Site-specific Range Research Articles

Mycoplasma genitalium in the US (MyGeniUS): Surveillance Data From Sexual Health Clinics in 4 US Regions.

Mycoplasma genitalium (MG) is on the CDC Watch List of Antimicrobial Resistance Threats, yet there is no systematic surveillance to monitor change. We initiated surveillance in sexual health clinics in 6 cities, selecting a quota sample of urogenital specimens tested for gonorrhea and/or chlamydia. We abstracted patient data from medical records and detected MG and macrolide-resistance mutations (MRMs) by nucleic acid amplification testing. We used Poisson regression to estimate adjusted prevalence ratios (aPRs) and 95% CIs, adjusting for sampling criteria (site, birth sex, symptom status). From October-December 2020 we tested 1743 urogenital specimens: 57.0% from males, 46.1% from non-Hispanic Black persons, and 43.8% from symptomatic patients. MG prevalence was 16.6% (95% CI: 14.9-18.5%; site-specific range: 9.9-23.5%) and higher in St Louis (aPR: 1.9; 1.27-2.85), Greensboro (aPR: 1.8; 1.18-2.79), and Denver (aPR: 1.7; 1.12-2.44) than Seattle. Prevalence was highest in persons <18 years (30.4%) and declined 3% per each additional year of age (aPR: .97; .955-.982). MG was detected in 26.8%, 21.1%, 11.8%, and 15.4% of urethritis, vaginitis, cervicitis, and pelvic inflammatory disease (PID), respectively. It was present in 9% of asymptomatic males and 15.4% of asymptomatic females, and associated with male urethritis (aPR: 1.7; 1.22-2.50) and chlamydia (aPR: 1.7; 1.13-2.53). MRM prevalence was 59.1% (95% CI: 53.1-64.8%; site-specific range: 51.3-70.6%). MRMs were associated with vaginitis (aPR: 1.8; 1.14-2.85), cervicitis (aPR: 3.5; 1.69-7.30), and PID cervicitis (aPR: 1.8; 1.09-3.08). MG infection is common in persons at high risk of sexually transmitted infections; testing symptomatic patients would facilitate appropriate therapy. Macrolide resistance is high and azithromycin should not be used without resistance testing.

ENHANCE DUNE-BUILDING PROCESSES WITH NATURE-BASED NOURISHMENT DESIGN

Vast coastal stretches around the world rely on dunes for flood protection. At the same time, the protection level of many dune systems can be undermined due to coastal erosion, sea-level rise, and greater population densities. To enhance dune building processes and the growth of coastal dunes, nature-based solutions, such as multi-purpose sand nourishments, are increasingly being implemented. However, the performance of the nourishment projects in terms of dune growth do not always match the expectations due to unforeseen supply-limiting factors (Hoonhout and de Vries, 2019). Sediment on a beach should be within a site-specific range of grain sizes to be available for pick up by the wind and deposition in the dunes. However, the grain-size characteristics do also influence several other supply-limiting factors, e.g., surface moisture, crust development, and beach slope, making nourishment design complicated. The process-based model Aeolis has been developed to predict aeolian transport rates considering several supply limiting factors. In this study, we introduce a new surface moisture functionality in Aeolis and demonstrate how the model can be used to optimize nourishment designs with respect to dune build-up. A number of test cases are presented to illustrate how grain size, sorting, and beach morphology are influencing aeolian transport rates.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/K_eiZ5kPk2g

Quantitative spatial distribution model of site-specific loess landslides on the Heifangtai terrace, China

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments.

