ACCURACY OF FNAC IN FEMALE BREAST LESIONS

Background: Various breast lesions are common lesions in females with a wide range of variability from inflammatory lesions, benign and malignant breast lesions. FNAC is first diagnostic test, as it has high sensitivity and specificity. Lesions were categorized on FNA into inflammatory lesions, benign neoplastic lesions, malignant neoplastic lesions, and suspicious for malignancy. Methods: This was a retrospective study done in the Department of Pathology, P.D.U. Medical College, Rajkot, Gujarat State, India from Aug-2013 to July 2014. FNAC of 392 cases of breast lesions were done and reported by expert pathologist. The histopathological specimens when available were reported by other pathologist without prior knowledge of FNA diagnosis. Sensitivity, Specificity and Accuracy of FNA diagnosis were then analyzed. Results: A total of 392 cases of breast lesions were diagnosed on FNA, out of them histopathological correlation was available in 87 cases. Benign breast lesions are more common in younger patients in 21-30 yrs age group and malignant lesions are more common in old age group patients of 41-60 yrs with few exceptions. In our setup fibroadenoma is the most common benign breast lesion (26.53%) and ductal carcinoma (17.86%) is the most common malignant lesion. The sensitivity, specificity and accuracy of FNAC for malignant lesions were found to be 91.43%, 100% and 96.25% respectively. Conclusion: FNAC is an effective and valid tool as the first line diagnostic modality in the preoperative diagnosis of the malignant and benign breast lesions.KEYWORDS: FNAC; Breast lesions; Fibroadenoma; Ductal carcinoma

ACCURACY OF FNAC IN FEMALE BREAST LESIONS

Background: Various breast lesions are common lesions in females with a wide range of variability from inflammatory lesions, benign and malignant breast lesions. FNAC is first diagnostic test, as it has high sensitivity and specificity. Lesions were categorized on FNA into inflammatory lesions, benign neoplastic lesions, malignant neoplastic lesions, and suspicious for malignancy. Methods: This was a retrospective study done in the Department of Pathology, P.D.U. Medical College, Rajkot, Gujarat State, India from Aug-2013 to July 2014. FNAC of 392 cases of breast lesions were done and reported by expert pathologist. The histopathological specimens when available were reported by other pathologist without prior knowledge of FNA diagnosis. Sensitivity, Specificity and Accuracy of FNA diagnosis were then analyzed. Results: A total of 392 cases of breast lesions were diagnosed on FNA, out of them histopathological correlation was available in 87 cases. Benign breast lesions are more common in younger patients in 21-30 yrs age group and malignant lesions are more common in old age group patients of 41-60 yrs with few exceptions. In our setup fibroadenoma is the most common benign breast lesion (26.53%) and ductal carcinoma (17.86%) is the most common malignant lesion. The sensitivity, specificity and accuracy of FNAC for malignant lesions were found to be 91.43%, 100% and 96.25% respectively. Conclusion: FNAC is an effective and valid tool as the first line diagnostic modality in the preoperative diagnosis of the malignant and benign breast lesions.KEYWORDS: FNAC; Breast lesions; Fibroadenoma; Ductal carcinoma

Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

“In small places, close to home”: urban environmental impacts on child rights across four global cities

Urban environments influence child behaviours, exposures and experiences and may affect health, development, achievement and realization of fundamental human rights. We examined the status of eleven UN Convention on the Rights of the Child articles, in a multi-case study across four global cities. Within all study cities, children experienced unequal exposure to urban environmental risks and amenities. Many violations of child rights are related to car-based transportation systems and further challenged by pressures on urban systems from rapid population increases in the context of climate change. A child rights framework provides principles for a collective, multi-sectoral re-imagination of urban environments that support the human rights of all citizens

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

Population-based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high-grade serous ovarian cancer

PURPOSE: The known epithelial ovarian cancer (EOC) susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting that other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes. METHODS: We sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases versus controls were further examined in an independent set of 14 135 EOC cases and 28 655 controls from the Ovarian Cancer Association Consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics. RESULTS: The ORs associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 to 5.68; p=0.00068), 1.99 for POLK (95% CI 1.15 to 3.43; p=0.014) and 4.07 for SLX4 (95% CI 1.34 to 12.4; p=0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 to 1.00, p=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive. CONCLUSIONS: We have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling

Population-based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high-grade serous ovarian cancer

Purpose: The known epithelial ovarian cancer (EOC) susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting that other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes. Methods: We sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases versus controls were further examined in an independent set of 14 135 EOC cases and 28 655 controls from the Ovarian Cancer Association Consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics. Results: The ORs associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 to 5.68; p=0.00068), 1.99 for POLK (95% CI 1.15 to 3.43; p=0.014) and 4.07 for SLX4 (95% CI 1.34 to 12.4; p=0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 to 1.00, p=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive. Conclusions: We have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling