All of them underwent a course of postoperative tangential breast irradiation. The patients were prospectively randomised into two groups. The results of both treatment planning procedures were compared. The time expenditure could be reduced from a median of Furthermore the treatment planning for the patient could be reduced from a median of
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Login or Create Account. Allow All Cookies. Yang, H. Sun, X. Wei, T. Shi, and H. Not Accessible Your account may give you access. Abstract We present simulation results based on a compression plates based DOT system for breast cancer imaging.
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Metrics details. The economic evaluation of stratified breast cancer screening gains momentum, but produces also very diverse results. Systematic reviews so far focused on modeling techniques and epidemiologic assumptions.
However, cost and utility parameters received only little attention. This systematic review assesses simulation models for stratified breast cancer screening based on their cost and utility parameters in each phase of breast cancer screening and care.
A literature review was conducted to compare economic evaluations with simulation models of personalized breast cancer screening. Study quality was assessed using reporting guidelines. Cost and utility inputs were extracted, standardized and structured using a care delivery framework. Studies were then clustered according to their study aim and parameters were compared within the clusters. Eighteen studies were identified within three study clusters.
Reporting quality was very diverse in all three clusters. Only two studies in cluster 1, four studies in cluster 2 and one study in cluster 3 scored high in the quality appraisal. In addition to the quality appraisal, this review assessed if the simulation models were consistent in integrating all relevant phases of care, if utility parameters were consistent and methodological sound and if cost were compatible and consistent in the actual parameters used for screening, diagnostic work up and treatment.
Of 18 studies, only three studies did not show signs of potential bias. This systematic review shows that a closer look into the cost and utility parameter can help to identify potential bias. Future simulation models should focus on integrating all relevant phases of care, using methodologically sound utility parameters and avoiding inconsistent cost parameters. Stratified breast screening aims at improving routine screening by allowing a stratification between risk groups.
Stratified screening protocols could then be developed for high-risk and low-risk groups, and the balance between harmful and beneficial screening effects could be recalibrated. Owing to the complex nature of stratified screening programs and the massive cost implications of randomized control trials, simulation modeling is often the only method available or feasible for economic evaluation. Health economic modeling aims to support political decision-making, but its results are often very diverse.
Part of this diversity was found to be related to a significant diversity in simulation techniques and modeling approaches. A recent review by Elkin et al. They found that the targeting mechanism is rarely included in the decision analytical models, but influences the results of cost-effectiveness studies substantially. Three years later, Hatz et al. The authors summarized how stratified approaches do not necessarily lead to superior or inferior cost-effectiveness compared with existing health care approaches.
They also found that stratified screening was often more cost-effective than stratified treatment but, overall, the variation in these studies was too substantial to reach a conclusion. Koleva-Kolarova et al. They assessed seven original models and compared disease, population and intervention input parameters as well as modeling approach and outcomes. They found that all of them predicted mortality reduction similar to randomized control trials.
Owing to the large variety in personalization approaches, systematic reviews struggle with comparing the specific stratification suggestions in the complex continuum of care for breast cancer. Onega et al. Their framework described the complete continuum of breast screening from risk assessment to treatment and thus supported the assessment of the care continuum in simulation models for stratified screening.
A systematic review focusing on the integration of the phases of care and an assessment of the cost and utility parameters used in each of the phases thus might be helpful to further assess the simulation models and evaluate if the underlying structural assumptions are appropriate for the respective research task. This article describes such a systematic review and presents an analysis of cost and utility parameters using the Onega framework [ 5 ]. It assesses simulation models for stratified breast cancer screening according to the integration of the phases of care delivery and illustrates the variation in cost and utility parameters.
By focusing on their validity and their potential impact on results, the importance of the respective phase of care for the evaluation can be assessed and potential of bias can be identified. Its aim is not to evaluate if stratified screening is superior to routine screening, but to evaluate the economic modeling approaches in this field.
Stratification can be used in many areas of the breast cancer patient pathway. We used an adaptation of their framework to categorize screening approaches into clusters focusing on risk assessment, detection, diagnosis or breast cancer treatment. This study focuses on approaches aiming at the stratification of patient groups into risk levels and the selection of the best screening strategy for each risk group.
The search strategy uses very broad descriptions for stratification or personalization , the screening for breast cancer and also for studies including cost-effectiveness. Keywords and synonyms were used in titles and abstracts. Since the terminology for simulation modeling is quite diverse, no specific search term was used for the database search. The search strategy thus was designed to identify economic evaluations for personalized breast cancer screening.
