Sensitivity of imaging for multifocal-multicentric breast carcinoma

<p>Abstract</p> <p>Background</p> <p>This retrospective study aims to determine: 1) the sensitivity of preoperative mammography (Mx) and ultrasound (US), and re-reviewed Mx to detect multifocal multicentric breast carcinoma (MMBC), defined by pathology on surgical specimens, and 2) to analyze the characteristics of both detected and undetected foci on Mx and US.</p> <p>Methods</p> <p>Three experienced breast radiologists re-reviewed, independently, digital mammography of 97 women with MMBC pathologically diagnosed on surgical specimens. The radiologists were informed of all neoplastic foci, and blinded to the original mammograms and US reports. With regards to Mx, they considered the breast density, number of foci, the Mx characteristics of the lesions and their BI-RADS classification. For US, they considered size of the lesions, BI-RADS classification and US pattern and lesion characteristics. According to the histological size, the lesions were classified as: index cancer, 2nd lesion, 3rd lesion, and 4th lesion. Any pathologically identified malignant foci not previously described in the original imaging reports, were defined as undetected or missed lesions. Sensitivity was calculated for Mx, US and re-reviewed Mx for detecting the presence of the index cancer as well as additional satellite lesions.</p> <p>Results</p> <p>Pathological examination revealed 13 multifocal and 84 multicentric cancers with a total of 303 malignant foci (282 invasive and 21 non invasive). Original Mx and US reports had an overall sensitivity of 45.5% and 52.9%, respectively. Mx detected 83/97 index cancers with a sensitivity of 85.6%. The number of lesions <it>un</it>detected by original Mx was 165/303. The Mx pattern of breasts with undetected lesions were: fatty in 3 (1.8%); scattered fibroglandular density in 40 (24.3%), heterogeneously dense in 91 (55.1%) and dense in 31 (18.8%) cases. In breasts with an almost entirely fatty pattern, Mx sensitivity was 100%, while in fibroglandular or dense pattern it was reduced to 45.5%. Re-reviewed Mx detected only 3 additional lesions. The sensitivity of Mx was affected by the presence of dense breast tissue which obscured lesions or by an incorrect interpretation of suspicious findings.</p> <p>US detected 73/80 index cancers (sensitivity of 91.2%), US missed 117 malignant foci with a mean tumor diameter of 6.5 mm; the sensitivity was 52.9%</p> <p>Undetected lesions by US were those smallest in size and present in fatty breast or in the presence of microcalcifications without a visible mass.</p> <p>US sensitivity was affected by the presence of fatty tissue or by the extent of calcification.</p> <p>Conclusion</p> <p>Mx missed MMBC malignant foci more often in dense or fibroglandular breasts. US missed small lesions in mainly fatty breasts or when there were only microcalcifications. The combined sensitivity of both techniques to assess MMBC was 58%. We suggest larger studies on multimodality imaging.</p

COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic

During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy. © 2022, The Author(s).U.S. Department of Education, ED: P031S190304; Texas A and M International University, TAMIU; National Research University Higher School of Economics, ВШЭThe COVIDiSTRESS Consortium would like to acknowledge the contributions of friends and collaborators in translating and sharing the COVIDiSTRESS survey, as well as the study participants. Data analysis was supported by Texas A&M International University (TAMIU) Research Grant, TAMIU Act on Ideas, and the TAMIU Advancing Research and Curriculum Initiative (TAMIU ARC) awarded by the US Department of Education Developing Hispanic-Serving Institutions Program (Award # P031S190304). Data collection by Dmitrii Dubrov was supported within the framework of the Basic Research Program at HSE University, RF

Determinação da densidade de empacotamento de sistemas granulares compostos a partir da areia normal do IPT: comparação entre modelos de otimização de distribuição granulométrica e composições aleatórias

Resumo O empacotamento de partículas em concretos e argamassas adquire cada dia mais importância no âmbito técnico, uma vez que muitas das propriedades dos materiais compósitos são influenciadas pelo índice de vazios e concentração de sólidos. Este trabalho teve o objetivo de comparar a densidade de empacotamento de agregados miúdos, cujas curvas granulométricas foram obtidas pelo uso de diferentes modelos de empacotamento de partículas. A areia normal brasileira, fornecida pelo IPT, foi utilizada para verificação experimental dos resultados teóricos obtidos pelo uso dos modelos. As densidades de empacotamento das curvas granulométricas foram calculadas através do modelo de empacotamento CPM (do inglês, compressible packing model). A máxima densidade de empacotamento foi buscada pelo uso de modelos de empacotamento que resultam em uma curva granulométrica ideal, fazendo-se valer de ensaios preliminares que indicavam uma maior densidade de empacotamento na fração grossa da areia, de 1,2 mm. Desta forma, além das curvas granulométricas ideais, fez-se também um estudo com composições aleatórias onde houve aumento progressivo de 5% na fração grossa da areia com a devida compensação nas frações mais finas. Utilizou-se, como referência, a curva granulométrica que representa a média dos limites inferior e superior da zona ótima recomendada pela norma NBR 7211. Os resultados obtidos mostraram que é possível aumentar a densidade de empacotamento através do uso de modelos de empacotamento de partículas e composições aleatórias, com relação à curva média da norma. Esse aumento foi da ordem de 2% e 5% para as densidades de empacotamento real e virtual, respectivamente. A verificação experimental dos resultados teóricos demonstrou que o modelo CPM infravalorou a densidade de empacotamento em aproximadamente 10%

Mapa temático de vegetação e população indígena do Estado do Rio Grande do Sul

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Mapa temático de vegetação e população indígena do Estado do Rio Grande do Sul

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