New Range Margin Recommendations Based on Dose Calculation Uncertainties

To recommend new, treatment site specific range margins for proton therapy by re-evaluating the dose-calculation accuracy of analytical dose calculation (ADC) algorithms. We analyze 1920 treatment fields to provide new recommendations for range margins. Six treatment sites (liver, prostate, breast, head-and-neck, lung and medullo) were sub-divided into 12 categories. For each category, the range margins were estimated for target coverage using 2D range profiles of treatment fields in the patients comparing ADC to delivered (as simulated using the TOPAS Monte Carlo tool) fields. The fields were adjusted to ensure a perfect match of dose and range of delivered and ADC fields in water phantoms. The results for the sub-sites are shown in table 1. Overall, heterogeneous patient geometries are not well described resulting in an average range uncertainty of 4.8%, while homogenous sites could be delivered using a shorter range margin of on average 3.2%. We find that range margins can be reduced for some treatment fields while the majority of fields require margins slightly higher than currently used. However, when not correcting for loss of dose due to small field sizes, scattering etc. 3.5% should be used. Prostate fields could also use a constant range margin of 2.5 mm. For heterogeneous geometries (see table and figure) generic range margins of 5.3% may be necessary.Abstract TU_28_3172; Table 1New recommendations for range margins (without requiring changes to current procedures). Values in % of the range. An additional 1.2 mm for setup uncertainties are the same for all sites and not listed here.Treatment categoryNew Margin [%]Breast upper nodes5.3Fields involving nasal/mouth cavity4.8Lung5.2Neck Nodes3.8Medullo (upper spine)4.8Breast4.6Liver (with lung)3.7Medullo (lower spine)3.5Whole brain3.7Brain boosts / mets3.2Liver (no significant lung involvement)2.9Prostate2.5 mm (const.) Open table in a new tab

Tolerance design of patient-specific range QA using the DMAIC framework in proton therapy.

To implement the DMAIC (Define-Measure-Analyze-Improve-Control) can be used for customizing the patient-specific QA by designing site-specific range tolerances. The DMAIC framework (process flow diagram, cause and effect, Pareto chart, control chart, and capability analysis) were utilized to determine the steps that need focus for improving the patient-specific QA. The patient-specific range QA plans were selected according to seven treatment site groups, a total of 1437 cases. The process capability index, Cpm was used to guide the tolerance design of patient site-specific range. For prostate field, our results suggested that the patient range measurements were capable at the current tolerance level of ±1mm in clinical proton plans. For other site-specific ranges, we analyzed that the tolerance tends to be overdesigned to insufficient process capability calculated by the patient-specific QA data. The customized tolerances were calculated for treatment sites. Control charts were constructed to simulate the patient QA time before and after the new tolerances were implemented. It is found that the total simulation QA time was decreased on average of approximately 20% after establishing new site-specific range tolerances. We simulated the financial impact of this project. The QA failure for whole process in proton therapy would lead up to approximately 30% increase in total cost. DMAIC framework can be used to provide an effective QA by setting customized tolerances. When tolerance design is customized, the quality is reasonably balanced with time and cost demands.

Site-Specific Range Uncertainties Due to by Dose Calculation Algorithms for Proton Therapy

To investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict the range of proton fields and to verify currently used range uncertainty margins in proton therapy.

Site-specific range uncertainties caused by dose calculation algorithms for proton therapy

The purpose of this study was to assess the possibility of introducing site-specific range margins to replace current generic margins in proton therapy. Further, the goal was to study the potential of reducing margins with current analytical dose calculations methods. For this purpose we investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict the range of proton fields. Dose distributions predicted by an analytical pencil-beam algorithm were compared with those obtained using Monte Carlo (MC) simulations (TOPAS). A total of 508 passively scattered treatment fields were analyzed for seven disease sites (liver, prostate, breast, medulloblastoma–spine, medulloblastoma–whole brain, lung and head and neck). Voxel-by-voxel comparisons were performed on two-dimensional distal dose surfaces calculated by pencil-beam and MC algorithms to obtain the average range differences and root mean square deviation for each field for the distal position of the 90% dose level (R90) and the 50% dose level (R50). The average dose degradation of the distal falloff region, defined as the distance between the distal position of the 80% and 20% dose levels (R80–R20), was also analyzed. All ranges were calculated in water-equivalent distances. Considering total range uncertainties and uncertainties from dose calculation alone, we were able to deduce site-specific estimations. For liver, prostate and whole brain fields our results demonstrate that a reduction of currently used uncertainty margins is feasible even without introducing MC dose calculations. We recommend range margins of 2.8% + 1.2 mm for liver and prostate treatments and 3.1% + 1.2 mm for whole brain treatments, respectively. On the other hand, current margins seem to be insufficient for some breast, lung and head and neck patients, at least if used generically. If no case specific adjustments are applied, a generic margin of 6.3% + 1.2 mm would be needed for breast, lung and head and neck treatments. We conclude that the currently used generic range uncertainty margins in proton therapy should be redefined site specific and that complex geometries may require a field specific adjustment. Routine verifications of treatment plans using MC simulations are recommended for patients with heterogeneous geometries.

