Abstract:The new WHO/EORTC classification for cutaneous lymphomas comprises mature T-cell and natural killer (NK)-cell neoplasms, mature B-cell neoplasms, and immature hematopoietic malignancies. It reflects the unique features of lymphoproliferative diseases of the skin, and at the same time it is as compatible as possible with the concepts underlying the WHO classification for nodal lymphomas and the EORTC classification of cutaneous lymphomas. This article reviews the histological, phenotypical, and molecular genetic features of the various nosological entities included in this new classification. These findings always have to be interpreted in the context of the clinical features and biologic behavior.

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Cutaneous lymphomas represent a unique group of lymphomas and are the second most frequent extranodal lymphomas. As with other neoplasias, the pathogenesis is based mainly on a stepwise accumulation of mutations of suppressor genes and oncogenes caused by genetic, environmental or infectious factors. The diagnostic work-up includes clinical, histological, imaging and hematological investigations and in many cases immunohistochemical and molecular biological analyses. The current WHO/EORTC classification of cutaneous lymphomas differentiates "mature T-cell and NK-cell lymphomas", "mature B-cell lymphomas" and "immature hematopoietic malignancies", their variants and subgroups. It is compatible with the WHO classification for neoplasias of the hematopoietic and lymphoid tissue and respects the organ-specific peculiarities of primary cutaneous lymphomas. The assignment of the various types of cutaneous lymphomas into prognostic categories (pre-lymphomatous "abortive" disorders; definite malignant lymphomas of low-grade malignancy; definite malignant lymphomas of high-grade malignancy) provides essential information on the biological behavior and allows an appropriate planning of the therapeutic strategy, which may be topical or systemic and aggressive or non-aggressive. Besides the classical options for therapy, there are new and "experimental" strategies, the efficacy of which has to be studied in clinical trials.

Focal nonhemorrhagic lesion in the splenium of the corpus callosum in a patient with epilepsy treated with antiepileptic drugs was observed with MRI imaging. We have found only one such case during the past 2 years (series of MRI examinations of approximately 500 patients with various forms of epilepsy).

BackgroundGenomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images.ResultsWe developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach – a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available.ConclusionsThe framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1072-z) contains supplementary material, which is available to authorized users.