Proton Magnetic Resonance Spectroscopy (H-1 MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic...
详细信息
ISBN:
(纸本)9783319317441;9783319317434
Proton Magnetic Resonance Spectroscopy (H-1 MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex non-negative matrix factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.
The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is perf...
详细信息
The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast-enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow-up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast-enhancing regions with a blind-source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramer's V. We identified three distinct metabolic patterns-proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast-enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast-enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers. In a retrospective study with 31 glioblastoma patients 1 month after concomitant treatment, we identified three metabolic patterns with MRS, in contrast-enhancing regions with suspicions of progression. One pattern is proliferative, the second is necrotic, and the third has necrotic lipids and a polyunsaturated fatty acid peak. Although the patterns are associated with outcome 2 months later, they are not fully predictive due to intra-patient heterogeneity. The heterogeneity observed is not linked either to progression or to the contrast-enhanci
We propose a supervised learning approach to automatic quantification of cell populations in flow cytometric samples. One sample contains up to millions of measurement vectors with a dimensionality between 10 and 20. ...
详细信息
We propose a supervised learning approach to automatic quantification of cell populations in flow cytometric samples. One sample contains up to millions of measurement vectors with a dimensionality between 10 and 20. Normally, each measurement vector corresponds to a single cell in the biological sample. Identifying biologically meaningful cell populations is essentially a clustering problem, however, standard clustering methods are impractical, because size, shape and location of corresponding clusters may vary strongly between samples mainly due to phenotypic differences and inter-laboratory variations. In our holistic approach, we implicitly employ the structural information (such as relative locations and shape of sub-populations). A new input sample is reconstructed by a linear combination of artificial reference samples each represented by a Gaussian Mixture Model (GMM), in which for each Gaussian component the class label of the corresponding cluster of observations is known. The reference samples are calculated from a larger set of training samples by non-negativematrixfactorization and can be regarded as the basis of a lower dimensional feature space, in which input samples are reconstructed. We show a method for calculating the feature space transformation based on minimization the L-2 distance defined between two GMM. The feature space representation of the sample is then used to assign each observation to one of the specified sub-populations by a Bayes decision. We present classification results on a database of about 170 patients with Acute Lymphoblastic Leukemia (ALL), where high accuracy in the prediction of relatively small leukemic populations is crucial. The approach is not limited to our application. It can be employed wherever analysis of large, multi-dimensional, numerical data of a specific class of samples with related structure has to be performed. (C) 2016 Elsevier Ltd. All rights reserved.
暂无评论