Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis.

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.

Local and Non-local Regularization Techniques in Emission (PET/SPECT) Tomographic Image Reconstruction Methods.

Emission tomographic image reconstruction is an ill-posed problem due to limited and noisy data and various image-degrading effects affecting the data and leads to noisy reconstructions. Explicit regularization, through iterative reconstruction methods, is considered better to compensate for reconstruction-based noise. Local smoothing and edge-preserving regularization methods can reduce reconstruction-based noise. However, these methods produce overly smoothed images or blocky artefacts in the final image because they can only exploit local image properties. Recently, non-local regularization techniques have been introduced, to overcome these problems, by incorporating geometrical global continuity and connectivity present in the objective image. These techniques can overcome drawbacks of local regularization methods; however, they also have certain limitations, such as choice of the regularization function, neighbourhood size or calibration of several empirical parameters involved. This work compares different local and non-local regularization techniques used in emission tomographic imaging in general and emission computed tomography in specific for improved quality of the resultant images.

Assessment of occupational exposure among pakistani medical staff during 2007-2011.

The data analysis of occupationally exposed medical workers in Nuclear Medicine (NM), Radiotherapy (RT) and Diagnostic Radiology (DR) at the Institute of Nuclear Medicine and Oncology (INMOL), Pakistan is presented for the time interval (2007-2011). The whole-body exposure doses of the workers were measured by using the Film Badge Dosimetry technique. The annual average effective doses in NM, RT and DR have been found well below the permissible annual limit of 20 mSv (averaged over a period of 5 consecutive years), with no over-exposure detected. This declining trend of annual average effective dose is the consequence of improved radiation protection practices at INMOL during the recent years.

Prediction of impacts on liver enzymes from the exposure of low-dose medical radiations through artificial intelligence algorithms.

OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute of Nuclear Medicine and Oncology Hospital. METHODS: Ninety workers from the Radiology and Nuclear Medicine departments were included. A high-capacity thermoluminescent was used for annual average effective radiation dose measurements. The liver function tests were conducted for all subjects and controls. Three supervised learning models (multilayer precentron; logistic regression; and random forest) were applied and cross-validated to predict any alteration in liver enzymes. The t-test was applied to see if subjects and controls were significantly different in liver function tests. RESULTS: The annual average effective doses were in the range of 0.07-1.15 mSv. Alanine transaminase was 50% high and aspartate transaminase was 20% high in radiation workers. There existed a significant difference (p=0.0008) in Alanine-aminotransferase between radiation-exposed and radiation-unexposed workers. Random forest model achieved 90-96.6% accuracies in Alanine-aminotransferase and Aspartate-aminotransferase predictions. The second best classifier model was the Multilayer perceptron (65.5-80% accuracies). CONCLUSION: As there is a need of regular monitoring of hepatic function in radiation-exposed people, our artificial intelligence-based predicting model random forest is proved accurate in prediagnosing alterations in liver enzymes.

Exploration of structural, thermal stability and band-gap tunability of organic and inorganic mixed cation (MA)1-xCsxPbBr3perovskite harvester via ultrasonication synthesis route.

Extensive investigation over the last few years has been done on halide based perovskite light harvester due to higher power conversion efficiency but the thermal stability with organic cation i.e. methylamine is challenging for the commercialization. Therefore, for improved structural and thermal stability, it is significant to develop a mixed cation base perovskite compound. To improve the thermal and structural stability of the material and easy synthesis method for industrialization of the material, we have demonstrated the compositional engineering of MA/CsPbBr3perovskite material via ultrasonication synthesis process. The x-ray diffraction, transmission electron microscope, diffuse reflectance spectrometer and simultaneous thermal analyzer (STA) analysis were performed in order to understand the impact of the Cs+into MAPbBr3perovskite structure. Structural study reveals that up to 40% Cs+incorporation into MAPbBr3has purePm-3mcubic phase of perovskite compound with continuously increase in micro strain and lattice contraction. On the other hand, with increasing the concentration of Cs+than MA+, optical band gap slightly increases. The thermodynamic behavior and thermal stability of the sample was studied with STA (differential scanning calorimetry/thermogravimetry). For the new generation optoelectronics with admirable stability, we believe that pure phase MA0.60Cs0.40PbBr3perovskite compound may be a promising candidate.

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies.

