Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.

Background and purpose: We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods: Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results: The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion: We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.

Investigating the Antibacterial and Anti-inflammatory Potential of Polyol-Synthesized Silver Nanoparticles.

Silver nanoparticles (Ag-NPs) were synthesized by using the polyol method. The structural and morphological characteristics of Ag-NPs were studied by using X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM). The XRD analysis revealed the formation of single-phase polycrystalline Ag-NPs with an average crystallite size and lattice constant of ∼23 nm and 4.07 Å, respectively, while the FE-SEM shows the formation of a uniform and spherical morphology. Energy-dispersive X-ray spectroscopy confirmed the formation of single-phase Ag-NPs, and no extra elements were detected. A strong absorption peak at ∼427 nm was observed in the UV-vis spectrum, which reflects the surface plasmon resonance (SPR) behavior characteristic of Ag-NPs with a spherical morphology. Fourier-transform infrared (FTIR) spectra also supported the XRD and EDX results with regard to the purity of the prepared Ag-NPs. Anti-inflammatory activity was tested using HRBCs membrane stabilization and heat-induced hemolysis assays. The antibacterial activity of Ag-NPs was evaluated against four different types of pathogenic bacteria by using the disc diffusion method (DDM). The Gram-negative bacterial strains used in this study are Escherichia coli (E. coli), Klebsiella, Shigella, and Salmonella. The analysis suggested that the antibacterial activities of Ag-NPs have an influential role in inhibiting the growth of the tested Gram-negative bacteria, and thus Ag-NPs can find a potential application in the pharmaceutical industry.

A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset.

BACKGROUND: Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups. METHODS: For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018. RESULTS: The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018. CONCLUSION: Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.

Viability of the virtual cone technique using a fixed small multi-leaf collimator field for stereotactic radiosurgery of trigeminal neuralgia.

Dosimetric uncertainties in very small (≤1.5 × 1.5 cm2 ) photon fields are remarkably higher, which undermines the validity of the virtual cone (VC) technique with a diminutive and variable MLC fields. We evaluate the accuracy and reproducibility of the VC method with a very small, fixed MLC field setting, called a fixed virtual cone (fVC), for small target radiosurgery such as trigeminal neuralgia (TGN). The fVC is characterized by 0.5 cm x 0.5 cm high-definition (HD) MLC field of 10MV FFF beam defined at 100 cm SAD, while backup jaws are positioned at 1.5 cm x 1.5 cm. A spherical dose distribution equivalent to 5 mm (diameter) physical cone was generated using 10-14 non-coplanar, partial arcs. Dosimetric accuracy was validated using SRS diode (PTW 60018), SRS MapCHECK (SNC) measurements. As a quality assurance measure, 10 treatment plans (SRS) for TGN, consisting of various arc ranges at different collimator angles were analyzed using 6 MV FFF and 10 MV FFF beams, including a field-by-field study (n = 130 fields). Dose outputs were compared between the Eclipse TPS and measurements (SRS MapCHECK). Moreover, dosimetric changes in the field defining fVC, prompted by a minute (± 0.5-1.0 mm) leaf shift, was examined among TPS, diode measurements, and Monte Carlo (MC) simulations. The beam model for fVC was validated (≤3% difference) using SRS MapCHECK based absolute dose measurements. The equivalent diameters of the 50% isodose distribution were found comparable to that of a 5 mm cone. Additionally, the comparison of field output factors, dose per MU between the TPS and SRS diode measurements using the fVC field, including ± 1 mm leaf shift, yielded average discrepancies within 5.5% and 3.5% for 6 MV FFF and 10 MV FFF beams, respectively. Overall, the fVC method is a credible alternative to the physical cone (5 mm) that can be applied in routine radiosurgical treatment of TGN.

Liver cancer risk quantification through an artificial neural network based on personal health data.

BACKGROUND: Liver cancer is one of the most common types of cancer and the third leading cause of cancer-related deaths globally. The most common type of primary liver cancer is called hepatocellular carcinoma (HCC) which accounts for 75-85% of cases. HCC is a malignant disease with aggressive progression and limited therapeutic options. While the exact cause of liver cancer is not known, habits/lifestyles may increase the risk of developing the disease. MATERIAL AND METHODS: This study is designed to quantify the liver cancer risk through a multi-parameterized artificial neural network (ANN) based on basic health data including habits/lifestyles. In addition to input and output layers, our ANN model has three hidden layers having 12, 13, and 14 neurons, respectively. We have used the health data from the National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) datasets to train and test our ANN model. RESULTS: We have found the best performance of the ANN model with an area under the receiver operating characteristic curve of 0.80 and 0.81 for training and testing cohorts, respectively. CONCLUSION: Our results demonstrate a method that can predict liver cancer risk with basic health data and habits/lifestyles. This novel method could be beneficial to high-risk populations by enabling early detection.

