1
Predicting the Impact of Treatment Options on Survival and Breast Conservation in Patients With Ductal Carcinoma In Situ (DCIS)
Rinaa Punglia, MD1, Natasha Stout, PhD2, Angel Cronin, MS1, Hajime Uno, PhD1, Elissa Ozanne, PhD3, Michael Hassett, MD, MPH1, Elizabeth Frank, MA1, Deborah Schrag, MD, MPH1, Caprice Greenberg, MD, MPH4, Djora Soeteman, PhD2
1Dana Farber Cancer Institute, Boston, MA 2Harvard Medical School, Boston, MA 3Dartmouth College Geisel School of Medicine, Hanover, NH 4University of Wisconsin Madison, Madison WI
Original title: Impact of Radiation Therapy on Breast Conservation in DCIS PCORI ID: CE-12-11-4173 HSRProject ID: HSRP20143205 Clinical Trials.gov ID: NCT02248662
_______________________________ To cite this document, please use: Punglia R, et al. (2019). Predicting the Impact of Treatment Options on Survival and Breast Conservation in Patients With Ductal Carcinoma In Situ (DCIS). Washington, DC: Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/1.2020.CE.12114173
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Table of Contents
ABSTRACT ............................................................................................................................ 3 BACKGROUND ..................................................................................................................... 5
Aim 1 .......................................................................................................................................... 5 Aim 2 .......................................................................................................................................... 6 Aim 3 .......................................................................................................................................... 7
METHODS ............................................................................................................................ 8 Aim 1 .......................................................................................................................................... 8
Cohort Selection .................................................................................................................................. 8 Stakeholder Engagement .................................................................................................................... 9 Analysis .............................................................................................................................................. 10
Aim 2 ........................................................................................................................................ 13 Study Cohort and Databases ............................................................................................................. 13 Type of Surgery for Second Breast Event .......................................................................................... 16 Regional Treatment Intensity ............................................................................................................ 16 Stakeholder Engagement .................................................................................................................. 19
Aim 3 ........................................................................................................................................ 19 Model Assumptions ........................................................................................................................... 19 Recurrence Over Time ........................................................................................................................ 27 Dependent Events ............................................................................................................................. 27 Stakeholder Engagement .................................................................................................................. 32
RESULTS ............................................................................................................................. 33 Aim 1 ........................................................................................................................................ 33 Aim 2 ........................................................................................................................................ 38 Aim 3 ........................................................................................................................................ 48
Analysis and Model Outcomes .......................................................................................................... 48 Model Validation ............................................................................................................................... 48 Creating Lookup Tables for the DCIS Decision Tool .......................................................................... 49 Translating Output Data into Spreadsheets ...................................................................................... 52
DISCUSSION ....................................................................................................................... 53 Aim 1 ........................................................................................................................................ 53
Aim 1 Limitations ............................................................................................................................... 53 Aim 2 ........................................................................................................................................ 54
Aim 2 Limitations ............................................................................................................................... 55 Aim 3 ........................................................................................................................................ 56
Aim 3 Limitations ............................................................................................................................... 57 CONCLUSIONS .................................................................................................................... 58 REFERENCES ....................................................................................................................... 60 NOTE ON PUBLISHED MATERIAL ......................................................................................... 64
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APPENDIX .......................................................................................................................... 65
ABSTRACT
Background
Currently, more than 70% of women with ductal carcinoma in situ (DCIS) receive breast-conserving surgery but then are at risk of a second cancer diagnosis in the same breast. Radiation therapy (RT) after breast-conserving surgery decreases recurrence in the 10 years after diagnosis by one-half but does not improve survival. Women with DCIS are also at elevated risk for cancer in the contralateral breast. Radiation after breast-conserving surgery for DCIS limits therapy choice to mastectomy if a woman has a second cancer in the treated breast because radiation can be given only once due to limits of normal tissue tolerance. If radiation was not received initially, a patient may be able to avoid mastectomy after a second ipsilateral breast cancer. For these reasons, the choice of treatment for DCIS is complex. A web-based decision aid would help a patient quantify the tradeoffs between her long-term survival and breast preservation.
Objectives
1. Determine the risk of and risk factors for new breast cancer after DCIS. 2. Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the association of regional use of RT on this likelihood.
3. Determine the tradeoffs associated with RT for DCIS in terms of breast conservation for an individual patient in a web-based decision aid.
