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Digital mammography and tomosynthesis for breast cancer diagnosis

Editor who approved publication: The objective of this study was to conduct a value analysis of digital breast tomosynthesis DBT for breast cancer screening among women enrolled in US commercial health insurance plans to assess the potential budget impact associated with the clinical benefits of DBT. An economic model was developed to estimate the system-wide financial impact of DBT as a breast cancer screening modality within a hypothetical US managed care plan with one million members.

The model focused on two main drivers of DBT value, ie, the capacity for DBT to reduce the number of women recalled for additional follow-up imaging and diagnostic services and the capacity of DBT to facilitate earlier diagnosis of cancer at less invasive stages where treatment costs are lower. Comparative clinical and economic outcomes were simulated for one year following screening and compared on an incremental basis.

Base-case analysis results show that 4,523 women in the hypothetical million member health plan who are screened using DBT avoid the use of follow-up services.

The results of this study demonstrate clinical and economic favorability of DBT for breast cancer screening among commercially-insured US women.

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  • CC slice 36, mediolateral oblique MLO slice 40, measuring about 1;
  • As more is learned about the potential for DBT to streamline diagnostic pathways, it is possible that additional cost savings not included in our current analysis will be identified.

Wider adoption of DBT mammography presents an opportunity to deliver value-based care in the US health care system. Three-dimensional mammography referred to in this paper as digital breast tomosynthesis, or DBT is a technological advancement of mammography over two-dimensional digital mammography. Materials and methods Economic model overview An economic model was developed to estimate the system-wide financial impact of DBT as a breast cancer screening modality within a hypothetical US managed care plan with one million members.

To delineate the impact and value of DBT, two screening mammography scenarios were considered for all eligible women in the health plan who undergo annual breast cancer screening mammography: The differences between the two scenarios represent the value of DBT, and this is expressed through a variety of metrics commonly used by health care decision-makers. The primary driver of DBT economic value comes from the capacity of DBT to reduce the number of women who are recalled for additional follow-up imaging and diagnostic testing services, and the corresponding reduction in the costs of using these health care resources.

A secondary driver of DBT economic value is the capacity of DBT to facilitate earlier diagnosis of cancer, particularly diagnosis of cancers at less invasive stages when treatment costs are lower.

Together, these value drivers serve to offset additional reimbursement costs of DBT and produce a net cost savings for the hypothetical health plan under consideration. Model inputs Data sources Parameter inputs and data sources for the economic model are summarized in Table 1. These values served as the default base-case parameters for the model, although the model was designed with extensive flexibility to accommodate variations and perform customized analyses specified by the model user.

Truven Health Analytics MarketScan Commercial Claims and Encounters Database, which contains medical and prescription data on approximately 35 million US employees annually and their dependants with employer-sponsored private health insurance. Truven Health Analytics MarketScan Medicare Supplemental Database, which contains medical and prescription data on approximately 3 million retirees annually with Medicare supplemental or Medigap insurance paid for by employers.

It includes the Medicare-covered portion of payment Coordination of Benefits Amount and the employer-paid portion. Medicare supplemental insurance typically covers copayments, coinsurance, and deductibles not covered by traditional Medicare plans. Patient population The Truven Health MarketScan Commercial and Medicare Supplemental Databases were analyzed over for a 3-year 2010—2012 period to identify women aged 40—75 years undergoing screening mammography.

Women were required to have 12 months pre-index and 6 months post-index continuous enrollment in the claims database. Women with any breast cancer screening imaging procedure or a breast cancer diagnosis in the 12-month pre-index period were digital mammography and tomosynthesis for breast cancer diagnosis.

Thus, the rate of women utilizing follow-up imaging was estimated by identifying women who received a diagnostic mammogram Healthcare Common Procedure Coding System codes G0204 or G0206, and CPT codes 77055 or 77056 or a breast ultrasound procedure CPT code 76645 in the 6 months following the index screen.