The purpose of this research was to provide a "systematic literature review" of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches-to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

Assessing liver proteins and enzymes of medical workers exposed to ionizing radiation (IR).

The cross-sectional study was conducted to examine hepatic function via liver enzymes/proteins assessments, along with the estimation of an inflammatory response from C-reactive protein (CRP)-which is a liver-synthesized protein. The liver function tests with aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP) and bilirubin (BBN), and CRP test were conducted for radiation-exposed workers-REW (n = 32) and radiation-unexposed workers-RUW (n = 21). The annual average effective doses (AAED) were measured from thermoluminescent dosimeter. A t test and bivariate correlation analyses were applied. Only one worker had a high AST value (50 U/L), one worker had a negligible high ALT value (43 U/L) and only one worker had a negligible high bilirubin value (1.3 g/dL). There were normal levels of CRP (up to 6 mg/L) in all individuals. There existed a nonsignificant difference (p < 0.050) between the mean values of liver enzymes and proteins in all exposed and unexposed workers. Nonsignificant weak correlations are reported in liver enzymes/proteins parameters: AST, ALT, ALP, BBN, CRP with the AAED range (whole-body: 0.91-3.39 mSv) during 2011-2015. The normal values of liver enzymes/proteins' (AST, ALT, ALP, BBN, CRP) values may ensure a good hepatic health of radiation-exposed medical workers with AAED range mentioned. We found that low ionizing radiation doses did not alter the liver function test parameters and did not affect the concentration of an inflammatory response protein, i.e., CRP.

Thirty Years Cancer Incidence Data for Lahore, Pakistan: Trends and Patterns 1984-2014

This research was conducted to generate trends and patterns of most common male and female cancers from 1984-2014 for the city population of Lahore Pakistan. Cancer incidence data gathered for different organs were processed through cleaning, integration, transformation, reduction and mining for ultimate representation. Risk of cancer appeared to be continuously increasing among both males and females. Overall, lymphomas and breast cancer are the most common neoplasm in males and females, respectively, in Lahore with almost the highest rates in the Asian Pacific region. The incidence of head and neck, brain, and lung cancers, as well as leukemia have rapidly increased among males, whereas, ovarian, cervix, head and neck and lymphomas have become more common among females. The present communication should be helpful for adequate strategic planning, identification of risk factors and taking appropriate prevention and control measures at the national level.

Prediction of impacts on liver enzymes from the exposure of low-dose medical radiations through artificial intelligence algorithms

SUMMARY OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute of Nuclear Medicine and Oncology Hospital. METHODS: Ninety workers from the Radiology and Nuclear Medicine departments were included. A high-capacity thermoluminescent was used for annual average effective radiation dose measurements. The liver function tests were conducted for all subjects and controls. Three supervised learning models (multilayer precentron; logistic regression; and random forest) were applied and cross-validated to predict any alteration in liver enzymes. The t-test was applied to see if subjects and controls were significantly different in liver function tests. RESULTS: The annual average effective doses were in the range of 0.07-1.15 mSv. Alanine transaminase was 50% high and aspartate transaminase was 20% high in radiation workers. There existed a significant difference (p=0.0008) in Alanine-aminotransferase between radiation-exposed and radiation-unexposed workers. Random forest model achieved 90-96.6% accuracies in Alanine-aminotransferase and Aspartate-aminotransferase predictions. The second best classifier model was the Multilayer perceptron (65.5-80% accuracies). CONCLUSION: As there is a need of regular monitoring of hepatic function in radiation-exposed people, our artificial intelligence-based predicting model random forest is proved accurate in prediagnosing alterations in liver enzymes.

Trends and Analysis of Cancer Incidence for Common Male and Female Cancers in the Population of Punjab Province of Pakistan during 1984 to 2014.