Methods
1. To examine predictors of contralateral breast cancer following DCIS, we identified women diagnosed with DCIS in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. We used multivariable Cox proportional hazards models to examine risks and predictors of contralateral second breast cancer.
2. We performed a retrospective analysis of population-based databases SEER and SEER-Medicare. We also measured mastectomy versus breast-conserving surgery (BCS) at a second breast event (DCIS recurrence or new invasive cancer).
3. We developed a discrete event simulation model integrating data from the published literature to simulate the clinical events after 6 treatments for women with newly diagnosed DCIS.
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Results
1. In multivariable analysis, age and year of diagnosis, race, size, and estrogen receptor (ER) status were all significant predictors of contralateral breast cancer.
2. Residence in a health service area (HSA) with greater radiotherapy use for DCIS was associated with an increased likelihood of receiving mastectomy versus BCS at a subsequent breast event, even among women who had not previously received radiotherapy for DCIS.
3. One million women of a given age at diagnosis were simulated for each treatment strategy. The model outcomes were disease-free survival, invasive disease-free survival, overall survival, and likelihood of breast preservation over a 10-year and lifetime horizon. The simulation process was automated to create the model output tables for the decision tool.
Conclusions
1. We demonstrate that DCIS that expresses the estrogen receptor is associated with a statistically increased risk of having a contralateral breast cancer diagnosis.
2. Geographic areas with more radiotherapy use for DCIS had more use of mastectomy at the time of a second breast event even among patients eligible for breast conservation.
3. This work culminates in a decision aid that will enable patients and their physicians to choose the treatment most consonant with the patient’s history, characteristics, and preferences; it has the potential to improve both quality of life and decision making for patients diagnosed with DCIS.
Limitations
1. There may be underascertainment of contralateral breast cancer diagnosis in SEER. 2. The SEER database does not capture radiation use and second breast events. 3. Although the decision aid aims to help patients and their physicians choose a treatment
path based on potential outcomes, it does not consider every possible outcome patients can experience.
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BACKGROUND
Aim 1
Determine the risk of and risk factors for new contralateral breast cancer after DCIS.
An expected 1 million women will be living with a diagnosis of ductal carcinoma in situ
(DCIS) in 2016,1 a number likely to grow given current screening and survival rates. Despite the
large number of women affected, the optimal treatment for DCIS remains uncertain. This is
likely due to both the uncertainty about the natural history of DCIS, as well as a lack of data
regarding the effectiveness of various approaches to treatment. Currently, 6 standard
treatments for DCIS exist: lumpectomy alone, lumpectomy with radiation, lumpectomy with
radiation and tamoxifen, lumpectomy with tamoxifen, and mastectomy with and without
breast reconstruction While the primary focus of treatment has been to minimize the risk of
DCIS progression to invasive breast cancer in the same breast, women with DCIS are also at
elevated risk for a new breast cancer in the opposite, or contralateral, breast. Incidence of new
contralateral invasive breast cancer is estimated to be 4.5 out of 1000 person-years.2 This risk is
3 to 4 times that of women without a history of breast cancer and similar to that of women
with a diagnosis of invasive breast cancer.3
With improvements in local therapy after breast-conserving surgery (BCS; a procedure
that enables patients to retain their breast), the risk of contralateral breast cancer for women
with DCIS may exceed that of the ipsilateral breast.4 Using the ipsilateral breast to estimate risk
of new breast cancer diagnosis in the decade following DCIS diagnosis is confounded by the
effect of RT in reducing local recurrence among those who receive it. It also requires a
separation from the risk of recurrence of initial DCIS, which is often not possible. Instead, the
contralateral breast is an ideal site to study risk of new cancer, as it is unirradiated and does not
carry the risk of recurrence conferred by the initial DCIS diagnosis. A focus on contralateral
breast cancer is especially important given the option of anti-estrogen treatment for DCIS.
Tamoxifen, a selective estrogen receptor (ER) modulator, decreases the risk of new breast
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cancers in both the ipsilateral and contralateral breasts, and may also directly affect the risk of
recurrence after DCIS.5-10 We therefore used the contralateral breast as a proxy for new cancer
risk in the ipsilateral breast after 10 years.11 We sought to determine the incidence of and risk
factors for developing contralateral breast cancer after DCIS, especially as these patients may
derive the greatest benefit from chemoprevention with anti-estrogen therapy. Examining risk
factors for new breast cancer after DCIS could help frame how decisions are made related to
treatment (Stout NK, Cronin AM, Uno H, et al, unpublished data).
