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.

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.

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.

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.

Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Purpose

This study examines how service feedback and physician popularity affect physician demand in the context of virtual healthcare environment. Based on the signaling theory, the critical factor of environment uncertainty (i.e. disease risk) and its impact on physician demand is also investigated. Further, the research on the endogeneity of online reviews in healthcare is also examined in the current study.

Design/methodology/approach

A secondary data econometric analysis using 3-wave data sets of 823 physicians obtained from two PRWs (Healthgrades and Vitals) was conducted. The analysis was run using the difference-in-difference method to consider physician and website-specific effects.

Findings

The study's findings indicate that physician popularity has a stronger positive effect on physician demand compared with service feedback. Improving popularity leads to a relative increase in the number of appointments, which in turn enhance physician demand. Further, the impact of physician popularity on physician demand is positively mitigated by the disease risk.

Originality/value

The authors' research contributes to a better understanding of the signaling transmission mechanism in the online healthcare environment. Further, the findings provide practical implications for key stakeholders into how an efficient feedback and popularity mechanism can be built to enhance physician service outcomes in order to maximize the financial efficiency of physicians.

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.

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.

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.

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.

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.

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.

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.

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.

Pancreatic Cancer Prediction Through an Artificial Neural Network

Early detection of pancreatic cancer is challenging because cancer-specific symptoms occur only at an advanced stage, and a reliable screening tool to identify high-risk patients is lacking. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data of 800,114 respondents captured in the National Health Interview Survey (NHIS) and Pancreatic, Lung, Colorectal, and Ovarian cancer (PLCO) datasets, together containing 898 patients diagnosed with pancreatic cancer. Prediction of pancreatic cancer risk was assessed at an individual level by incorporating 18 features into the neural network. The established ANN model achieved a sensitivity of 87.3 and 80.7%, a specificity of 80.8 and 80.7%, and an area under the receiver operating characteristic curve of 0.86 and 0.85 for the training and testing cohorts, respectively. These results indicate that our ANN can be used to predict pancreatic cancer risk with high discriminatory power and may provide a novel approach to identify patients at higher risk for pancreatic cancer who may benefit from more tailored screening and intervention.

Lung Nodule Detection via Deep Reinforcement Learning

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

Estimation of Scale Factors in Presence of Multiple Signals: Application to sEMG Analysis

When several realizations of an unknown recurrent signal are observed apart from a time expansion or compression, the classical way of estimating these time scaling factors is to take one signal as reference for the estimation. This approach does not take into account the common information between all possible couples of realizations. To achieve this task we use a Maximum-Likelihood based method, in a sub-optimal manner. Using some realistic assumptions and simplifications, we propose a tractable solution. The improvement of classical results is shown through a simulation whose conclusion is that the larger the number of realizations, the more correct the estimation. Finally, we apply the method to electrically evoked sEMG.

Averaging Signals with Random Time Shift and Time Scale Fluctuations

Objectives: Our main objective was to propose an alternative to the technique of signal averaging (SA) to avoid shape distortions due to jittering and scale fluctuations, leading to a mean shape signal rather than to an average one.

Methods: In case of both time shift and time scale fluctuations of the individual signals, the first point was to show what model makes it possible to interpret their expected action as a linear shift invariant filter followed by a scale invariant one. So, even in the case of equal shape signals, the average is clearly not the same shape. The second point was to propose another averaging process, using the normalized integrals and called Shape Averaging (ShA) which provides, in this case, a mean signal preserving the common shape.

Results: The performances of ShA were firstly shown by simulation. Shifted and scaled versions of a given signal, without and with additive noise, have been generated at random. The mean shape signal obtained by ShA was compared to the shifted and scaled signal using the exact average values of the shifts and scale factors. A very good reconstruction of the mean shape signal is obtained for SNR = 20 dB and quite good for 8 dB, especially compared to SA. The method was then applied to a series of M-waves coming from surface EMG signals. In this case, the comparison of ShA with SA makes it possible to appreciate the validity of equal shape signal hypothesis.

