A clinico-radiological and pathological profile of lung cancer patients presented to All India Institute of Medical Sciences (Patna)

Background: Lung cancer is one of the most common cancers and cause of cancer-related deaths worldwide. The clinicopathologicalprofile of lung cancer has shown marked regional and geographical variation. Majority of the patients have locallyadvanced or disseminated disease at presentation and are not candidates for surgery. Objective: The aim of this study was toevaluate the clinico-radiological and pathological profile of lung cancer patients and difference in histopathology betweensmoker and non-smoker. We also assessed yield of the various diagnostic procedures used for confirmation of lung cancer.Materials and Methods: A total of 30 patients diagnosed between May 1, 2016, and December 31, 2016. The completedemographic profile, smoking status, clinical, radiological, and diagnostic details were recorded in the study. Data were enteredand analyzed using SPSS software. Results: A total of 30 patients (19 male and 11 female) included in our study with mean age of55.26 years. Cough (80%) and dyspnea (80%) were the most common symptom and mass (86%), pleural effusion (53.3%) was themost common radiological presentation of patients. Clubbing and hemoptysis both was found only in 8 out of 30 (26%) patients.Adenocarcinoma (46.6%) was the most common histopathological type followed by squamous cell carcinoma (16.6%) and smallcell carcinoma (13.3%). The majority of patients (60%) presented in Stage 4. Computed tomography guided biopsy had better yieldin compare to ultrasonography guided (80% vs. 70.8%). Bronchoscopic procedure had lowest yield (38.8%). Conclusion: Theclinicopathological profile of lung cancer has changed in last few years, especially in the increase in adenocarcinoma incidence,and now it is the most common cause in both smokers and non-smoker

Editorial: Frontiers in malaria research

No abstract available

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press