Comparisons of treatment optimization directly incorporating systematic patient setup uncertainty with a margin-based approach.
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ABSTRACT:
SUBMITTER: Moore JA
PROVIDER: S-EPMC3293365 | biostudies-other | 2012 Feb
REPOSITORIES: biostudies-other
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Project description:To develop a method for creating variable planning margins around a clinical treatment volume (CTV) and to evaluate its application in head and neck cancer radiotherapy in accounting for locoregional variations of nonrigid setup uncertainties.Ten computed tomography (CT) images (with a resolution of 0.68 × 0.68 × 2.5 mm(3)) of a head and neck cancer patient were acquired from the first two weeks of treatment for this study. Five rigid structures (the C2, C5, and caudal C7 vertebrae, mandible, and jugular notch) were used as the landmarks for creating variable local margins. At different CTV locations, local margins were calculated as the weighted average of margins determined at different landmark points from previous studies. The weight was determined by a Gaussian falloff function of the distance between the current location and each landmark point. The CTV delineated on the planning CT image, spanning from the upper portion of the mouth to the lower part of the neck, was expanded to form the planning treatment volume (PTV) with either variable or the conventional constant margins. To evaluate the target coverage, the original planning CTV was deformably mapped to each daily treatment CT using a deformable image registration method. We examined the overlap of the deformed CTV and the rigidly aligned PTV for each margin design strategy and compared the efficacy of the variable margin with the constant margin approach.For the variable margin approach with a baseline C2 margin of 2.5 mm in the left-right, anterior-posterior, and superior-inferior directions, an average of 99.2% of the CTV was within the PTV, and for the approach with a constant 2.5 mm margin, an average of 97.9% of the CTV was within the PTV. With a baseline margin of 2.0 mm, the variable margin approach had an average coverage of 97.8%, similar to that of the constant 2.5 mm margin approach. However, its average nonoverlapped PTV proportion was 32.4%, smaller than that of the constant 2.5 mm margin approach (33.7%). Paired t-tests of computations from the ten treatment fractions showed no significant difference in CTV coverage for the variable margin approach with a baseline of 2.0 mm and the constant 2.5 margin approach (p = 0.054), but the nonoverlapped PTV proportion was significantly smaller for the variable margin approach with a baseline of 2.0 mm than for the constant 2.5 mm margin approach (p < 0.0001). The CTV coverage with the variable margin approach was also significantly higher than with the constant margin approach in the lower neck area, where a larger setup error normally occurs.We implemented a variable margin approach to account for locoregional variations of setup uncertainties for head and neck cancer radiotherapy, and demonstrated the effectiveness of this approach when compared with the conventional global constant margin expansion approach, where the treatment target spreads out to a broad region. As variable margin data become available and more clinical studies are performed, this approach could be applicable to other treatment sites as well.
| S-EPMC3416883 | biostudies-other
Project description:The purpose of this study was to evaluate setup uncertainties for brain sites with ExacTrac X-Ray 6D system and to provide optimal margin guidelines. Fifteen patients with brain tumor were included in this study. Two X-ray images with ExacTrac X-Ray 6D system were used to verify patient position and tumor target localization before each treatment. The 6D fusion software first generates various sets of DRRs with position variations in both three translational and three rotational directions (six degrees of freedom) for the CT images. Setup variations (translation and rotation) after correction were recorded and corrected before treatment. The 3D deviations are expressed as mean ± standard deviation. The random error (?(?i)), systematic error (?i), and group systematic error (M(?i)) for the different X-ray were calculated using the definitions of van Herk.(1) Mean setup errors were calculated from X-ray images acquired after all fractions. There is moderate patient-to-patient variation in the vertical direction and small variations in systematic errors and magnitudes of random errors are smaller. The global systematic errors were measured to be less than 2.0 mm in each direction. Random component of all patients are smaller ranging from 0.1-0.3 mm small. The safety margin (SM) to the lateral, is 0.5 mm and 2.6 mm for van Herk(1) and Stroom et al.,(2) respectively, craniocaudal axis is 1.5 mm and 3.4 mm, respectively, and with respect to the antero-posterior axis, 2.3 mm and 3.9 mm. Daily X-ray imaging is essential to compare and assess the accuracy of treatment delivery to different anatomical locations.
