“Radiotherapy for older women (ROW)”: A risk calculator for women with early-stage breast cancer

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Abstract

Objective

Among older adult women with early-stage breast cancer who undergo lumpectomy, the benefits of radiotherapy vary according to tumor characteristics and life expectancy. We aimed to develop a risk calculator to predict individualized probability of long-term survival and local recurrence, accounting for these factors.

Methods

We developed a simulation model to estimate an individual patient's risk of local recurrence and all-cause mortality according to age, comorbidities, functional status, tumor characteristics, and radiotherapy status. We integrated two existing prediction models, the Early Breast Cancer Trialist's Collaborative Group prediction model for breast cancer specific outcomes and ePrognosis for life expectancy. An online risk calculator “Radiotherapy for Older Women (ROW)” was developed through an iterative multi-stage process, that included individual consultation and group meetings with an advisory committee (AC) comprised of patients, advocates, clinicians, and researchers.

Results

We developed the tool over 40 months and had 15 group meetings. The risk calculator developed as a simulation model with 16 factors (5 tumor-related, 3 demographic, 4 comorbidities, and 4 functional statuses). Across 56,700 simulated scenarios, the benefit of RT in terms of absolute 10-year local recurrence reduction, ranged from 0% to 34%, depending on individual characteristics. Based on feedback from the AC, overall survival and local recurrence were chosen as the output for ROW, with these outcomes displayed numerically (percentages and natural frequencies) and graphically (pictographs).

Conclusions

This tool “ROW” could facilitate shared decision making regarding receipt of radiotherapy for older women with early breast cancer. Additional studies to examine usability testing are underway.

Introduction

For older adult women with early-stage, estrogen receptor (ER) positive breast cancer who undergo breast-conserving surgery (BCS), two randomized controlled trials have demonstrated that adjuvant whole breast radiation therapy (WBRT, hereafter as RT) reduces local recurrence but does not improve overall survival [1,2]. Based on these data, consensus guidelines recommend that omission of RT can safely be considered for women aged 70 or older with stage I disease who receive endocrine therapy (ET) [3]. However, more than two-thirds of these women undergo RT [4,5]. While RT does reduce local recurrence, it also requires travel to receive daily radiation for several weeks and poses risks of side effects, such as fatigue, breast pain, and pneumonitis [[6], [7], [8], [9], [10]]. Therefore, it is critical that women are well-informed when considering the receipt of RT.

Risk of local recurrence can vary significantly. Tumor size, tumor grade, margin width, ER status, and number of positive axillary nodes, for example, have all been associated with local recurrence [[11], [12], [13]]. Several risk calculators or nomograms have been developed to help RT decision-making by predicting outcomes with and without adjuvant RT [12]. However, these programs do not focus on older women, and do not incorporate competing mortality. It is well known that the benefits of an intervention decrease as life expectancy decreases [14,15]. Older patients are more likely to have multiple comorbidities or impaired functional status, and to die of non-breast-cancer-related causes, and therefore might be less likely to benefit from RT. To our knowledge, there is no RT risk calculator that incorporates life expectancy into its prediction model, despite the substantive relationship between life expectancy and recurrence in this population.

Accordingly, we sought to develop an interactive, individualized, automated risk calculator “Radiotherapy for Older Women (ROW).” Our objective was to promote patient-centered treatment decisions by providing personalized risk estimates for older women with early-stage breast cancer. We proposed a novel model that integrates two published predictive models, ePrognosis (a survival prediction tool frequently used in geriatric assessment) [16,17] and the Early Breast Cancer Trialist's Collaborative Group (EBCTCG) prediction models [13]. We also convened an advisory committee (AC) to help develop ROW to be patient-centered and user-friendly. Through collaborative discussions, we presented individualized outcome estimates in an electronic format to facilitate provider-patient communication.

Section snippets

Study Design

The Human Investigation Committee of the authors' institution approved this study. A synopsis of the study design is shown in Fig. 1. In brief, this was a two-part study: (1) development of simulation models to estimate personalized outcomes, with and without RT; and (2) development of a risk calculator for older adult women deciding whether or not to undergo RT for early-stage breast cancer. As stakeholder engagement is a crucial component to the success of decision support development,

Simulation Results

Among 56,700 simulations, the mean 10-year local recurrence was 22.2% (range: 1–73%) for patients not undergoing RT, compared to 8.6% (range: 0–41%) for patients undergoing RT. The average absolute benefit of RT in 10-year local recurrence reduction across all scenarios was 13.5%, ranging from 0% to 34%. The mean 10-year all-cause mortality was 53.4% (range: 5–97%) for those not undergoing RT and 52.3% (range: 5–97%) if undergoing RT. The absolute mortality reduction attributable to the receipt

Discussion

Approximately half of all breast cancers in the United States occur in women over the age of 65 [27]. For those with early disease, understanding the risks and benefits of RT is critical for treatment decision-making. There is, however, no risk calculator specific for the older adult population. As a majority of older adult women undergo RT despite a lack of survival benefit [28], developing a patient-centered risk calculator to provide individualized risk estimates could help facilitate

Funding

This study was supported by grant 1K01HS023900-01 from the Agency for Healthcare Research and Quality (Dr. Wang), and by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, part of the National Institutes of Health, under Award Number AR060231-05 (Dr. Fraenkel). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Financial Disclosures

Drs. Wang and Mougalian receive research support from Genentech. Dr. Killelea receives consulting fees from Genentech. Dr. Mougalian receives consulting fees from Eisai and Celgene Drs. Gross and Mougalian are on a grant sponsored by National Comprehensive Cancer Network/Pfizer. Dr. Gross receives research support from Johnson & Johnson, and support for travel from Flatiron, Inc. These sources of support were not used for any portion of the current manuscript. None of the other coauthors have

Author Contribution

Conception and design: Shi-Yi Wang.

Administrative support: Shi-Yi Wang.

Website development: Shi-Yi Wang, Fuad Abujarad.

Simulation modeling and data analysis: Shi-Yi Wang, Tiange Chen.

Data analysis and interpretation: All authors.

Manuscript writing: All authors.

Final approval of manuscript: All authors.

Accountable for all aspects of the work: All authors.

Declaration of Competing Interest

Drs. Wang and Mougalian receive research support from Genentech. Dr. Killelea receives consulting fees from Genentech. Dr. Mougalian receives consulting fees from Eisai and Celgene. Drs. Gross and Mougalian are on a grant sponsored by National Comprehensive Cancer Network/Pfizer. Dr. Gross receives research support from Johnson & Johnson, and support for travel from Flatiron, Inc. These sources of support were not used for any portion of the current manuscript. None of the other coauthors have

Acknowledgement

We appreciate Jazbel Wang for her assistance in website development.

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