Machine Learning Models Can Define Clinically Relevant Bone Density Subgroups based on Patient Specific Calibrated CT Scans in Patients Undergoing Reverse Shoulder Arthroplasty.

BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and position, reproducible methods for quantifying the patients' bone density are currently not available. The purpose of this study was to perform bone density analyses including patient specific calibration in an RSA cohort based on preoperative CT imaging. It was hypothesized that preoperative CT bone density measures would provide objective quantification of the patients' humeral bone quality. METHODS: This study consisted of three parts, (1) analysis of a patient-specific calibration method in cadaveric CT scans, (2) retrospective application in a clinical RSA cohort, and (3) clustering and classification with machine learning models. Forty cadaveric shoulders were scanned in a clinical CT and compared regarding calibration with density phantoms, air muscle, and fat (patient-specific) or standard Hounsfield unit. Post-scan patient-specific calibration was used to improve the extraction of three-dimensional regions of interest for retrospective bone density analysis in a clinical RSA cohort (n=345). Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of low bone densities in the respective patients. RESULTS: The patient-specific calibration method demonstrated improved accuracy with excellent intraclass correlation coefficients (ICC) for cylindrical cancellous bone densities (ICC>0.75). Clustering partitioned the training data set into a high-density subgroup consisting of 96 patients and a low-density subgroup consisting of 146 patients, showing significant differences between these groups. The SVM showed optimized prediction accuracy of low and high bone densities compared to conventional statistics in the training (accuracy=91.2%; AUC=0.967) and testing (accuracy=90.5 %; AUC=0.958) data set. CONCLUSION: Preoperative CT scans can be used to quantify the proximal humeral bone quality in patients undergoing RSA. The use of machine learning models and patient-specific calibration on bone mineral density demonstrated that multiple 3D bone density scores improved the accuracy of objective preoperative bone quality assessment. The trained model could provide preoperative information to surgeons treating patients with potentially poor bone quality.

Long-term outcomes of urethral balloon dilation for anterior urethral stricture: A prospective cohort study.

Objective: To prospectively follow up a cohort of anterior urethral stricture disease patients managed with balloon dilation (BD) for 3 years to evaluate the long-term outcomes and to study factors that contribute to recurrence. Methods: This study included men who had urethral BD for significant anterior urethral stricture disease between January 2017 and March 2019. Data about the patient age, stricture characteristics, and recurrence date were recorded, along with information on postoperative indwelling catheter use and operative complications. Furthermore, information about the self-calibration procedure was collected and where available, free flow (FF) measurements during the follow-up period were recorded and analyzed. Success was defined as a lack of symptoms and acceptable FF rates (maximum flow rate>12 mL/s). Results: The final analysis was conducted on 187 patients. The mean follow-up period was 37 months. The long-term overall success rate at the end of our study was 66.8%. Our recurrence rate was 7.4% at 12 months, 24.7% at 24 months, and reached 33.2% at the end of our study. The time to recurrence ranged from 91 days to 1635 days, with a mean of 670 days. The stricture-free survival was significantly shorter with lengthy peno-bulbar (p=0.031) and multiple strictures (p=0.015), and in the group of patients who were not committed to self-calibration protocol (p<0.011). However, post-procedural self-calibration was the most important factor that may have decreased the incidence of recurrence (odds ratio=5.85). Adjuvant self-calibration after BD not only reduced the recurrence rate from 85.4% in the non-self-calibration group to 15.1% in the self-calibration one (p<0.001), but also improved the overall stricture-free survival and FF parameters. Conclusion: Urethral BD has a high recurrence rate in the long-term, especially with long and multiple strictures. Adjuvant self-calibration has proven to reduce the recurrence risk and the need for re-intervention.

Comparison of EV characterization by commercial high-sensitivity flow cytometers and a custom single-molecule flow cytometer.