Skin Reaction for Spine and Brain Patients at a Health Proton Therapy Center: A Retrospective Study and Physics Consideration

for proton therapy patients. The range uncertainty guidelines in treatment planning depend on the proton therapy center. At our institution we add 3.5% to the prescribed range plus 1 mm. Our results show that for some sites (e.g., liver) this seems to be generous, while for other sites, it largely depends on the tumor location and beam angle. We conclude that the use of site-specific range uncertainty margins in proton therapy might be appropriate. Note that TOPAS will be made available to the proton therapy community in 2013. Author Disclosure: H. Paganetti: None. J. Schuemann: None. C. Grassberger: None. J. Verburg: None. D. Giantsoudi: None. M. Moteabbed: None. C. Min: None. M. Testa: None. B. Faddegon: None. J. Perl: None.

Adaptive Replanning is Required During Intensity Modulated Proton Therapy For Head-and-Neck Cancers

for proton therapy patients. The range uncertainty guidelines in treatment planning depend on the proton therapy center. At our institution we add 3.5% to the prescribed range plus 1 mm. Our results show that for some sites (e.g., liver) this seems to be generous, while for other sites, it largely depends on the tumor location and beam angle. We conclude that the use of site-specific range uncertainty margins in proton therapy might be appropriate. Note that TOPAS will be made available to the proton therapy community in 2013. Author Disclosure: H. Paganetti: None. J. Schuemann: None. C. Grassberger: None. J. Verburg: None. D. Giantsoudi: None. M. Moteabbed: None. C. Min: None. M. Testa: None. B. Faddegon: None. J. Perl: None.

Three-dimensional topographical variation of femoral cartilage T2 in healthy volunteer knees

Quantitative knee cartilage T2 assessment on limited two-dimensional midsagittal or midcoronal planes may be insufficient to assess variations in normal cartilage composition. The purpose of this work was to reveal characteristic 3D distribution of T2 values in femoral cartilage in healthy volunteer knees. Sixteen volunteers were enrolled in this study. One knee joint in each volunteer was imaged using a 3D fast image employing steady-state acquisition cycled phases (FIESTA-C) sequence for modeling distal femoral morphology, as well as a sagittal T2 mapping of cartilage. 3D distribution of cartilage T2 values was generated for the femoral condyles. At each medial and lateral condyle, four regions of interest (ROI) were manually defined based on the cartilage covered by the 3D surface model of the medial and lateral menisci. The 3D maps showed a relatively inhomogeneous distribution of cartilage T2 on the medial and lateral condyles. Cartilage T2 values in the internal half of the weight-bearing zone were significantly higher than those in all other zones on both lateral and medial condyles. Analysis of 3D distribution of femoral cartilage T2 may be valuable in determining the site-specific normal range of cartilage T2 in the healthy knee joint.