BACKGROUND: The Pakistan Atomic Energy Commission Cancer Registry (PAECCR) program has made availability of a common cancer incidence database possible in Pakistan. The cancer incidence data from nuclear medicine and oncology institutes were gathered and presented. MATERIALS AND METHODS: The cancer incidence data for the last 30 years (1984-2014) are included to describe a data set of male and female patients. The data analysis concerning occurrence, trends of common cancers in male and female patients, stage-wise distribution, and mortality/follow-up cases is also incorporated for the last 10 years (2004-2014). RESULTS: The total population of provincial capital Lahore is 9,800,000. The total number of cancer cases was 80,390 (males 32,156, females 48,134). The crude incidence rates in PAECCR areas were 580.8/105 during 2010 to 885.4/105 in 2014 (males 354.1/105, females 530.1/105). The cancer incidence rates for head and neck (15.70%), brain tumors (10.5%), and non-Hodgkin lymphoma (NHL, 9.53%) were found to be the highest in male patients, whereas breast cancer (46.7%), ovary tumors (6.80%), and cervix (6.31%) cancer incidence rates were observed to be the most common in female patients. The age range distribution of diagnosed and treated patients in conjunction with the percentage contribution of cancer patients from 15 different cities of Punjab province treated at the Institute of Nuclear Medicine and Oncology, Lahore are also included. Leukemia was found to be the most common cancer for the age group of 1-12 years. It has been identified that the maximum number of diagnosed cases were found in the age range of 51-60 years for males and 41-50 years for female cancer patients. CONCLUSIONS: Overall cancer incidence of the thirty years demonstrated that head and neck and breast cancers in males and in females respectively are the most common cancers in Punjab province in Pakistan, at rates almost the highest in Asia, requiring especial attention. The incidence of brain, NHL, and prostate cancers among males and ovarian and cervix cancers among females have increased rapidly. These data from a major population of Punjab province should be helpful for implementation of appropriate planning, prevention and cancer control measures and for determination of risk factors within the country.

Assessment of the occupational radiation exposure doses to workers at INMOL Pakistan (2007-11).

The assessment of occupationally exposed medical radiation workers at the Institute of Nuclear Medicine and Oncology (INMOL), Pakistan has been perfomed. The whole-body radiation exposure doses of 120 workers in nuclear medicine (NM), radiotherapy (RT) and diagnostic radiology (DR) were measured by using the film badge dosimetry technique for the time interval (2007-11) and their results presented. The annual average effective doses in NM, RT and DR were found to be well below the permissible annual limit of 20 mSv (averaged over a period of 5 consecutive y). The declining trend observed in the annual average dose values during the time interval (2007-11) is an indication of ameliorated radiation protection practices at INMOL, Pakistan.

Consistency and validity of self-reporting scores in stress measurement surveys.

Stress has been attributed to physiological and psychological demands that exceed the natural regulatory capacity of a person. Chronic stress is not only a catalyst for diseases such as hypertension, diabetes, insomnia but may also lead to social problems such as marriage breakups, suicide and violence. Objective assessment of stress is difficult so self-reports are commonly used to indicate the severity of stress. However, empirical information on the validity of self-reports is limited. The present study investigated the authenticity and validity of different self-report surveys. An analysis, based on a three-pronged strategy, was performed on these surveys. It was concluded that although subjects are prone to systematic error in reporting, self-reports can provide a useful substitute for data modeling specifically in stress evaluation where other objective assessments such as determination of stress using only physiological response are difficult.

An ANN model for treatment prediction in HBV patients.

Two types of antiviral treatments, namely, interferon and nucleoside/nucleotide analogues are available for hepatitis infections. The selection of drug and dose determined using known pharmacokinetics and pharmacodynamics data is important. The lack of sufficient information for pharmacokinetics of a drug may not produce the desired results. Artificial neural network (ANN) provides a novel model-independent approach to pharmacokinetics and pharmacodynamics data. ANN model is created by supervised learning of 90 patients sample to predict the treatment strategy (lamivudine only and Lamivudine + Interferon) on the basis of viral load, liver function test, visit number, treatment duration, ethnic area, sex, and age. The model was trained with 68 (77.3%) samples and tested with 20 (22.7%) samples. The model produced 92% accuracy with 92.8% sensitivity and 83.3% specificity.

Recovery and regularization of initial temperature distribution in a two-layer cylinder with perfect thermal contact at the interface.

We investigate the inverse problem associated with the heat equation involving recovery of initial temperature distribution in a two-layer cylinder with perfect thermal contact at the interface. The heat equation is solved backward in time to obtain a relationship between the final temperature distribution and the initial temperature profile. An integral representation for the problem is found, from which a formula for initial temperature is derived using Picard's criterion and the singular system of the associated operators. The known final temperature profile can be used to recover the initial temperature distribution from the formula derived in this paper. A robust method to regularize the outcome by introducing a small parameter in the governing equation is also presented. It is demonstrated with the help of a numerical example that the hyperbolic model gives better results as compared to the parabolic heat conduction model.