High-sensitivity flow cytometers have been developed for multi-parameter characterization of single extracellular vesicles (EVs), but performance varies among instruments and calibration methods. Here we compare the characterization of identical (split) EV samples derived from human colorectal cancer (DiFi) cells by three high-sensitivity flow cytometers, two commercial instruments, CytoFLEX/CellStream, and a custom single-molecule flow cytometer (SMFC). DiFi EVs were stained with the membrane dye di-8-ANEPPS and with PE-conjugated anti-EGFR or anti-tetraspanin (CD9/CD63/CD81) antibodies for estimation of EV size and surface protein copy numbers. The limits of detection (LODs) for immunofluorescence and vesicle size based on calibration using cross-calibrated, hard-dyed beads were ∼10 PE/∼80 nm EV diameter for CytoFLEX and ∼10 PEs/∼67 nm for CellStream. For the SMFC, the LOD for immunofluorescence was 1 PE and ≤ 35 nm for size. The population of EVs detected by each system (di-8-ANEPPS+/PE+ particles) differed widely depending on the LOD of the system; for example, CellStream/CytoFLEX detected only 5.7% and 1.5% of the tetraspanin-labelled EVs detected by SMFC, respectively, and median EV diameter and antibody copy numbers were much larger for CellStream/CytoFLEX than for SMFC as measured and validated using super-resolution/single-molecule TIRF microscopy. To obtain a dataset representing a common EV population analysed by all three platforms, we filtered out SMFC and CellStream measurements for EVs below the CytoFLEX LODs as determined by bead calibration (10 PE/80 nm). The inter-platform agreement using this filtered dataset was significantly better than for the unfiltered dataset, but even better concordance between results was obtained by applying higher cutoffs (21 PE/120 nm) determined by threshold analysis using the SMFC data. The results demonstrate the impact of specifying LODs to define the EV population analysed on inter-instrument reproducibility in EV flow cytometry studies, and the utility of threshold analysis of SMFC data for providing semi-quantitative LOD values for other flow cytometers.

Evaluation of monthly variations in electrometer calibration coefficients using a charge generator for radiation therapy.

Electrometers are important devices that are part of the standard dosimetry system. Therefore, we evaluated the variation of electrometer calibration coefficients (kelec) over 1 year in this study. We investigated two types of electrometers: a rate mode and an integrate mode. Each electrometer was connected to a charge generator, a constant charge was applied, and kelec was determined by measuring the current. The current measurements were repeated once a month. For electrometers with multiple ranges, measurements were taken at low and medium ranges. Almost all kelec measurements agreed within 0.2% of the initial measurements. However, the low range of the electrometer with an integrate mode showed seasonal variation, with a variation greater than 0.2%. This study shows that electrometers may exhibit errors that cannot be detected through annual inspections. The importance of quality assurance using a charge generator at one's own institution was demonstrated.

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios.

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Development and validation of a model to predict ceiling of care in COVID-19 hospitalized patients.

BACKGROUND: Therapeutic ceiling of care is the maximum level of care deemed appropiate to offer to a patient based on their clinical profile and therefore their potential to derive benefit, within the context of the availability of resources. To our knowledge, there are no models to predict ceiling of care decisions in COVID-19 patients or other acute illnesses. We aimed to develop and validate a clinical prediction model to predict ceiling of care decisions using information readily available at the point of hospital admission. METHODS: We studied a cohort of adult COVID-19 patients who were hospitalized in 5 centres of Catalonia between 2020 and 2021. All patients had microbiologically proven SARS-CoV-2 infection at the time of hospitalization. Their therapeutic ceiling of care was assessed at hospital admission. Comorbidities collected at hospital admission, age and sex were considered as potential factors for predicting ceiling of care. A logistic regression model was used to predict the ceiling of care. The final model was validated internally and externally using a cohort obtained from the Leeds Teaching Hospitals NHS Trust. The TRIPOD Checklist for Prediction Model Development and Validation from the EQUATOR Network has been followed to report the model. RESULTS: A total of 5813 patients were included in the development cohort, of whom 31.5% were assigned a ceiling of care at the point of hospital admission. A model including age, COVID-19 wave, chronic kidney disease, dementia, dyslipidaemia, heart failure, metastasis, peripheral vascular disease, chronic obstructive pulmonary disease, and stroke or transient ischaemic attack had excellent discrimination and calibration. Subgroup analysis by sex, age group, and relevant comorbidities showed excellent figures for calibration and discrimination. External validation on the Leeds Teaching Hospitals cohort also showed good performance. CONCLUSIONS: Ceiling of care can be predicted with great accuracy from a patient's clinical information available at the point of hospital admission. Cohorts without information on ceiling of care could use our model to estimate the probability of ceiling of care. In future pandemics, during emergency situations or when dealing with frail patients, where time-sensitive decisions about the use of life-prolonging treatments are required, this model, combined with clinical expertise, could be valuable. However, future work is needed to evaluate the use of this prediction tool outside COVID-19.