Assessment of national dosimetry quality audits results for teletherapy machines from 1989 to 2015

The purpose of this study was to ensure accuracy in radiation dose delivery, external dosimetry quality audit has an equal importance with routine dosimetry performed at clinics. To do so, dosimetry quality audit was organized by the Secondary Standard Dosimetry Laboratory (SSDL) of Pakistan Institute of Nuclear Science and Technology (PINSTECH) at the national level to investigate and minimize uncertainties involved in the measurement of absorbed dose, and to improve the accuracy of dose measurement at different radiotherapy hospitals. A total of 181 dosimetry quality audits (i.e., 102 of Co‐60 and 79 of linear accelerators) for teletherapy units installed at 22 different sites were performed from 1989 to 2015. The percent deviation between users’ calculated/stated dose and evaluated dose (in the result of on‐site dosimetry visits) were calculated and the results were analyzed with respect to the limits of ±2.5% (ICRU “optimal model”) ±3.0% (IAEA on‐site dosimetry visits limit) and ±5.0% (ICRU minimal or “lowest acceptable” model). The results showed that out of 181 total on‐site dosimetry visits, 20.44%, 16.02%, and 4.42% were out of acceptable limits of ±2.5%±3.0%, and ±5.0%, respectively. The importance of a proper ongoing quality assurance program, recommendations of the followed protocols, and properly calibrated thermometers, pressure gauges, and humidity meters at radiotherapy hospitals are essential in maintaining consistency and uniformity of absorbed dose measurements for precision in dose delivery.

PACS number(s): 87.50.cm, 87.50.sj, 87.50.up, 87.50.wj, 87.50.yk, 87.55.km, 87.55.Qr

Translating bed total body irradiation lung shielding and dose optimization using asymmetric MLC apertures

A revised translating bed total body irradiation (TBI) technique is developed for shielding organs at risk (lungs) to tolerance dose limits, and optimizing dose distribution in three dimensions (3D) using an asymmetrically‐adjusted, dynamic multileaf collimator. We present a dosimetric comparison of this technique with a previously developed symmetric MLC‐based TBI technique. An anthropomorphic RANDO phantom is CT scanned with 3 mm slice thickness. Radiological depths (RD) are calculated on individual CT slices along the divergent ray lines. Asymmetric MLC apertures are defined every 9 mm over the phantom length in the craniocaudal direction. Individual asymmetric MLC leaf positions are optimized based on RD values of all slices for uniform dose distributions. Dose calculations are performed in the Eclipse treatment planning system over these optimized MLC apertures. Dose uniformity along midline of the RANDO phantom is within the confidence limit (CL) of 2.1% (with a confidence probability p=0.065). The issue of over‐ and underdose at the interfaces that is observed when symmetric MLC apertures are used is reduced from more than ±4% to less than ±1.5% with asymmetric MLC apertures. Lungs are shielded by 20%, 30%, and 40% of the prescribed dose by adjusting the MLC apertures. Dose‐volume histogram analysis confirms that the revised technique provides effective lung shielding, as well as a homogeneous dose coverage to the whole body. The asymmetric technique also reduces hot and cold spots at lung‐tissue interfaces compared to previous symmetric MLC‐based TBI technique. MLC‐based shielding of OARs eliminates the need to fabricate and setup cumbersome patient‐specific physical blocks.

PACS number(s): 87.55.‐x, 87.55.de, 87.55.D‐

New empirical equation for the atomic form factor function in the momentum transfer range, q=0-50 Å(-1) for the elements in the range 1 ≤ Z ≤ 30

The importance of Atomic Form Factors (f) is well-known to the scientific community. Tabulated values for f are mostly used in calculating cross-sections and Monte Carlo sampling for the coherent scattering of photons. The uses of these values are subjected to different approximations and interpolation techniques because the available data points for f in the literature for specified momentum-transfer-grids are very limited. In order to make it easier to accurately use the tabulated data, a mathematical expression for f functions would be a great achievement. Therefore, the current study was designed to suggest an empirical expression for the f functions. In the results, an empirical equation for Hubbell's tabulated data for f is created in the momentum transfer range, q = 0–50 Å⁻¹ for the elements in the range 1≤ Z ≤30. The number of applied parameters was seven. The fitting to f showed that the maximum deviation was within 3%, 4% and 5% for the element having, Z = 1–11, Z = 12–22 and Z = 23–30, respectively, while the average deviations were within 0.3–2.25% for all elements (i.e., Z = 1–30). The values generated by the analytical equation were used in the Monte Carlo code instead of Hubbell’s tabulated values. The statistical noise in the Probability Distribution Functions of coherently scattered photons was efficiently removed. Furthermore, it also reduced the dependence on different interpolation techniques and approximations, and on the use of large tabulated data for f with the specified elements.

Source of statistical noises in the Monte Carlo sampling techniques for coherently scattered photons

Detailed comparisons of the predictions of the Relativistic Form Factors (RFFs) and Modified Form Factors (MFFs) and their advantages and shortcomings in calculating elastic scattering cross sections can be found in the literature. However, the issues related to their implementation in the Monte Carlo (MC) sampling for coherently scattered photons is still under discussion. Secondly, the linear interpolation technique (LIT) is a popular method to draw the integrated values of squared RFFs/MFFs (i.e. A(Z, v(i)²)) over squared momentum transfer (v(i)² = v(1)²,......, v(59)²). In the current study, the role/issues of RFFs/MFFs and LIT in the MC sampling for the coherent scattering were analyzed. The results showed that the relative probability density curves sampled on the basis of MFFs are unable to reveal any extra scientific information as both the RFFs and MFFs produced the same MC sampled curves. Furthermore, no relationship was established between the multiple small peaks and irregular step shapes (i.e. statistical noise) in the PDFs and either RFFs or MFFs. In fact, the noise in the PDFs appeared due to the use of LIT. The density of the noise depends upon the interval length between two consecutive points in the input data table of A(Z, v(i)²) and has no scientific background. The probability density function curves became smoother as the interval lengths were decreased. In conclusion, these statistical noises can be efficiently removed by introducing more data points in the A(Z, v(i)²) data tables.

Studying wedge factors and beam profiles for physical and enhanced dynamic wedges

This study was designed to investigate variation in Varian's Physical and Enhanced Dynamic Wedge Factors (WF) as a function of depth and field size. The profiles for physical wedges (PWs) and enhanced dynamic wedges (EDWs) were also measured using LDA-99 array and compared for confirmation of EDW angles at different depths and field sizes. WF measurements were performed in water phantom using cylindrical 0.66 cc ionization chamber. WF was measured by taking the ratio of wedge and open field ionization data. A normalized wedge factor (NWF) was introduced to circumvent large differences between wedge factors for different wedge angles. A strong linear dependence of PW Factor (PWF) with depth was observed. Maximum variation of 8.9% and 4.1% was observed for 60 degrees PW with depth at 6 and 15 MV beams respectively. The variation in EDW Factor (EDWF) with depth was almost negligible and less than two per cent. The highest variation in PWF as a function of field size was 4.1% and 3.4% for thicker wedge (60 degrees ) at 6 and 15 MV beams respectively and decreases with decreasing wedge angle. EDWF shows strong field size dependence and significant variation was observed for all wedges at both photon energies. Differences in profiles between PW and EDW were observed on toe and heel sides. These differences were dominant for larger fields, shallow depths, thicker wedges and low energy beam. The study indicated that ignoring depth and field size dependence of WF may result in under/over dose to the patient especially doing manual point dose calculation.