A novel Monte Carlo (MC) dose model for small MLC fields of the cyberknife® M6TM radiosurgery system using the EGSnrc.

The multi-leaf collimator (MLC)-equipped CyberKnife® M6 radiosurgery system (CKM6) (Accuray Inc., Sunnyvale, CA) has been increasingly employed for stereotactic radiosurgery (SRS) to treat relatively small lesions. However, achieving an accurate dose distribution in such cases is usually challenging due to the combination of numerous small fields ≤ (30 × 30) mm2 . In this study, we developed a new Monte Carlo (MC) dose model for the CKM6 system using the EGSnrc to investigate dose variations in the small fields. The dose model was verified for the static MLC fields ranging from (53.8 × 53.9) to (7.6 × 7.7) mm2 at 800 mm source to axis distance in a water phantom, based on the computed doses of Accuray Precision® (Accuray Inc.) treatment planning system (TPS). We achieved a statistical uncertainty of ≤4% by simulating 30-50 million incident particles/histories. Then, the treatment plans were created for the same fields in the TPS, and the corresponding measurements were performed with MapCHECK2 (Sun Nuclear Corporation), a standard device for patient-specific quality assurance (PSQA). Results of the MC simulations, TPS, and MapCHECK2 measurements were inter-compared. An overall difference in dosimetric parameters such as profiles, tissue maximum ratio (TMR), and output factors (OF) between the MC simulations and the TPS results was found ≤3% for (53.8 × 53.9-15.4 × 15.4) mm2 MLC fields, and it rose to 4.5% for the smallest (7.6 mm × 7.7 mm) MLC field. The MapCHECK2 results showed a deviation ranging from -1.5% to + 4.5% compared to the TPS results, whereas the deviation was within ±2.5% compared with the MC results. Overall, our MC dose model for the CKM6 system showed better agreement with measurements and it could serve as a secondary dose verification tool for the patient-specific QA in small fields.

Efficacy and Safety of Brivaracetam in Persons With Epilepsy in a Real-World Setting: A Prospective, Non-Interventional Study.

BACKGROUND AND AIM: Epilepsy stands out as one of the most prevalent neurological conditions. Brivaracetam (BRV) is a noteworthy antiseizure medication (ASM) distinguished by its pronounced and selective interaction with the synaptic vesicle protein 2A (SV2A) within the brain. Prior investigations, including regulatory trials, post-marketing assessments, and comparative meta-analyses, have consistently underscored BRV's equivalency in efficacy and superior tolerability when pitted against other antiseizure drugs. This study aimed to evaluate the effectiveness, safety, and acceptability of BRV in treating epileptic patients in the Pakistani population. METHODS: This prospective observational study, conducted in Pakistan from February to December 2022, employed a non-probability consecutive sampling technique. This study included 368 adult patients diagnosed with epilepsy, with a focus on those aged 18 and above experiencing focal seizures. Demographic data, clinical history, seizure types, and epilepsy profiles were recorded. Patients were administered BRV (Brivera; manufactured by Helix Pharma Pvt Ltd., Sindh, Pakistan) monotherapy therapy under physician guidance and followed up for three months. The study assessed changes in seizure frequency, side effects, and drug resistance at baseline, 14th day, and 90th day. Safety aspects were monitored, including documenting any adverse effects associated with BRV therapy. RESULTS: A total of 368 epileptic patients were included in this study, of which 287 (61.3%) were males and 181 (38.7%) were females. The mean age was 32.91±17.11 years. The mean number of seizures at the baseline visit was 5.74±6.21, at 14 days was 2.89±3.84 and at 90 days was 1.73±5.01 (p<0.001). Overall, a more than 50% reduction in seizure episodes was achieved in 178 (56.3%) patients at day 90, and less than 50% reduction in seizure episodes was achieved by 95 (26.8%) patients on Day 14, with a highly significant association between them (p<0.001). Among 316 patients, only 41 (4.4%) of all BRV-treated patients experienced adverse events; Of these 41 patients, 17 (41.7%) reported dizziness and 14(34.2%) reported behavioral issues. CONCLUSIONS: Epileptic patients receiving BRV demonstrated a substantial reduction of greater than 50% seizure episodes at the end of follow-up visits. Moreover, BRV exhibited fewer adverse effects in individuals with epilepsy.

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach.

(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.