Importance of Transplant Era on Post-Heart Transplant Predictive Models: A UNOS Cohort Analysis.

BACKGROUND: The application of posttransplant predictive models is limited by their poor statistical performance. Neglecting the dynamic evolution of demographics and medical practice over time may be a key issue. OBJECTIVES: Our objective was to develop and validate era-specific predictive models to assess whether these models could improve risk stratification compared to non-era-specific models. METHODS: We analyzed the United Network for Organ Sharing (UNOS) database including first noncombined heart transplantations (2001-2018, divided into four transplant eras: 2001-2005, 2006-2010, 2011-2015, 2016-2018). The endpoint was death or retransplantation during the 1st-year posttransplant. We analyzed the dynamic evolution of major predictive variables over time and developed era-specific models using logistic regression. We then performed a multiparametric evaluation of the statistical performance of era-specific models and compared them to non-era-specific models in 1000 bootstrap samples (derivation set, 2/3; test set, 1/3). RESULTS: A total of 34 738 patients were included, 3670 patients (10.5%) met the composite endpoint. We found a significant impact of transplant era on baseline characteristics of donors and recipients, medical practice, and posttransplant predictive models, including significant interaction between transplant year and major predictive variables (total serum bilirubin, recipient age, recipient diabetes, previous cardiac surgery). Although the discrimination of all models remained low, era-specific models significantly outperformed the statistical performance of non-era-specific models in most samples, particularly concerning discrimination and calibration. CONCLUSIONS: Era-specific models achieved better statistical performance than non-era-specific models. A regular update of predictive models may be considered if they were to be applied for clinical decision-making and allograft allocation.

Design and characterization of a hybrid PET detector with DOI capability.

BACKGROUND: Monolithic or semi-monolithic detectors are attractive for positron emission tomography (PET) scanners with depth-of-interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. PURPOSE: We introduce a novel hybrid detector design that combines pixelated and semi-monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. METHODS: A prototype detector with eight hybrid lutetium-yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy-weighted and energy-squared weighted averages were used for estimating the x- (pixelated direction) and y-positions (non-pixelated direction). Pseudo-pixels were defined as discrete areas on the flood image based on the crystal look-up table (LUT). The intrinsic spatial resolutions in the pixelated and non-pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy-weighted average of the earliest n time stamps. Two energy windows of 250-700 and 400-600 keV were applied for the measurements. RESULTS: The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x- and y-positions of events could be achieved. Under 400-600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non-pixelated directions; the average DOI resolution of the second row of pseudo-pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy-weighted average of the earliest four-time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250-700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps. CONCLUSIONS: The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability.

Semi-quantitative analysis of formaldehyde in food using calibration chart based on number of colored wells of microwell plate titration.

Microplate titration quantifies sodium hydroxide generated from formaldehyde reacting with excess sulfite in a 96-microwell plate. Phenolphthalein indicators change from red to colorless when all hydroxide ions react. Methodology optimized reagent concentrations, and reaction time and created a Calibration Chart for semi-quantitative determination. The chart shows formaldehyde concentration ranges corresponding to red well counts from 0 to 200 mM in 20 mM increments. Inter-operator repeatability demonstrates precision (3 replicates), correlating red wells with standard formaldehyde concentrations. This instrument-free technique uses readily available commercial plates, eliminating the need for specialized equipment and calibration. The methodology offers simplicity with its reliance on readily available commercial plates and minimal specialized equipment, hence it is cost-effective and easily transportable 96-microwell plates enhancing the methodology's portability, and efficient semi-quantitative analysis of formaldehyde. The analysis of twelve solutions from food samples agrees with the quantitative values using titration.

Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI.

BACKGROUND: Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. METHODS: We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). RESULTS: The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. CONCLUSIONS: Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.

A novel EPID-based MLC QA method with log files achieving submillimeter accuracy.

