic activity are documented here. The first was induced by a convective storm in August 2009 which triggered a debris-flow that deposited ~1,800 m3 of sediment in the trap. The debris-flow originated in the upper reach of the main channel and our observations showed that sediment outputs were entirely supplied by channel scouring. Hillslope debris-flows were initiated on talus slopes, as revealed by terrestrial LiDAR resurveys; however they were disconnected to the main channel. The second and third periods of geomorphic activity were induced by long duration and low intensity rainfall events in September and October 2009 which generate small flow events with intense bedload transport. These events contribute to recharge the debris-flow channel with sediments by depositing important gravel dunes propagating from headwaters. The total recharge in the torrent subsequent to bedload transport events was estimated at 34% of the sediment erosion induced by the August debris-flow.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Current Issues in the Diagnosis and Classification of Peripheral T-Cell Lymphomas

Mature T-cell and T/NK-cell neoplasms are both uncommon and heterogeneous, among the broad category of non-Hodgkin's lymphomas. Due to the lack of specific genetic alterations in the vast majority of cases, most currently defined entities show overlapping morphologic and immunophenotypic features and therefore pose a challenge to the diagnostic pathologist. The goal of the symposium is to address current criteria for the recognition of specific subtypes of T-cell lymphoma, and to highlight new data regarding emerging immunophenotypic or molecular markers. This activity has been designed to meet the needs of practicing pathologists, and residents and fellows enrolled in training programs in anatomic and clinical pathology. It should be a particular benefit to those with an interest in hematopathology. Upon completion of this activity, participants should be better able to: -To be able to state the basis for the classification of mature T-cell malignancies involving nodal and extranodal sites. -To recognize and accurately diagnose the various subtypes of nodal and extranodal peripheral T-cell lymphomas. -To utilize immunohistochemical and molecular tests to characterize atypical T-cell proliferations. -To recognize and accurately diagnose T-cell lymphoproliferative lesions involving the skin and gastrointestinal tract, and be able to provide guidance regarding their clinical aggressiveness and management -To be able to utilize flow cytometric data to identify diverse functional T-cell subsets.

Sediment budget monitoring of debris-flow and bedload transport in the Manival Torrent, SE France

Steep mountain catchments typically experience large sediment pulses from hillslopes which are stored in headwater channels and remobilized by debris-flows or bedload transport. Event-based sediment budget monitoring in the active Manival debris-flow torrent in the French Alps during a two-year period gave insights into the catchment-scale sediment routing during moderate rainfall intensities which occur several times each year. The monitoring was based on intensive topographic resurveys of low- and high-order channels using different techniques (cross-section surveys with total station and high-resolution channel surveys with terrestrial and airborne laser scanning). Data on sediment output volumes from the main channel were obtained by a sediment trap. Two debris-flows were observed, as well as several bedload transport flow events. Sediment budget analysis of the two debris-flows revealed that most of the debris-flow volumes were supplied by channel scouring (more than 92%). Bedload transport during autumn contributed to the sediment recharge of high-order channels by the deposition of large gravel wedges. This process is recognized as being fundamental for debris-flow occurrence during the subsequent spring and summer. A time shift of scour-and-fill sequences was observed between low- and high-order channels, revealing the discontinuous sediment transfer in the catchment during common flow events. A conceptual model of sediment routing for different event magnitude is proposed.