Assessment of computerized treatment planning system accuracy in calculating wedge factors of physical wedged fields for 6 MV photon beams

Wedge filters are commonly used in external beam radiotherapy to achieve a uniform dose distribution within the target volume. The main objective of this study was to investigate the accuracy of the beam modifier algorithm of Theraplan plus (TPP version 3.8) treatment planning system and to confirm that either the beam hardening, beam softening and attenuation coefficients along with wedge geometry and measured wedge factor at single depth and multiple fields sizes can be the replacement of wedged profile and wedged cross-sectional data or not. In this regard the effect of beam hardening and beam softening was studied with physical wedges for 6 MV photons. The Normalized Wedge Factors (NWFs) were measured experimentally as well as calculated with the Theraplan plus, as a function of depth and field size in a water phantom for 15°, 30°, 45°, and 60° wedge filters. The beam hardening and softening was determined experimentally by deriving the required coefficients for all wedge angles. The TPP version 3.8 requires wedge transmission factor at single depth and multiple field sizes. Without incorporating the hardening and softening coefficients the percent difference between measured and calculated NFWs was as high as 7%. After the introduction of these parameters into the algorithm, the agreement between measured and TPP (V 3.8) calculated NWFs were improved to within 2 percent for various depths. Similar improvement was observed in TPP version 3.8 while calculating NWFs for various field sizes when the required coefficients were adjusted. In conclusion, the dose calculation algorithm of TPP version 3.8 showed good accuracy for a 6 MV photon beam provided beam hardening and softening parameters are taken into account. From the results, it is also concluded that, the beam hardening, beam softening and attenuation coefficients along with wedge geometry and measured wedge factor at single depth and multiple fields sizes can be the replacement of wedged profile and wedged cross-sectional data in the TPS. The study also indicated that by ignoring the beam softening and beam hardening will result in an inaccurate dose to the target volume of the patient.

Erratum to "Technical note: Accuracy checks of physical beam modifier factors algorithm used in computerized treatment planning system for a 15 MV photon beam" [Rep. Pract. Oncol. Radiother. 14 (2009) 214-220]

[This corrects the article DOI: 10.1016/j.rpor.2009.12.002.].

Should heart, lung, and liver transplant recipients receive immunosuppression induction for kidney transplantation?

As the outcomes of heart, liver, and lung transplantation continue to improve, more patients will present for subsequent renal transplantation. It remains unclear whether these patients benefit from induction immunosuppression. We retrospectively reviewed induction on solid organ graft recipients who underwent renal transplant at our center from January 1, 1995 to March 30, 2007. Induction and the non-induction groups were compared by univariate and Kaplan-Meier analyses. There were 21 patients in each group, with mean follow-up of 4.5-6.0 years. Forty-seven percent of patients receiving induction had a severe post-operative infection, compared with 28.6% in the non-induction group (p = NS). The one yr rejection rate in the induction group was 9.5% compared with 14.3% for non-induction (p = NS). One-yr graft survival was 81.0% and 95.2% in the induction and non-induction group (p = NS). In summary, there is a trend toward lower patient and graft survival among patients undergoing induction. These trends could relate to selection bias in the decision to prescribe induction immunosuppression, but further study is needed to better define the risks and benefits of antibody-induction regimens in this population.

Epidemiology of the breast cancer patients registered at Institute of Radiotherapy and Nuclear Medicine, Peshawar, Pakistan

In Pakistan, malignant diseases are increasing day by day, but no epidemiological cancer study on large scale has been designed. The main objective of this study was to provide a baseline data on frequency, morphological types, gender and age distribution etc., of breast cancer in North-West Frontier Province and Federally Administered Tribal Areas of Pakistan, and to compare it with the published data. In this context, 2134 breast cancer patients (2059 female and 75 male) registered at Institute of Radiotherapy and Nuclear Medicine, Peshawar from 1995 to 2001, were studied. Crude incidence, standardized incidence ratios (SIR, world) and age-specific incidence rates (ASIR) were determined both for male and female patients. Same morphological distribution was found in both genders. Moreover, breast cancer was found to be the most common malignancy among the women (96.49%). Male to female breast cancer ratio was found to be 3.5 times higher than the reported data. The highest ASIR of approximately 10.6/100,000 per year among women was observed in the age group of 55-59 years. In men, the highest ASIR of 0.84/100,000 per year was observed in the age group of 65-69 years. The SIR (world) for women was 3.15/100,000 per year, while for male this was 0.13/100,000 per year.