Statistical biopsy: An emerging screening approach for early detection of cancers.

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

Examining Different Factors in Web-Based Patients' Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System.

(1) Background: The appearance of physician rating websites (PRWs) has raised researchers' interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers' information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs' features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor-patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

Multi-scale Xception based depthwise separable convolution for single image super-resolution.

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography.

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

A general-purpose Monte Carlo particle transport code based on inverse transform sampling for radiotherapy dose calculation.

The Monte Carlo (MC) method is widely used to solve various problems in radiotherapy. There has been an impetus to accelerate MC simulation on GPUs whereas thread divergence remains a major issue for MC codes based on acceptance-rejection sampling. Inverse transform sampling has the potential to eliminate thread divergence but it is only implemented for photon transport. Here, we report a MC package Particle Transport in Media (PTM) to demonstrate the implementation of coupled photon-electron transport simulation using inverse transform sampling. Rayleigh scattering, Compton scattering, photo-electric effect and pair production are considered in an analogous manner for photon transport. Electron transport is simulated in a class II condensed history scheme, i.e., catastrophic inelastic scattering and Bremsstrahlung events are simulated explicitly while subthreshold interactions are subject to grouping. A random-hinge electron step correction algorithm and a modified PRESTA boundary crossing algorithm are employed to improve simulation accuracy. Benchmark studies against both EGSnrc simulations and experimental measurements are performed for various beams, phantoms and geometries. Gamma indices of the dose distributions are better than 99.6% for all the tested scenarios under the 2%/2 mm criteria. These results demonstrate the successful implementation of inverse transform sampling in coupled photon-electron transport simulation.

Monte Carlo simulation of coherently scattered photons based on the inverse-sampling technique.

The acceptance-rejection technique has been widely used in several Monte Carlo simulation packages for Rayleigh scattering of photons. However, the models implemented in these packages might fail to reproduce the corresponding experimental and theoretical results. The discrepancy is attributed to the fact that all current simulations implement an elastic scattering model for the angular distribution of photons without considering anomalous scattering effects. In this study, a novel Rayleigh scattering model using anomalous scattering factors based on the inverse-sampling technique is presented. Its performance was evaluated against other simulation algorithms in terms of simulation accuracy and computational efficiency. The computational efficiency was tested with a general-purpose Monte Carlo package named Particle Transport in Media (PTM). The evaluation showed that a Monte Carlo model using both atomic form factors and anomalous scattering factors for the angular distribution of photons (instead of the atomic form factors alone) produced Rayleigh scattering results in closer agreement with experimental data. The comparison and evaluation confirmed that the inverse-sampling technique using atomic form factors and anomalous scattering factors exhibited improved computational efficiency and performed the best in reproducing experimental measurements and related scattering matrix calculations. Furthermore, using this model to sample coherent scattering can provide scientific insight for complex systems.

Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification.

While colorectal cancer (CRC) is third in prevalence and mortality among cancers in the United States, there is no effective method to screen the general public for CRC risk. In this study, to identify an effective mass screening method for CRC risk, we evaluated seven supervised machine learning algorithms: linear discriminant analysis, support vector machine, naive Bayes, decision tree, random forest, logistic regression, and artificial neural network. Models were trained and cross-tested with the National Health Interview Survey (NHIS) and the Prostate, Lung, Colorectal, Ovarian Cancer Screening (PLCO) datasets. Six imputation methods were used to handle missing data: mean, Gaussian, Lorentzian, one-hot encoding, Gaussian expectation-maximization, and listwise deletion. Among all of the model configurations and imputation method combinations, the artificial neural network with expectation-maximization imputation emerged as the best, having a concordance of 0.70 ± 0.02, sensitivity of 0.63 ± 0.06, and specificity of 0.82 ± 0.04. In stratifying CRC risk in the NHIS and PLCO datasets, only 2% of negative cases were misclassified as high risk and 6% of positive cases were misclassified as low risk. In modeling the CRC-free probability with Kaplan-Meier estimators, low-, medium-, and high CRC-risk groups have statistically-significant separation. Our results indicated that the trained artificial neural network can be used as an effective screening tool for early intervention and prevention of CRC in large populations.

Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence.