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Follow-up service costs were the sum of: Follow-up services costs were assumed to occur within the 6-month period following the index mammogram; costs in the remaining 6 months of the year were not included as they could represent utilization associated with a subsequent screening event. A detailed breakdown of the derivation of costs is shown in the database analysis results in Table 2.

Breast cancer treatment costs were not included, nor were patient payments eg, copayment or coinsurance. Table 2 Cost estimation for follow-up services Note: A fixed percentage 18. As shown in Table 1and as implemented in the model, 18. First, the mean total health plan cost of a newly diagnosed breast cancer patient in the year following diagnosis was derived from the administrative claims data. This value was distributed across stages 1—4 using the same stage distribution of breast cancer costs as presented in a study of the costs for women with newly-diagnosed breast cancer by disease stage, and matched to noncancer controls.

Mammogram Basics

These values were applied to the overall estimate from the claims database analyses to yield the cost distribution by stage shown in Table 1. Stage 0 costs were similarly estimated using the percentage difference between stage 0 and stage 1 costs derived from a 4-year longitudinal study in a US health maintenance organization population, 38 which was later adapted for use in a cost-effectiveness study of computer-aided detection screening mammography.

Model outputs A variety of informative outputs are generated by the model. Cost outcomes are calculated similarly. Again, the incremental difference represents the impact of DBT.

  • Also, in somewhat rare circumstances, women undergoing screening ultrasound following a screening mammogram due to dense breast tissue may have been misclassified as recalls, which could have artificially inflated the rate we report for women utilizing follow-up services;
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  • Comparing the diagnostic efficacy of full field digital mammography with digital breast tomosynthesis using BIRADS score in a tertiary cancer care hospital;
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An identical set of cost calculations is performed and expressed on a per patient basis. Finally, the cost calculations are expressed in terms of total and incremental costs on a per member per month basis.

Results Database analysis results Results from the database analysis can be found in Table 1. Annual screening rates among women aged 40—75 years increased modestly from 2010 34. A total of 1,521,667 women mean age 50. The age of the women included in the study was distributed as follows: Of these women, 233,543 15.

The patient population for the model analysis comprised 84,549 women aged 40—75 years in a one million member health plan who undergo mammography screening each year Table 1. With the base-case assumption that the FFDM rate of follow-up services is 15.

Cost estimates are also shown in Table 3 on a per patient basis ie, total costs divided by the number of women screened. Table 3 DBT value analysis results Abbreviations: Similar dynamics can be observed for the other metrics shown in Table 3.

  • Materials and methods Economic model overview An economic model was developed to estimate the system-wide financial impact of DBT as a breast cancer screening modality within a hypothetical US managed care plan with one million members;
  • First, we point out that the way we defined women recalled for additional follow-up imaging and diagnostic testing services closely approximates the BI-RADS definitions of recall, but there may have been a small number of women who were in fact recalled but went directly to magnetic resonance imaging, biopsy, or fine needle aspiration without diagnostic imaging;
  • Table 3 DBT value analysis results Abbreviations;
  • But the benefits of mammography outweigh any possible harm from the radiation exposure.

Discussion The results of this study demonstrate clinical and economic favorability for DBT in breast cancer screening among commercially-insured US women. These results are supported by the strength of the underlying clinical publications and administrative claims database utilized, which contains health care information on over 30 million patients per year.

As such, the parameters of our model and the analysis results generated by them are robust, and these results should be generalizable to commercially-insured women in the US health system. Mammography practices differ from institution to institution, and subjective decisions about recalling women for follow-up imaging and diagnostic services after their initial mammogram vary widely, even among individual radiologists and diagnosticians working within particular institutions.

Furthermore, we did not account for costs associated with patients themselves, such as direct costs of copayments, coinsurance, and deductibles, and the indirect costs of transportation to and from medical appointments, work absence and lost productivity, and childcare coverage.