In order to identify simulation models, all identified economic evaluation were screened for the population in their methodology. If simulated or hypothetical populations were used, studies were identified as simulation models. Studies of interest use comparative simulation approaches and compare a variety of screening strategies, of which one needs to be routine mammography screening and at least one needs to suggest a stratified screening approach. They do not necessarily need to reflect the current technology or current research, but rather a fitting economic evaluation.
The literature search results are then filtered using the following inclusion criteria:. Focus on new screening strategies, not on methods to increase participation in existing strategies. Exclusion criteria further filter out non-peer-reviewed publications such as conference abstracts, commentaries or study protocols, economic evaluations with updates, economic evaluations that do not use a simulation approach or only review other simulations, economic evaluations that do not use utility values, studies focusing primarily on women with a specific socio-economic or racial background, which are not comparable to other studies.
The literature search and evaluation were conducted with the help of a second researcher and a review protocol. Literature appraisal is based on an overview of reporting guidelines [ 7 ] and challenges in the field of the economic evaluation of personalized medicine as formulated by Annemans et al.
The overview [ 7 ] compares the most commonly used quality appraisal tools for health economic modeling [ 9 , 10 , 11 ]. The list extracted from this review [ 11 ] adds additional elements [ 8 ]. Annemans et al. While some of these items are already adequately reflected in existing quality appraisal tools, such as the importance of defining the scope of the economic evaluation, others are not yet completely addressed, for example the special importance of incorporating both test and intervention specifications into the model.
This quality appraisal helps to establish a benchmark for a comparison of the study quality for economic evaluations in personalized medicine.
A second researcher validated the quality appraisal. Additional file 1 : Supplementary material S2 includes the checklist and explanation of the new items as well as an illustration of the definition of good quality used for the quality criteria. Data extraction utilizes the framework in Fig. In each of these phases, costs can occur and quality of life can be affected. Data extraction focuses on the price parameters of technologies and quality of life decrement used in each of these phases.
All monetary parameters are standardized to USD, as the latest available year of purchasing power-parity-based PPP exchange rates, and USD, as the most common currency. Quality of life decrements are reported as percentages from the base value in order to normalize utilities between studies using age-specific utility weights and studies assuming perfect health independent of age.
Conceptual framework, adapted from Onega et al. After removing duplicates, studies were assessed for inclusion criteria. Of these, studies did not focus on breast cancer, were not cost-effectiveness studies, did not focus on screening, studies did not assess personalized approaches and studies focused on strategies for raising screening uptake or re-attendance. Of these, 52 studies were excluded because they were conference abstracts, outdated versions of newer publications, study protocols or comments on other papers, did not describe results for risk groups, focused on co-morbid study populations, did not apply health economic models, did not measure quality of life with utilities or only described models without implementing them.
In all, 18 studies fulfilled all criteria and were included in this review. In 18 studies, three distinct clusters of stratification approaches were identified. One cluster focuses on stratified screening in the general population, one focuses on a pre-selected high-risk population and one evaluates newly introduced risk assessment technologies. Studies in cluster 1 use risk factors describing moderate risk to generate risk clusters. These risk factors are for example familial risk, age, breast density, history of biopsy and others.
Schousboe et al. Sprague et al. However, Tosteson et al. Stout et al. Trentham-Dietz et al. Tosteson et al. The other studies in cluster 1 suggest personalized screening frequencies.
In cluster 2, studies focus on identifying the right screening technology for women already identified with high risk of breast cancer. Ahern et al. Pataky, Ismail et al. They evaluate using annual mammography screening instead biennial for this risk group.
Studies suggest stratification by adding MRI for women at very high risk. Cott Chubiz et al. The other studies [ 23 , 24 , 25 , 26 ] propose annual screening using both technologies. Taneja et al. In cluster 3, studies assess the introduction of additional risk assessment to stratify women according to their risk.
The focus in these studies is on an earlier stage of the care continuum compared to the studies in cluster 1 and 2. Ozanne and Esserman [ 28 ] evaluate atypia testing to identify women for tamoxifen prevention.
Manchanda et al. Folse et al. When assessing the quality of simulation studies, the quality of the actual simulation can only be assessed as far as all quality-relevant items are reported in the main article, supplementary information or referenced articles and websites.
In some cases, the actual quality of the simulation model might be higher, but cannot be assessed since the relevant items were not reported in the article or referenced article. The criteria list includes 40 items with 40 positive answers as the maximum possible score.
Longer bars in Fig. The bars use different colors to identify the quality categories.