Incidence and mortality rates of endometrial cancer are increasing, leading to increased interest in endometrial cancer risk prediction and stratification to help in screening and prevention. Previous risk models have had moderate success with the area under the curve (AUC) ranging from 0.68 to 0.77. Here we demonstrate a population-based machine learning model for endometrial cancer screening that achieves a testing AUC of 0.96. We train seven machine learning algorithms based solely on personal health data, without any genomic, imaging, biomarkers, or invasive procedures. The data come from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). We further compare our machine learning model with 15 gynecologic oncologists and primary care physicians in the stratification of endometrial cancer risk for 100 women. We find a random forest model that achieves a testing AUC of 0.96 and a neural network model that achieves a testing AUC of 0.91. We test both models in risk stratification against 15 practicing physicians. Our random forest model is 2.5 times better at identifying above-average risk women with a 2-fold reduction in the false positive rate. Our neural network model is 2 times better at identifying above-average risk women with a 3-fold reduction in the false positive rate. Our machine learning models provide a non-invasive and cost-effective way to identify high-risk sub-populations who may benefit from early screening of endometrial cancer, prior to disease onset. Through statistical biopsy of personal health data, we have identified a new and effective approach for early cancer detection and prevention for individual patients.

Clinical Profile and Prognosis of Cerebral Venous Sinus Thrombosis.

Introduction Cerebral venous sinus thrombosis (CVST) is an acute cerebrovascular disease diagnosed nowadays more frequently. Magnetic resonance venogram (MRV) is the modality of choice for accurate diagnosis. Young females in their childbearing age are prone to develop CVST. Clinical presentation is mainly with headache, focal neurologic deficits, and seizures. Around one third of the patients have altered sensorium at presentation. Prognosis of CVST is good if diagnosed and treated early. Long-term deficits may remain in one quarter of patients. The aim of our study was to do clinical profiling and prognosis of CVST patients. Materials and methods This is a descriptive study conducted at the department of Neurology, Sheikh Zayed Medical College/Hospital, Rahim Yar Khan. Study duration was one year. Patients fulfilling inclusion and exclusion criteria were included in the study. Patients confirmed to have CVST on magnetic resonance imaging (MRI)/MRV were included in final analysis. Ethical approval was taken from the Institutional Review Board.  Results Thirty three out of 54 patients were included in the final analysis. Out of them, 29 (87.8%) were females and four (12.1%) were males. The mean age at the time of presentation was 31.36 ± 9.61. Of the 29 females, only three were pregnant and 26 were in the postpartum period at the time of presentation. Twelve (41.4%) females were primigravida. Focal deficits were present in 30 (90.9%) patients; headache was present in 26 (78.8%) patients; seizures were present in 24 (72.7%) patients on presentation; and anemia was present in 20 (60.6%) patients. Conclusion CVST is an important cause of intracranial hypertension, seizures, and stroke in young people. Clinical presentation is extremely variable, and a high index of suspicion is needed. Magnetic resonance imaging brain with MRV is the current diagnostic modality of choice. Medical management with anticoagulants and supportive measures has excellent clinical outcomes.

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To improve screening specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12 to 14 categories of personal health data from the National Health Interview Survey (NHIS). Years 1997-2016 of the NHIS contain 583,770 respondents who had never received a diagnosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking the survey. The trained ANN has sensitivity of 0.57 ± 0.03, specificity of 0.89 ± 0.02, positive predictive value of 0.0075 ± 0.0003, negative predictive value of 0.999 ± 0.001, and concordance of 0.80 ± 0.05 per the guidelines of Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in 60 cases). The number of non-CRC respondents misclassified as high risk is decreased from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and easy to implement publicly.

An overview of radioactivity measurement studies in Pakistan.

In our environment, various naturally occurring radionuclides are present (both underground and overground) in several places, which results in lifelong human exposure. The radiation dose received by human beings from the radiation emitted by these naturally occurring radionuclides is approximately 87%. Exposure to radiation poses radiological health hazards. To assess the human health hazards from radiation, the concentration of these naturally occurring radionuclides are measured in soil (used for cultivation), building materials (soil, bricks, sand, marble, etc.), water and dietary items, worldwide. The available literature revealed that numerous studies related to the subject have been carried out in Pakistan. Most of these studies measured the radioactivity concentrations of primordial [uranium (238U), thorium (232Th), radium (226Ra) and potassium (40K)] and anthropogenic [cesium (137Cs)] radionuclide in soil samples (used for cultivation), fertilizers, building materials (i.e. bricks, rocks, sand, soil, marble, etc.), as well as water and dietary items, using a sodium iodide detector or high purity germanium. An effort was made in 2008 to compile these studies as a review article. However, since then, considerable studies have been undertaken and reported in the literature. Therefore, the main objective of the present article is to provide a countrywide baseline data on radionuclide levels, by overviewing and compiling the relevant studies carried out in Pakistan.

Stratifying Ovarian Cancer Risk Using Personal Health Data.

Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk. Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening.