Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

Background: Understanding the perceptions of patients regarding tuberculosis (TB) will enable better design of a comprehensive, client-oriented program for the disease. Methods: This study was conducted district-wise across India in 2015–2016 as part of the National Family Health Surveys (NFHS). Results: We discovered that the prevalence of TB remains significantly high, with quite a high percentage of people being unaware of the exact cause of disease proliferation. The majority of people believed that touching or sharing utensils can be a source of TB. This perception affected the participants’ responses about seeking diagnosis and treatment. However, it is a good sign that most people knew that TB is a curable disease that can be prevented to some extent if immunization with the Bacillus Calmette-Guérin (BCG) vaccine is done at the correct stage. So, a large section of the population had their children vaccinated. In addition, they would go for diagnosis if they had symptoms suggestive of the disease. Conclusion: Findings from this study are indicative of the fact that a large population is aware that health facilities can make a significant contribution to the treatment of tuberculosis. There is a need to further investigate how this information could potentially be used to enhance early seeking of appropriate services among TB patients.

Aim: The main objective is to incorporate the major foetal parameters – biparietal diameter, head circumference, abdominal circumference and femur length for prediction of gestational age through ultrasonography between 10th and 42nd weeks of gestation and try to do a simultaneous comparative study with gestational age predicted by last menstrual period. Methods: The study has been conducted particularly on the population of Bangladesh. It has been done on 229 Bangladeshi women who had usual singleton foetuses, with evidence of menstrual dates by sonography before fourteen weeks of gestation. Foetal anatomical structures have been scanned and measured at the time of sonographic inspection. For each patient, in addition to the four foetal parameters such as Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC) and Femur Length (FL), the other parameters like Gestational Age (GA) by Last Menstrual Period (LMP) as well as by Ultrasonography (USG) have been recorded. Here we have adopted non-linear regression models in order to predict the response on gestational age. Usually, different modelling methods have been used for this purpose. Results: The logarithmic models normally presented better results if gestational age was predicted based on a single parameter than polynomial models whereas if all predictor variables were considered together, then Nernst model may turn out to be the best one. Also, it was seen that the accuracy level of gestational age predicted by ultrasonography was slightly more accurate than that determined by last menstrual period. Conclusions: There is a high degree of association among the different foetal parameters. Further, there is a high degree of association between the gestational ages by LMP and that by USG. Prediction of gestational ages by USG technique gives a good degree of accuracy and hence can be a reliable technique for estimation of gestational ages.

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