Aim 2
Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the effect of regional use of radiation therapy on
this likelihood.
Patients and their physicians are often confronted with a decision between more
intensive versus less intensive treatment for a particular diagnosis. Quality decision making
between these options requires careful balancing of the risks and side effects, as well as
weighing the expected outcomes and their associated value as assessed by the patient.
Although the incidence of DCIS has risen dramatically,12 there exists considerable debate
about optimal treatment. In general, people with DCIS have high rates (approximately 96% for 5
years) of recurrence-free survival.13 Intensive therapies for DCIS such as mastectomy (removal
of the breast) or RT following BCS reduce the likelihood of a second ipsilateral breast cancer
diagnosis,6,14-16 but have not been shown to improve survival on meta-analysis.17 In addition,
radiation usually necessitates mastectomy should a new cancer or DCIS develop in the same
breast at any point during the patient’s lifetime. This is because there are limits to normal
tissue radiation tolerance, and 2 courses of RT are generally not recommended. Previous
radiation can also complicate reconstructive options following mastectomy. The tradeoff
between risk of second breast diagnosis, and side effects and potential consequences of RT,
underscores the need for patient preference–driven decision making.
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Patients who receive BCS alone without RT may be candidates for repeat BCS if they
have a second breast event in the same breast. One study suggests that some women choose
not to have radiation after DCIS because they want to have a breast preservation option should
a second breast diagnosis occur.18 However, the likelihood of mastectomy versus BCS at time of
new diagnosis in a previously unirradiated breast is variable.19-21 Whether a woman receives
repeat BCS for a new diagnosis may not only be a function of the stage of diagnosis, but may
also be determined by the treatment patterns in geographical regions used for management of
DCIS. We sought to study whether the regional frequency of radiation use for initial DCIS is
associated with mastectomy at the time of a second breast event among women who have not
received RT at initial DCIS diagnosis.22 By studying the spillover effect of RT on mastectomy
likelihood by region, we will identify another component of the consequences of provider
biases on health outcomes. The goal of identifying such biases would be to serve as an impetus
for change to more patient-directed decision making.
Aim 3
Integrate the findings of aims 1 and 2, to determine the tradeoffs associated with
radiation therapy for DCIS in terms of breast conservation for an individual patient in a web-
based decision aid.
Despite the large number of women affected by DCIS, the optimal treatment regimen is
uncertain, which adds challenges to the decision-making process between women and their
physicians.23 A randomized trial comparing all approaches to DCIS treatment is not feasible, so
the tradeoffs between strategies have not been fully assessed.23 Disease simulation models
provide a framework that synthesizes data from randomized trials and retrospective studies.23
These models then evaluate the relative performance of the interventions under study.
We developed a discrete event simulation (DES) model integrating data from published
literature to simulate the clinical events after 6 treatments (lumpectomy alone, lumpectomy
with radiation, lumpectomy with radiation and tamoxifen, lumpectomy with tamoxifen, and
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mastectomy with and without breast reconstruction) for women with newly diagnosed DCIS.23
The objective was to quantify the tradeoff between long-term survival and breast preservation
for 6 DCIS treatments that are considered current standard practice. This DES model will
ultimately form a web-based decision aid (known as the DCIS Decision Tool). The DCIS Decision
Tool will serve as a resource for women who are trying to make treatment choices for their
DCIS.
METHODS
Aim 1
Determine the risk of and risk factors for new contralateral breast cancer after DCIS.
For this retrospective cohort study, we drew data from the population-based cancer
registries participating in the National Cancer Institute’s Surveillance, Epidemiology, and End
Results (SEER) program. This program provides access to information from 17 affiliated cancer
registries, which include approximately 28% of the US population.24 In addition to recording
incident cancer cases, SEER provides detailed clinical information about cancer site, stage, and
histology, as well as subsequent cancer diagnoses. The strengths of SEER data include size and
generalizability.