The purpose of this study is to develop an electronic portal imaging device-based multi-leaf collimator calibration procedure using log files. Picket fence fields with 2-14 mm nominal strip widths were performed and normalized by open field. Normalized pixel intensity profiles along the direction of leaf motion for each leaf pair were taken. Three independent algorithms and an integration method derived from them were developed according to the valley value, valley area, full-width half-maximum (FWHM) of the profile, and the abutment width of the leaf pairs obtained from the log files. Three data processing schemes (Scheme A, Scheme B, and Scheme C) were performed based on different data processing methods. To test the usefulness and robustness of the algorithm, the known leaf position errors along the direction of perpendicular leaf motion via the treatment planning system were introduced in the picket fence field with nominal 5, 8, and 11 mm. Algorithm tests were performed every 2 weeks over 4 months. According to the log files, about 17.628% and 1.060% of the leaves had position errors beyond ± 0.1 and ± 0.2 mm, respectively. The absolute position errors of the algorithm tests for different data schemes were 0.062 ± 0.067 (Scheme A), 0.041 ± 0.045 (Scheme B), and 0.037 ± 0.043 (Scheme C). The absolute position errors of the algorithms developed by Scheme C were 0.054 ± 0.063 (valley depth method), 0.040 ± 0.038 (valley area method), 0.031 ± 0.031 (FWHM method), and 0.021 ± 0.024 (integrated method). For the efficiency and robustness test of the algorithm, the absolute position errors of the integration method of Scheme C were 0.020 ± 0.024 (5 mm), 0.024 ± 0.026 (8 mm), and 0.018 ± 0.024 (11 mm). Different data processing schemes could affect the accuracy of the developed algorithms. The integration method could integrate the benefits of each algorithm, which improved the level of robustness and accuracy of the algorithm. The integration method can perform multi-leaf collimator (MLC) quality assurance with an accuracy of 0.1 mm. This method is simple, effective, robust, quantitative, and can detect a wide range of MLC leaf position errors.

Study on the effect of aeration system on the mass transfer performance of supersaturated total dissolved gas.

ABSTRACTThe release of supersaturated total dissolved gas (STDG) from dams has been linked to the development of gas bubble disease, which can ultimately result in the death of fish. In order to minimize the impact of STDG on aquatic ecology, the effect of aeration on mass transfer at the air-liquid interface is taken into account. This paper selects four commonly used aerators to carry out indoor aeration tower experiments under different aeration conditions (aeration aperture, aeration water depth, and aeration volume), exploring aerators that can efficiently promote STDG release. The results indicated that the diaphragm aerator was found to have the greatest effect on STDG release, followed by corundum and spin mix aerator. In contrast, a pinhole aerator was found to have the least beneficial impact on STDG release. The increase in the release coefficient for the diaphragm aerator in comparison to the pinhole aerator is 32%. A prediction model for the aeration system was developed based on the mass transfer mechanism at the gas-liquid interface. The parameters in the model were determined using experimental data, which effectively improved the model's prediction accuracy. The findings of this study may serve as a reference point for the selection of the most suitable aerator in the actual engineering of STDG mitigation by aeration technology.

Construction of a nomogram model for predicting the outcome of debulking surgery for ovarian cancer on the basis of clinical indicators.

Objective: This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model. Methods: Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves. Results: Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility. Conclusion: Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits.

Development of a Relative Dosimetric System for Calibration of 32P Eye Applicators Using Radiochromic Films.

Introduction: Beta irradiation after bare scleral surgery of primary pterygium is an effective and safe treatment, which reduces the risk of local recurrence. Purpose: Obtaining the reference dose rate for a radioactive applicator consisting of a plate as a 32P absorber, a steel window and a steel capsule. Methods: Relative dosimetry and dose profile were measured using two types of radiochromic films, HD-810 and EBT1, for the 32P applicator and were compared with Monte Carlo simulation data. Dose uniformity in the 32P applicator was obtained with radiochromic HD-810 film. Results: The measurement depth dose distribution data at distances up to 3.8 mm were compared with calculation data, and the values were not found to differ statistically. Depth dose distribution with a large dose gradient was determined and the dose rate data obtained 0.0053 ± 9.9% in unit of Gy/s.mCi at a 0.1 mm depth distance. Practical results indicated that the dose nonuniformity and the maximum symmetrical for the 32P applicator were 11.5% and 9.2%, respectively. Conclusions: Our experiments show that the use of the radiochromic film to perform the relative dosimetric checks is feasible and the activity value with acceptable error can be determined through this indirect method.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

[Box: see text].