Quantitative analysis of the factors responsible for over or under dose of 131 I therapy patients of hyperthyroidism

Radioiodine (131 I) therapy has been in use for more than 60 y. Several protocols have been suggested and used for prescribing the activity to be administered to the patients for the treatment of hyperthyroidism; application of these protocols may result in an under or over dose of the hyperthyroid patients. The main objective of this study was to carry out quantitative analysis of the factors responsible for possible under or over dosage of the patients. In this regard, a total of 59 patients [15 diffuse goitre (DG) and 44 nodular goitre (NG) cases] were studied. In order to compare the thyroid doses calculated by using different protocols, the dosimetric approach was followed. 131 I uptakes were measured after 24 and 48 h, respectively, by giving 0.5 MBq of 131 I to each patient. Thyroid mass and effective half-life were also calculated for each patient and the variations in the thyroid doses were analysed. According to the results 28 and 54% patients were under dosed and 72 and 46% patients were over dosed with DG and NG, respectively. The protocols, which have not taken into account the thyroid mass, multi pre-therapeutic 131 I uptakes and the effective half-life of 131 I of the individual patient, showed a higher degree of deviation from the required thyroid dose. Besides these parameters, some fundamental factors such as radiosensitivity, previous exposure to thyroid drugs and duration of the disease are recommended to be incorporated, which can certainly affect the clinical out comes.

Release criteria from hospitals of 131I thyrotoxicosis therapy patients in developing countries--case study

The current release limit, recommended by the International Atomic Energy Agency (IAEA)(1), from hospitals of patients undergoing 131I thyrotoxicosis therapy is approximately 1100 MBq (approximately 30 mCi). Owing to the difference in socio-economic conditions, literacy rate, family system, etc., this release limit may not be applicable in most of the developing countries like Pakistan. Therefore, the prime objective of this case study was to re-evaluate the release criteria for 131I thyrotoxicosis therapy patients by taking into account their lifestyle, economic conditions and other facilities such as availability of private/public transport, etc. In this context, systematic studies were carried out and 50 patients (i.e. 35 outpatients and 15 inpatients) at the Nuclear Medicine Oncology and Radiotherapy Institute (NORI), Islamabad, were studied. Exposure rate at the surface of the body and at a distance of 1 m from the standing patient was measured. Results obtained from this study showed that the dose equivalent delivered by these patients to their family members (particularly children) and general public was higher than annual dose limits recommended by the International Commission for Radiation Protection in their report ICRP Publication 60(2). In the light of this study, it is recommended that the release activity limit of approximately 370 MBq (or dose rate level of approximately 10 microSv h-1 at 1 m from the patient) be adopted instead of approximately 1100 MBq in developing countries like Pakistan.

Prescription and dispensing practices in public sector health facilities in Pakistan: survey report

OBJECTIVES

To gather information on existing prescription practices, dispensing practices and patient satisfaction in government health services of the NWFP, Baluchistan and Punjab province.

METHODS

A cross sectional study design was used for this purpose. Ten health care facilities were selected from each province keeping appropriate representation from first level health facilities, district health facilities and tertiary care hospital. Analysis of selected indicators was carried out on the basis of provinces, health facilities, gender and different age groups.

RESULTS

Documentation of 914 responses was completed from three provinces. Almost equal distribution of encounters was maintained representing different health facilities. Forty seven percent of encounters involved children under 15 years of age. Female patients comprised of 56% and the mean age of the entire sample was 26 years. The mean dispensing time was only 38 seconds, the mean consultation time was 1.79 minutes and the average number of drugs per prescription turned out to be 2.7 out of which only 1.6 drugs were being dispensed from the facility. More than half of the prescriptions contained antibiotics and 15% of patients were prescribed with injectables. Only half of the patients expressed satisfaction with their visit to health facility.

CONCLUSIONS

Like many other developing countries, prescription and dispensing practices are not satisfactory in public sector health facilities of Pakistan. Appropriate and workable solutions need to be developed and implemented in the country to improve systems. Regular audits and qualitative studies should become part of the effort.

Averaging signals with random time shift and time scale fluctuations

OBJECTIVES

Our main objective was to propose an alternative to the technique of signal averaging (SA) to avoid shape distortions due to jittering and scale fluctuations, leading to a mean shape signal rather than to an average one.