Cohort Selection
To examine the incidence and predictors of contralateral breast cancer following DCIS,
we identified women aged 40-79 who had a diagnosis of DCIS between January 1, 1990, and
December 31, 2014, recorded in SEER. We defined a DCIS diagnosis using the following
International Classification of Diseases for Oncology codes: 8050, 8201, 8210, 8230, 8401, 8500,
8501, 8503, 8504, 8507, 8522, 8523, 8540, and 8543. We limited the cohort for our primary
analysis to diagnoses prior to December 31, 2002, when tamoxifen was not used routinely for
DCIS. The initial presentation describing the differential effects of tamoxifen by ER status was
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delivered in December 2002. In a supplementary analysis, we also examined the tamoxifen-era
cohort of women diagnosed between January 1, 2003, and December 31, 2013. All women
were followed until 1 of 4 competing events occurred: (1) diagnosis of a second breast cancer
(invasive or in situ) with no intervening cancers from another site; (2) death from breast cancer
or non–breast cancer causes; (3) loss to follow-up; and (4) end of study follow-up on December
31, 2014, the latest available date. Second breast cancers were categorized as contralateral if
the primary DCIS and second breast cancer were noted on opposite breasts, and ipsilateral if
the same breast.
We excluded women with DCIS who had (1) a prior cancer history before DCIS, (2) a
bilateral DCIS at the time of diagnosis, (3) an invasive breast cancer within 6 months of DCIS, or
(4) a contralateral breast cancer diagnosis within 6 months. We also excluded women if the
laterality of her primary DCIS or second breast cancer was unknown or follow-up time was
unknown. We excluded women with prior cancer history because this could affect treatment
and treatment choices or options. We excluded women with bilateral DCIS at time of diagnosis,
invasive breast cancer within 6 months of DCIS, or a contralateral breast cancer diagnosis within
6 months because these patients could be considered to have cancer diagnosed at the same
time (see Aim 1, Table 1 for details on cohort characteristics).
Stakeholder Engagement
Four patient stakeholders participated throughout the project. All 4 are women aged 45
and older; 3 are white and 1 is African American. All have extensive experience in the health
care field as patient advocates and as breast cancer survivors. Stakeholder occupations range
from nursing to counseling breast cancer patients throughout their care journeys.
These 4 stakeholders were kept up-to-date about the databases being explored for this
study and about the information found in the datasets. On one occasion, for example, we
discussed the underascertainment of second cancers discovered in the SEER dataset and how
our team would adjust for that. We communicated the analyses being performed on the SEER
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datasets, and the stakeholders were given opportunities to provide suggestions throughout the
data analysis process. Stakeholders also reviewed initial drafts of the manuscript.
Additionally, we discussed all possible outcomes that could be explored in this aim, and
our stakeholders provided feedback to better define these outcomes. This occurred via email,
as well as through quarterly team conference calls. We provided our stakeholders with
information about the completion of the analysis and encouraged them to be in communication
with the research team if any new thoughts or feedback arose. Stakeholders also offered their
edits and suggestions for our manuscripts. A key patient advocate is a coauthor on our
manuscript.
Analysis
We used multivariable competing risk regression to examine predictors of time from
index DCIS to contralateral breast cancer (invasive or in situ).25 A competing risk regression is
useful when multiple outcomes may occur, such as ipsilateral recurrence, contralateral breast
cancer diagnosis, or death. We created a composite competing event of the earliest date of
second ipsilateral breast cancer (invasive or in situ) with no intervening cancers and death.
Women with no events were censored at either the date at which they were lost to follow-up
or end of study (December 31, 2014)—whichever came first. Explanatory variables included age
and race of the woman at the time of primary DCIS, year of diagnosis, SEER registry, size, grade,
ER status, and initial treatment. Additionally, we adjusted for socioeconomic factors measured
by education and household income based on county-level attributes. Variable definitions are
in Aim 1, Table 2. We performed all statistical analyses using Stata SE for Windows (College
Station, TX). (See also Stout NK, Cronin AM, Uno H, et al, unpublished data.)
11
Aim 1, Table 1. Competing Risks Regression Results for Predictors of Contralateral Breast Eventsa
a Among women diagnosed with DCIS between 1/1/1990 and 12/31/2002 via Fine and Gray model (n = 46,007).
12
Aim 1, Table 2. Variable Mapping
13
Aim 2
Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the effect of regional use of radiation therapy on
this likelihood.