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Project description:Matching metagenomic and/or metatranscriptomic data, currently often under-used, can be useful reference for metaproteomic tandem mass spectra (MS/MS) data analysis. Here we developed a software pipeline for identification of peptides and proteins from metaproteomic MS/MS data using proteins derived from matching metagenomic (and metatranscriptomic) data as the search database, based on two novel approaches Graph2Pro (published) and Var2Pep (new). Graph2Pro retains and uses uncertainties of metagenome assembly for reference-based MS/MS data analysis. Var2Pep considers the variations found in metagenomic/metatranscriptomic sequencing reads that are not retained in the assemblies (contigs). The new software pipeline provides one stop application of both tools, and it supports the use of metagenome assembly from commonly used assemblers including MegaHit and metaSPAdes. When tested on two collections of multi-omic microbiome data sets, our pipeline significantly improved the identification rate of the metaproteomic MS/MS spectra by about two folds, comparing to conventional contig- or read-based approaches (the Var2Pep alone identified 5.6% to 24.1% more unique peptides, depending on the data set). We also showed that identified variant peptides are important for functional profiling of microbiomes. All results suggested that it is important to take into consideration of the assembly uncertainties and genomic variants to facilitate metaproteomic MS/MS data interpretation.
| S-EPMC6692780 | biostudies-literature
Project description:While genomics provides new clinical opportunities, its complexity generates uncertainties. This systematic review aimed to summarize what is currently known about the experience of uncertainty for adult patients undergoing cancer genomic testing. A search of five databases (2001 to 2018) yielded 6508 records. After removing duplicates, abstract/title screening, and assessment of full articles, ten studies were included for quality appraisal and data extraction. Qualitative studies were subjected to thematic analysis, and quantitative data were summarized using descriptive statistics. Cancer genomic results reduced uncertainty for patients regarding treatment decisions but did not reduce uncertainty in the risk context. Qualitative and quantitative data synthesis revealed four themes: (1) coexisting uncertainties, (2) factors influencing uncertainty, (3) outcomes of uncertainty, and (4) coping with uncertainty. Uncertainty can motivate, or be a barrier to, pursuing cancer genomic testing. Appraisal of uncertainty influences the patient experience of uncertainty, the outcome of uncertainty for patients, as well as the coping strategies utilized. While this systematic review found that appraisal of uncertainty is important to the patients' experience of uncertainty in the cancer genomic context, more mixed methods longitudinal research is needed to address the complexities that contribute to patient uncertainty across the process.
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Project description:BACKGROUND: An important step in strain optimization is to identify reactions whose activities should be modified to achieve the desired cellular objective. Preferably, these reactions are identified systematically, as the number of possible combinations of reaction modifications could be very large. Over the last several years, a number of computational methods have been described for identifying combinations of reaction modifications. However, none of these methods explicitly address uncertainties in implementing the reaction activity modifications. In this work, we model the uncertainties as probability distributions in the flux carrying capacities of reactions. Based on this model, we develop an optimization method that identifies reactions for flux capacity modifications to predict outcomes with high statistical likelihood. RESULTS: We compare three optimization methods that select an intervention set comprising up- or down-regulation of reaction flux capacity: CCOpt (Chance constrained optimization), DetOpt (Deterministic optimization), and MCOpt (Monte Carlo-based optimization). We evaluate the methods using a Monte Carlo simulation-based method, MCEval (Monte Carlo Evaluations). We present two case studies analyzing a CHO cell and an adipocyte model. The flux capacity distributions required for our methods were estimated from maximal reaction velocities or elementary mode analysis. The intervention set selected by CCOpt consistently outperforms the intervention set selected by DetOpt in terms of tolerance to flux capacity variations. MCEval shows that the optimal flux predicted based on the CCOpt intervention set is more likely to be obtained, in a probabilistic sense, than the flux predicted by DetOpt. The intervention sets identified by CCOpt and MCOpt were similar; however, the exhaustive sampling required by MCOpt incurred significantly greater computational cost. CONCLUSIONS: Maximizing tolerance to variable engineering outcomes (in modifying enzyme activities) can identify intervention sets that statistically improve the desired cellular objective.
