Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Contains fulltext : 206059.pdf (publisher's version ) (Open Access

El narcoperiodismo de García Márquez: uma análise dos aspectos da narcoliteratura no livro-reportagem Notícia de um sequestro

Desde os anos 1970, a cobertura da mídia tradicional sobre o narcotráfico caracterizou-se pela superficialidade de suas narrativas cujo processo impossibilita a profundidade de análise. Porém, alguns repórteres foram bem-sucedidos ao aproximar o narcotráfico e o jornalismo literário, rompendo com essa barreira limitante, principalmente, a partir da produção de livros-reportagem. O tema influenciou a literatura do continente (originando termos como narcoliteratura, narconarrativa e narcocultura), bem como o contexto do tráfico de drogas proporcionou a produção editorial de obras de não ficção, a partir dos final dos anos 80, atingindo o ápice nos anos 90 e 2000. Desta forma, este artigo discute o papel do livro-reportagem para a produção cultural da narcoliteratura, a partir de uma análise de seus aspectos dentro da obra jornalística Notícia de um sequestro (1996), de Gabriel García Márquez. O artigo está apoiado nos conceitos de livro-reportagem, de Edvaldo Pereira Lima e nas discussões sobre narcocultura, de Omar Rincón e de Diana Palaversich

Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound

Contains fulltext : 177954.pdf (publisher's version ) (Closed access)Our aim was to investigate whether Breast Imaging Reporting and Data System-Ultrasound (BI-RADS-US) lexicon descriptors can be used as imaging biomarkers to differentiate molecular subtypes (MS) of invasive ductal carcinoma (IDC) in automated breast ultrasound (ABUS). We included 125 IDCs diagnosed between 2010 and 2014 and imaged with ABUS at two institutes retrospectively. IDCs were classified as luminal A or B, HER2 enriched or triple negative based on reports of histopathologic analysis of surgical specimens. Two breast radiologists characterized all IDCs using the BI-RADS-US lexicon and specific ABUS features. Univariate and multivariate analyses were performed. A multinomial logistic regression model was built to predict the MSs from the imaging characteristics. BI-RADS-US descriptor margins and the retraction phenomenon are significantly associated with MSs (both p < 0.001) in both univariate and multivariate analysis. Posterior acoustic features and spiculation pattern severity were only significantly associated in univariate analysis (p < 0.001). Luminal A IDCs tend to have more prominent retraction patterns than luminal B IDCs. HER2-enriched and triple-negative IDCs present significantly less retraction than the luminal subtypes. The mean accuracy of MS prediction was 0.406. Overall, several BI-RADS-US descriptors and the coronal retraction phenomenon and spiculation pattern are associated with MSs, but prediction of MSs on ABUS is limited

Learning to detect lymphocytes in immunohistochemistry with deep learning

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From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Contains fulltext : 201136.pdf (Publisher’s version ) (Closed access

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Contains fulltext : 193420.pdf (publisher's version ) (Open Access)Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use

Brown bear research in Europe: a review of the data collected and their value for conservation

Europe is a mosaic of landscapes shaped by human presence and activity, nevertheless it still harbors ~18 000 brown bears, clustered in 10 populations. Brown bear management and conservation in Europe is carried out by national/regional governmental agencies and through the involvement of universities and research institutions, NGOs, protected areas’ administrations, and hunting associations. Due to their cultural and charismatic value, brown bears have been largely studied under various aspects of their biology, ecology, and management and much of this knowledge has been gathered through the collaboration of these various agents. Nevertheless, the data collected have been only partially used for research and published in the peer-reviewed literature and, therefore, may not be fully available to the scientific community and conservation practitioners. In addition, data collection and research efforts are often fragmented at an administrative level, and can be sparse and scattered or even lacking in some parts of the species range due to limited funding. Here we summarize the ecological data collected for the 10 brown bear populations in Europe, focusing primarily on movement, genetic, demographic, and diet data. We report the type of data collected, the methods as well as the geographic and temporal coverage of the data collection, the overall sample sizes, if the data were obtained at the population or country/regional level, what the data have been used for, and if results have been published. We use this information first to reveal the substantial amount of valuable data collected for the brown bear throughout Europe. Second, we identify existing gaps in knowledge and data collection and prioritize future efforts needed as well as areas where research funding may be more urgent. Third, we highlight the potentials for integrating the overall knowledge so far collected for improving understanding of brown bear ecology in the human-dominated European landscape and implementing more effective management and conservation planning