Study Cohort and Databases
We used 2 population-based databases: SEER and SEER-Medicare. Data from the SEER-
Medicare database included patients who are younger than 65 years of age at the time of initial
diagnosis of DCIS if their recurrence or new diagnosis occurs after they become Medicare-
eligible. From the SEER database, we identified 33 194 patients with DCIS between 1990 and
2011 treated with BCS without radiation (Aim 2, Figure 1). From the SEER-Medicare database,
we identified 5320 patients using SEER-Medicare diagnoses from 1990 to 2009 linked to
Medicare claims through 2010 (Aim 2, Figure 2).
14
Aim 2, Figure 1. Radiotherapy (RT) use after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS)
15
Aim 2, Figure 2. Flow diagram for SEER analysis
16
We used 2 datasets because each had different limitations. The consistency of our
findings across both datasets helps ensure our results are robust. We also conducted a separate
analysis in the linked SEER-Medicare dataset to explore how the findings were affected by (1)
using Medicare claims data to define treatment; (2) including additional predictor variables
(distance to closest radiation facility, prediagnosis comorbidity, and chemotherapy for
secondary breast event) not available in SEER; and (3) including secondary breast cancer
diagnoses suggested by Medicare claims (ie, claims for BCS or mastectomy > 6 months after
initial diagnosis), but without an associated second SEER diagnosis. We excluded patients with
unknown laterality (< 0.1%) in SEER (to facilitate analyses according to laterality of secondary
breast diagnosis), or a second breast cancer diagnosis within 6 months of initial diagnosis in
both datasets.
Type of Surgery for Second Breast Event
We studied receipt of mastectomy (versus BCS with or without radiation) for a second
breast diagnosis (stage 0-III breast cancer) among patients receiving BCS alone for primary DCIS.
We used multivariable logistic regression modeling with all variables of interest regardless of
statistical significance as univariate predictors. These variables include age at secondary
diagnosis, race, ethnicity, median income, high school education, residence type, secondary
SEER diagnosis, stage of secondary SEER breast cancer, ER status of secondary SEER breast
cancer, laterality of secondary SEER diagnosis, year of secondary diagnosis, interval between
diagnoses, treatment intensity for primary DCIS, Charlson comorbidity score, distance to
nearest radiation facility, chemotherapy for secondary breast event, and magnetic resonance
imaging (MRI) in 6 months before secondary breast event.
Regional Treatment Intensity
We assigned health service areas (HSAs) to 1 of the 3 clusters based on the observed
proportion of radiation use for DCIS as coded by SEER or determined by claims in SEER-
Medicare (Aim 2, Figure 3). Because a proportion is challenging to analyze statistically, we used
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hierarchical modeling to categorize the health service areas into 3 categories, using a latent
variable to determine which HSA belongs to each of the 3 categories. The cutoffs separating the
groups were based on the hierarchical model, taking the precision of the estimated proportion
of patients receiving radiation into account. We assigned HSAs with the highest proportions of
patients receiving radiation for DCIS to the “high” cluster; those with the lowest proportions to
the “low” cluster; and those in between to the “middle” cluster. We did not assign HSAs with
fewer than 20 patients diagnosed over the study period.22
18
Aim 2, Figure 3. Flow diagram for SEER-Medicare cohort
19
Stakeholder Engagement
Through email, we kept our patient stakeholders informed of the cohort being used for
this aim and of the preliminary analyses conducted with the SEER-Medicare dataset. We also
provided the preliminary results during our quarterly teleconference, during which we received
questions about creating educational materials for the decision tool. We consulted with our
stakeholders about the manuscript for this to get their feedback. Our team also asked our
stakeholders to provide feedback on a presentation titled “Understanding Ductal Carcinoma In-
Situ,” and they replied with edits via email. They suggested changes such as wording changes to
distinguish DCIS from invasive cancer, including legends on the graphs, and minor spelling
issues.
Aim 3
Integrate the findings of aims 1 and 2, to determine the tradeoffs associated with
radiation therapy for DCIS in terms of breast conservation for an individual patient in a web-
based decision aid.
Model Assumptions
The key model assumptions were the following:
• DCIS has no direct risk of breast cancer mortality:
o A risk of dying from breast cancer exists only with an invasive recurrence or new
invasive primary breast cancer.26,27
• The risk of DCIS recurrence following mastectomy is 0, but there is a small risk of
invasive recurrence (ie, a 1% recurrence risk of stage III or IV invasive disease over a 10-
year period).