| S-EPMC3626866 | biostudies-other
Project description:Clear communication with patients upon emergency department (ED) discharge is important for patient safety during the transition to outpatient care. Over one-third of patients are discharged from the ED with diagnostic uncertainty, yet there is no established approach for effective discharge communication in this scenario. From 2017 to 2019, the authors developed the Uncertainty Communication Checklist for use in simulation-based training and assessment of emergency physician communication skills when discharging patients with diagnostic uncertainty. This development process followed the established 12-step Checklist Development Checklist framework and integrated patient feedback into 6 of the 12 steps. Patient input was included as it has potential to improve patient-centeredness of checklists related to assessment of clinical performance. Focus group patient participants from 2 clinical sites were included: Thomas Jefferson University Hospital, Philadelphia, PA, and Northwestern University Hospital, Chicago, Illinois.The authors developed a preliminary instrument based on existing checklists, clinical experience, literature review, and input from an expert panel comprising health care professionals and patient advocates. They then refined the instrument based on feedback from 2 waves of patient focus groups, resulting in a final 21-item checklist. The checklist items assess if uncertainty was addressed in each step of the discharge communication, including the following major categories: introduction, test results/ED summary, no/uncertain diagnosis, next steps/follow-up, home care, reasons to return, and general communication skills. Patient input influenced both what items were included and the wording of items in the final checklist. This patient-centered, systematic approach to checklist development is built upon the rigor of the Checklist Development Checklist and provides an illustration of how to integrate patient feedback into the design of assessment tools when appropriate.
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Project description:Robust optimization leads to intensity-modulated proton therapy (IMPT) plans that are less sensitive to uncertainties and superior in terms of organs-at-risk (OARs) sparing, target dose coverage, and homogeneity compared to planning target volume (PTV)-based optimized plans. Robust optimization incorporates setup and range uncertainties, which implicitly adds margins to both targets and OARs and is also able to compensate for perturbations in dose distributions within targets and OARs caused by uncertainties. In contrast, the traditional PTV-based optimization considers only setup uncertainties and adds a margin only to targets but no margins to the OARs. It also ignores range uncertainty. The purpose of this work is to determine if robustly optimized plans are superior to PTV-based plans simply because the latter do not assign margins to OARs during optimization.The authors retrospectively selected from their institutional database five patients with head and neck (H&N) cancer and one with prostate cancer for this analysis. Using their original images and prescriptions, the authors created new IMPT plans using three methods: PTV-based optimization, optimization based on the PTV and planning risk volumes (PRVs) (i.e., "PTV+PRV-based optimization"), and robust optimization using the "worst-case" dose distribution. The PRVs were generated by uniformly expanding OARs by 3 mm for the H&N cases and 5 mm for the prostate case. The dose-volume histograms (DVHs) from the worst-case dose distributions were used to assess and compare plan quality. Families of DVHs for each uncertainty for all structures of interest were plotted along with the nominal DVHs. The width of the "bands" of DVHs was used to quantify the plan sensitivity to uncertainty.Compared with conventional PTV-based and PTV+PRV-based planning, robust optimization led to a smaller bandwidth for the targets in the face of uncertainties {clinical target volume [CTV] bandwidth: 0.59 [robust], 3.53 [PTV+PRV], and 3.53 [PTV] Gy (RBE)}. It also resulted in higher doses to 95% of the CTV {D(95%): 60.8 [robust] vs 59.3 [PTV+PRV] vs 59.6 [PTV] Gy (RBE)}, smaller D(5%) (doses to 5% of the CTV) minus D(95%) {D(5%) - D(95%): 13.2 [robust] vs 17.5 [PTV+PRV] vs 17.2 [PTV] Gy (RBE)}. At the same time, the robust optimization method irradiated OARs less {maximum dose to 1 cm(3) of the brainstem: 48.3 [robust] vs 48.8 [PTV+PRV] vs 51.2 [PTV] Gy (RBE); mean dose to the oral cavity: 22.3 [robust] vs 22.9 [PTV+PRV] vs 26.1 [PTV] Gy (RBE); maximum dose to 1% of the normal brain: 66.0 [robust] vs 68.0 [PTV+PRV] vs 69.3 [PTV] Gy (RBE)}.For H&N cases studied, OAR sparing in PTV+PRV-based optimization was inferior compared to robust optimization but was superior compared to PTV-based optimization; however, target dose robustness and homogeneity were comparable in the PTV+PRV-based and PTV-based optimizations. The same pattern held for the prostate case. The authors' data suggest that the superiority of robust optimization is not due simply to its inclusion of margins for OARs, but that this is due mainly to the ability of robust optimization to compensate for perturbations in dose distributions within target volumes and normal tissues caused by uncertainties.