SU-F-BRA-12: Comprehensive Uncertainty Analysis of Proton Stopping-Power-Ratio Estimation Using a KV-MV Dual Energy CT Scanner (DECT) for Margin Reduction

Purpose: To analyze the uncertainties in proton stopping-power-ratios (SPRs) calculation using a kV-MV DECT and evaluate the potential for margin reduction for proton therapyMethods: Sources of uncertainties in SPR estimation were broken into five categories: CT imaging uncertainties, CT modeling error, uncertainties in the mean-excitation-energy, SPR variation with proton energy and uncertainties due to variations in human tissue compositions. Additionally, lung, soft and bone tissues were analyzed separately because their uncertainties are too different to be considered as the same tissue type. The uncertainty of 1-standard-deviation (1-SD) was determined for each category and each tissue type. To derive a realistic composite uncertainty, tissue proportions of each tissue type were measured in three treatment sites: prostate, lung and head-and-neck, with 5 proton patient cases for each site. The beam angles and treatment target in the original treatment plan were used. Ray tracing technique was used to determine the tissue proportion from the skin surface to the distal surface of the target volume. The range uncertainty was determined for each ray line, and the 95th-percentile uncertainty was determined for the population of the ray lines belonging to the same tumor site. Results: The total uncertainty in SPR estimation (1-SD) was estimated to be 3.8%, 0.8% and 1.3% for lung, soft and bone tissues, respectively. The combined range uncertainty estimates (95th-percentile) for prostate, lung and head-and-neck treatment sites were 1.6%, 2.1% and 1.6%, respectively. Conclusions: The current practice uses a 3.5% range uncertainty for SPR estimation for all treatment sites. We found it is important to separate tissue types in order to better estimate the site-specific range uncertainties. The use of kV-MV DECT can reduce current margin by half for prostate and head-and-neck sites (1.6% vs. 3.5%) but can only reduce the uncertainty by 40% for lung cancer site (2.1% vs. 3.5%). Research partially sponsored by Varian Medical Systems.

Data quality at the Cancer Registry of Norway: An overview of comparability, completeness, validity and timeliness

AimTo provide a comprehensive evaluation of the quality of the data collected on both solid and non-solid tumours at the Cancer Registry of Norway (CRN). MethodsEstablished quantitative and semi-quantitative methods were used to assess comparability, completeness, accuracy and timeliness of data for the period 1953–2005, with special attention to the registration period 2001–2005. ResultsThe CRN coding and classification system by and large follows international standards, with some further subdivisions of morphology groupings performed in-house. The overall completeness was estimated at 98.8% for the registration period 2001–2005. There remains a variable degree of under-reporting particularly for haematological malignancies (C90–95) and tumours of the central nervous system (C70–72). For the same period, 93.8% of the cases were morphologically verified (site-specific range: 60.0–99.8%). The under-reporting in 2005 due to timely publication is estimated at 2.2% overall, based on the number of cases received at the registry during the following year. ConclusionThis review suggests the routines in place at the CRN yields comparable data that can be considered reasonably accurate, close-to-complete and timely, thereby justifying our policy of the reporting of annual incidence one year after the year of diagnosis.

Spatial Variation of Urease Activity Measured in Soil Monitoring

Since time and money are usually limited, researchers need to optimize and select sampling scales that reflect the spatial variation of the properties under consideration. This paper addresses the question how sampling designs can be evaluated with respect to the selection of sampling scales when monitoring soil enzyme activity. The proposed methodology is illustrated by studying the spatial variation of urease activity and organic C content at three sites that have different types of land use (pasture, arable land, and forest) as an example. At each site, an area of 0.75 hectares was sampled using a hierarchical multistage sampling scheme called nested sampling. Large differences in both the statistical and spatial distributions were observed between the three sites. For the arable land, a considerable part of the total variance of the two variables, urease activity and organic C content, could be statistically explained by stratifying the samples according to soil color, thereby reflecting differences in the origin of the organic matter. No correlation between the two variables was found within the forest and the pasture site, as well as within each of the two strata distinguished by soil color on the arable land site. Spatial autocorrelation of urease activity was found only for sample spacings of <1 m for the pasture site, while autocorrelation extended up to 15 m for the other two sites. To represent the full site-specific range of spatial variation, the sample spacings must encompass these distances. Because of its efficiency in identifying spatial scales of variation, nested sampling is especially well suited for application to pilot surveys by providing a basis for the design of more intensive sampling campaigns, including long-term soil monitoring programs.