METHODS

In case of both time shift and time scale fluctuations of the individual signals, the first point was to show what model makes it possible to interpret their expected action as a linear shift invariant filter followed by a scale invariant one. So, even in the case of equal shape signals, the average is clearly not the same shape. The second point was to propose another averaging process, using the normalized integrals and called Shape Averaging (ShA) which provides, in this case, a mean signal preserving the common shape.

RESULTS

The performances of ShA were firstly shown by simulation. Shifted and scaled versions of a given signal, without and with additive noise, have been generated at random. The mean shape signal obtained by ShA was compared to the shifted and scaled signal using the exact average values of the shifts and scale factors. A very good reconstruction of the mean shape signal is obtained for SNR = 20 dB and quite good for 8 dB, especially compared to SA. The method was then applied to a series of M-waves coming from surface EMG signals. In this case, the comparison of ShA with SA makes it possible to appreciate the validity of equal shape signal hypothesis.

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Genotyping single nucleotide polymorphisms using intact polymerase chain reaction products by electrospray quadrupole mass spectrometry

Both single nucleotide polymorphisms (SNPs) and mutations are commonly observed in the gene encoding the tumor suppressor protein, p53. SNPs occur at specific locations within genes whereas mutations may be distributed across large regions of genes. When determining nucleotide differences, mass spectrometry is the only method other than Sanger sequencing which offers direct structural information. Electrospray ionization (ESI) quadrupole mass spectrometry (MS) analysis of intact polymerase chain reaction (PCR) products was performed following a simple purification and on-line heating to limit ion adduction. The PCR products were amplified directly from genomic DNA rather than plasmids, as in our previous work. Two known polymorphisms of the p53 gene were genotyped. A cytosine (C) or guanine (G) transversion, designated C <--> G (G <--> C on the opposite strand), were each detected by a 40.0 Da change upon ESI quadrupole MS analysis. Using known PCR products as standards, the genotypes determined for 10 human samples corresponded with restriction fragment length polymorphism (RFLP) analysis. Cytosine/thymine (T) transitions, designated C <--> T (G <--> A on the opposite strand), were also genotyped by ESI-MS. This SNP is discriminated by a 15.0 Da change on one strand (C <--> T) and a 16.0 Da change on the other (G <--> A). Appropriate sample preparation and instrumental configuration (including heated sample inlet syringe and MS source), to limit adducts, are both vital for successful ESI quadrupole MS analysis of intact PCR products.

Estimation of scale factors in presence of multiple signals: application to sEMG analysis

When several realizations of an unknown recurrent signal are observed apart from a time expansion or compression, the classical way of estimating these time scaling factors is to take one signal as reference for the estimation. This approach does not take into account the common information between all possible couples of realizations. To achieve this task we use a Maximum-Likelihood based method, in a sub-optimal manner. Using some realistic assumptions and simplifications, we propose a tractable solution. The improvement of classical results is shown through a simulation whose conclusion is that the larger the number of realizations, the more correct the estimation. Finally, we apply the method to electrically evoked sEMG.

P300, N400, aerobic fitness, and maximal aerobic exercise

Electrophysiological effects of aerobic fitness and maximal aerobic exercise were investigated by comparing P300 and N400 before and after a maximal cycling test. Event-related potentials (ERPs) were obtained from 20 students divided into two matched groups defined by their aerobic fitness level (cyclists vs. sedentary subjects). The session of postexercise ERPs was performed immediately after body temperature and heart rate returned to preexercise values. At rest, no significant differences were observed in ERP parameters between cyclists and sedentary subjects. This finding argued against the hypothesis that ERP modifications may be directly assumed by aerobic fitness level. The postexercise session of ERPs showed a significant P300 amplitude increase and a significant P300 latency decrease in all subjects. Similarly, N400 effect increased significantly after the maximal exercise in all subjects. ERP changes were of the same magnitude in the two groups. The present study argues for a general arousing effect of maximal aerobic exercise, independently of the aerobic fitness level.