| S-EPMC3562272 | biostudies-other
Project description:The purpose of multi-level contribution margin accounting in cost accounting is to analyze the profitability of products and organizational entities with appropriate allocation of fixed costs and to provide relevant information for short-term, medium- and longer-term decisions. However, the conventional framework of multi-level contribution margin accounting does not usually incorporate a mathematical optimization method that simultaneously integrates variable and fixed costs to determine the best possible product mix within hierarchically structured organizations. This may be surprising in that operations research provides an optimization model in the form of the fixed-charge problem (FCP) that takes into account not only variable costs but also fixed costs of the activities to be planned. This paper links the two approaches by expanding the FCP to a multi-level fixed-charge problem (MLFCP), which maps the hierarchical decomposition of fixed costs in accordance with multi-level contribution margin accounting. In this way, previously hidden optimization potentials can be made visible within the framework of multi-level contribution margin accounting. Applying the linkage to a case study illustrates that the original assessment of profitability gained on the sole basis of a multi-level contribution margin calculation might turn out to be inappropriate or even inverted as soon as mathematical optimization is utilized: products, divisions, and other reference objects for fixed cost allocation, which at first glance seem to be profitable (or unprofitable) might be revealed as actually unprofitable (or profitable), when the multi-level contribution margin calculation is linked to the MLFCP. Furthermore, the proposed concept facilitates assessment of the costs of an increasing variant diversity, which also demonstrates that common rules on how to interpret a multi-level contribution margin calculation may have to be revised in some cases from the viewpoint of optimization. Finally, the impact of changes in the fixed cost structure and other parameters is tested via sensitivity analyses and stochastic optimization.
| S-EPMC7905367 | biostudies-literature
Project description:During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.
| S-EPMC10109485 | biostudies-literature
Project description:BackgroundPhysician cost profiles (also called efficiency or economic profiles) compare the costs of care provided by a physician to his or her peers. These profiles are increasingly being used as the basis for policy applications such as tiered physician networks. Tiers (low, average, high cost) are currently defined by health plans based on percentile cut-offs which do not account for statistical uncertainty. In this paper we compare the percentile cut-off method to another method, using statistical testing, for identifying high-cost or low-cost physicians.MethodsWe created a claims dataset of 2004-2005 data from four Massachusetts health plans. We employed commercial software to create episodes of care and assigned responsibility for each episode to the physician with the highest proportion of professional costs. A physicians' cost profile was the ratio of the sum of observed costs divided by the sum of expected costs across all assigned episodes. We discuss a new method of measuring standard errors of physician cost profiles which can be used in statistical testing. We then assigned each physician to one of three cost categories (low, average, or high cost) using two methods, percentile cut-offs and a t-test (p-value < or = 0.05), and assessed the level of disagreement between the two methods.ResultsAcross the 8689 physicians in our sample, 29.5% of physicians were assigned a different cost category when comparing the percentile cut-off method and the t-test. This level of disagreement varied across specialties (17.4% gastroenterology to 45.8% vascular surgery).ConclusionsHealth plans and other payers should incorporate statistical uncertainty when they use physician cost-profiles to categorize physicians into low or high-cost tiers.
| S-EPMC2842268 | biostudies-literature