[Verification of Response Uncertainty in Electrometers through Cross-Comparison with a Novel Current Source: A Comparative Study with Guidelines for Electrometers Used in Radiation Therapy Dosimeters].

BACKGROUND: A new quality assurance and control method for electrometers using a new current source, different from the method published in the guidelines for electrometers, has been reported. This current source uses dry batteries and exhibits excellent performance in terms of voltage, temperature, and time characteristics. The electrometer sensitivity coefficient can be calculated by comparing the sensitivity of one electrometer with that of another on the electrometer calibration coefficient that has been calibrated by a calibration laboratory in advance in both methods. The guideline method requires two or more sets of ionization chambers and electrometers in the facility. In contrast, our method does not use ionization chambers; therefore, the sensitivity ratio of the electrometer can be measured in any facility. This study compared the uncertainty of the electrometer sensitivity factor calculated using the new current source method (current method) with that calculated using a linear accelerator (LINAC) and ionization chambers (LINAC method) described in the electrometer guidelines. METHOD: In this study, we used a current source that we invented previously by Kawaguchi Electric Works in Japan. The sensitivity ratios of the electrometers were measured with three manufacture's electrometers. The electrometer sensitivity factor was calculated by multiplying the electrometer calibration coefficient. The ionization chamber was 30013 (PTW), and the current source was the current obtained from 10 MV TrueBeam X-rays under calibration conditions. The mean value, standard deviation, and coefficient of variation were calculated. The time required to set up the ionization chamber for calculating the sensitivity ratio of the electrometer was also measured. The accuracy was confirmed by calculating the expanded uncertainty of the electrometer sensitivity coefficients. RESULTS: The LINAC method had a maximum coefficient of variation of 0.072%. The gross time of the LINAC method was approximately 110 min. The current method had a maximum coefficient of variation of 0.0055% and took less than half the time taken by the LINAC method (35 min) because there was no waiting time for the ionization chamber to be set up and the applied voltage to stabilize under calibration conditions. The expanded uncertainties of the electrometer calibration coefficients were 0.36% and 0.36%, respectively. CONCLUSION: The new cross-comparison method for electrometer sensitivity factors using a current source is more efficient and useful than the linear accelerator method described in the guidelines; furthermore, this method ensured accuracy for quality assurance and control of electrometers.

Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning.

Objective.To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms.Approach.A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves-i.e. tissue substitutes and stoichiometric-were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures.Main results.Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (-2.5 0.5) HU °C-1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure.Significance.Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.

Impact of the measurement conditions and compression paddle on mammography dosimeter response.

The effect of mammography measurement conditions was investigated to evaluate their impact on measurement uncertainties in clinical practice. The most prominent physical X-ray beam quantities i.e., - air kerma, half-value layer, and X-ray tube voltage - were examined by measuring the response of two ionization chambers and six X-ray multimeters (XMMs) of different models. Measurements were performed using several anode/filter combinations and both with and without the compression paddle in the X-ray beam. Maximum differences of higher than 6 % were found for all quantities when the dosimeter displayed value was compared with the reference value or the variation within the clinical anode/filter combinations Mo/Mo and Mo/Rh were considered. The study showed that the calibration procedure with the W/Al anode/filter combination was reliable only for ionization chambers, and the response of XMMs varies in such a way that the calibration coefficient cannot be predicted between various measurement conditions used in calibration and clinical practices. XMM calibrations are typically performed without a compression paddle in the beam, and the response of the XMM changes when radiation quality is slightly altered. If XMM specific data is not available, based on this study, an additional uncertainty of 2 % (k = 1) could be used as a typical estimate, at least for air kerma measurements. XMMs should be used for clinical measurements in mammography only with correct settings. If the correct settings are not available, the XMMs should not be used or used only with extreme caution.

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.

PURPOSE: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. METHODS: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. RESULTS: The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. CONCLUSIONS: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach. HIGHLIGHTS: We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.

Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.

INTRODUCTION: Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies. METHODS: We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search. RESULTS: Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior. CONCLUSION: Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective. HIGHLIGHTS: We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.