  1. Several investigations have shown that DBT has potential in both screening and diagnostic settings.
  2. Several investigations have shown that DBT has potential in both screening and diagnostic settings.
  3. As such, the parameters of our model and the analysis results generated by them are robust, and these results should be generalizable to commercially-insured women in the US health system.
  4. The 3-dimensional breast tomosynthesis images obtained at the same time as the 2-dimensional images and under the same compression clearly demonstrated a mass with spiculated margins allowing very accurate localization in the UOQ and size determination. Mean age was 49.
  5. A 1-year prospective longitudinal study was conducted in the Department of Radio-diagnosis in our institute using Hologic Selenia Dimensions for mammography as well as tomosynthesis.

A 2012 conference presentation by Kalra et al 28 reported cost-effectiveness analyses of mammographic screening using DBT based on direct radiology costs resulting from differences in the recall rate observed at one institution over a 12-month period.

The difference between our analysis and the previous work of Kalra et al is likely due to the respective study designs and data. Specifically, the previous study by Kalra et al used 2011 Medicare reimbursement rates to estimate costs for all patients including non-Medicare patientsexcluded some diagnostic costs outside of radiology eg, open biopsyand did not account for cost savings due to earlier cancer detection. A follow-on study by Kalra et al 29 confirmed findings from their first study, with DBT screening decreasing the overall costs of unnecessary diagnostic workups by 17.

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The cost of follow-up services due to abnormal screening results is a core component when analyzing the added value of DBT. The analysis time frame, data source, and cost estimation techniques ie, Medicare fee schedule versus adjudicated commercial claims likely all contribute to the differences in follow-up services costs observed by Chubak et al and our own estimates presented here. Some evidence exists that tomosynthesis may not only affect the rate at which women are called back for additional services, but that it also affects the utilization rate of specific additional services.

For example, Philpotts et al 51 report that DBT expedites patient diagnostic workup and results in better patient throughput and resource utilization. As more is learned about the potential for DBT to streamline diagnostic pathways, it is possible that additional cost savings not included in our current analysis will be identified.

This analysis is subject to several limitations. First, we point out that the way we defined women recalled for additional follow-up imaging and diagnostic testing services closely approximates the BI-RADS definitions of recall, but there may have been a small number of women who were in fact recalled but went directly to magnetic resonance imaging, biopsy, or fine needle aspiration without diagnostic imaging.

Also, in somewhat rare circumstances, women undergoing screening ultrasound following a screening mammogram due to dense breast tissue may have been misclassified as recalls, which could have artificially inflated the rate we report for women utilizing follow-up services. Another limitation of this study is that the data source used for the analysis includes women with commercial health insurance and may not be representative of women with other forms of health insurance eg, Medicaid, full Medicare or the uninsured.

Additionally, the evidence demonstrating the clinical benefits of DBT, which serves as a foundation of this model, is based on studies performed on the only DBT system which was commercially available at the time of this analysis Selenia Dimensions breast tomosynthesis system.

Thus, this analysis may not be applicable to other systems for which limited clinical evidence currently exists. Finally, we note that our estimate of the average annual mammography screening rate of 35. Moreover, the ACS statistic pertains to women aged 40 years and older who had any mammogram in the past year whether screening or diagnosticwhereas the rate we report specifically pinpoints screening mammography. Conclusion Use of DBT as a mammography screening modality substantially reduces the need for follow-up diagnostic services and improves detection of invasive cancers, allowing for digital mammography and tomosynthesis for breast cancer diagnosis, less costly treatment.

Results from our value analysis of DBT demonstrate that these beneficial attributes could translate into meaningful cost savings for US commercial health insurers. Investigation of longer-term outcomes is also warranted to better understand digital mammography and tomosynthesis for breast cancer diagnosis broader clinical and economic implications of adoption of DBT. Acknowledgment The authors appreciate the valuable contributions by Greg Lenhart and Jim Nelson toward development of the economic model reported in this paper.

Disclosure Funding for this study was provided by Hologic, Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the paper apart from those disclosed.