A case report of perineal liposarcoma

Perineal localization of liposarcoma is extremely rare among all localization of soft tissue sarcoma. We report the case of 33-year-old man presenting a large perineal swelling.Throughout this case, we bring new insights into the surgical management difficulty of this tumor and we review the place of adjuvant therapy and prognosis factors. Keywords: Dedifferentiated liposarcoma, Liposarcoma prognosis, Perineu

3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach.

International audienceThis study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image)

Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence

International audienceFactor Analysis of Medical Image Sequences (FAMIS) is recognized as one pioneer successfully used approach for analyzing especially dynamic images' sequence for estimating kinetics and associated compartments having a physiological meaning. Some studies tried to extend the exploring of this approach to analyze Positron Emission Tomography (PET) image modality for dynamic sequences. PET images with 18F-fluorodesoxyglucose (18F-FDG) is the gold standard for in vivo, evaluation of tumor glucose metabolism and is widely used in clinical oncology. In this paper, a novel approach is proposed to obtain an automated quantification method for early accumulation of 18F-FDG tracer in order to explore breast cancer, by applying FAMIS tool on dynamic first pass 18F-FDG PET dynamic sequences. This approach starts by an automated identification of a tumor Region of Interest (ROI) from PET dynamic images' sequence. Then, a FAMIS approach is applied to separate two compartments: one compartment is associated to the vascular and a second one is associated to the purely tumor compartment. The latter allows the evaluation of the temporal evolution of the glucose tracer metabolism and therefore for pursuing cancer characterization. A new empiric parameter KFPQ (First Pass Quantification), computed from the evolution of the 18F-FDG radiotracer accumulation using the first 11 min PET early images, is proposed. This parameter is found to be correlated to standardized uptake value maximal index (SUVmax) metabolism tumor. The proposed framework is tested using image sequences' database for 25 different pathology cases, which is considered as largely sufficient by the clinical team. Among clinicians' experience, using a large dataset permits the possibility to obtain accurate information and precise early diagnosis. Pearson correlation coefficient is computed to evaluate as well as to analyze the relationship between the proposed empiric parameter KFPQ and glucose tracer metabolism SUVmax for the overall pathology cases. KFPQ is successfully evaluated by the dynamic first-pass 18F-FDG PET image sequences for exploring early breast cancer diagnosis. Quantitative evaluations, as discussed and validated by clinicians, confirmed the efficiency of the modeling and the usefulness of the new empiric parameter KFPQ to predict tumor glucose metabolism for early uptake. This can be considered as a significant indication for quantification as well as evaluation of early relapse and disease progression during the therapy

Factor Analysis-based Approach for Early Uptake Automatic Quantification of Breast Cancer by 18F-FDG PET Image Sequence.

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