• With recurrence, treatment options are a function of the initial treatment:
o If initial treatment does not include radiation, assume 33% will receive
mastectomy while 67% will receive lumpectomy with radiation.20
20
o If initial treatment includes radiation, all women must have mastectomy.
We made the first assumption because some patients may not be candidates for repeat
lumpectomy due to relative breast size and extent of disease. We followed those proportions to
estimate patients who would be eligible for repeat lumpectomy or mastectomy.
User-Selected Model Inputs
The patient-specific input variables of the model were age, which could be varied by 1-
year increments (range 40-80 years); DCIS recurrence risk; and risk of ipsilateral invasive breast
cancer, which could be varied by 1% increments (risk ranges from 0% to 40%). For example, the
value 5 represents a 5% risk of recurrence at 10 years.
DCIS Disease Model
We constructed the disease simulation model in TreeAge Pro Version 2016
(Williamstown, MA: TreeAge Software Inc). We compared expected long-term survival and
breast preservation outcomes for 6 management strategies for DCIS: lumpectomy alone,
lumpectomy with radiation, lumpectomy with radiation and tamoxifen, lumpectomy with
tamoxifen, and mastectomy with and without breast reconstruction (Aim 3, Figure 1). The
structure of the model for the lumpectomy treatment arm is shown in Aim 3, Figure 2. To
evaluate the performance of specific health interventions, disease simulation models can
provide a framework that synthesizes data from existing sources (eg, randomized trials,
retrospective studies).23
21
Aim 3, Figure 1. Comparing 6 different treatment strategies
22
Aim 3, Figure 2. Competing events that can occur
23
All women entered the model in a disease-free state after initial treatment and were at
risk of (1) a new primary DCIS cancer diagnosis in the contralateral breast (first branch), (2) a
new primary invasive cancer diagnosis in the contralateral breast (second branch), (3) a DCIS
recurrence in the ipsilateral breast (third branch), (4) an invasive recurrence in the ipsilateral
breast (fifth branch), (5) death from breast cancer (seventh branch), and (6) death from non–
breast cancer causes (ninth branch) (Aim 3, Figure 3). The simulation ended when a woman
died, or her age equaled or exceeded 100 years (ninth branch; end of time horizon). If a woman
had undergone mastectomy in the ipsilateral or contralateral breast as initial or secondary
treatment, the woman was no longer at risk for a DCIS recurrence in that particular breast but
was still at risk for an invasive recurrence (fifth and sixth branch).
Aim 3, Figure 3. Selecting the next treatment after a DCIS recurrence in the ipsilateral or new DCIS diagnosis in the contralateral breast
24
To model the disease course for a simulated woman, we established the temporal order
and timing of the discrete events listed in the preceding paragraph. We randomly sampled
times for each possible event, choosing the event with the earliest time to occur next and
discarding the other times. At the time of a new event, the time elapsed in the preceding state
was noted, and the age of the woman was updated as needed. We repeated this process after
the occurrence of each new nonfatal event. With this approach, the sequence of events
experienced by the women was randomly generated from the distributions of time assigned in
the model. In order to calculate the time-to-event for each possible event, we have entered
time expressions under each branch in Aim 3, Figure 3. The data we have used to define these
time distributions are described below. Model input parameters are also detailed in Aim 3,
Table 1.
Aim 3, Table 1. Model Assumptions and Input Parameters
Variable Value at 10 Years
Value at 10 Years
Type Source
Risk of recurrence DCIS Invasive
Lumpectomya 9
Ipsilateral 0.14 0.16
Contralateral 0.02 0.06
Lumpectomy with radiationa
Ipsilateral 0.08 0.06
Contralateral 0.03 0.05
Lumpectomy with radiation and tamoxifena
Ipsilateral 0.06 0.05
Contralateral 0.01 0.03
Lumpectomy with tamoxifen 7
Ipsilateral 0.35 0.44 HR
25
Variable Value at 10 Years
Value at 10 Years
Type Source
Contralateral 0.66 1.17 HR
Mastectomy with or without reconstruction
Ipsilateral – 0.01 26,27
Stage distribution of invasive recurrence
24
Stage I 0.61
Stage II 0.27
Stage III 0.07
Stage IV 0.05
Stage distribution of invasive new breast cancer
24
Stage I 0.63
Stage II 0.29
Stage III 0.05
Stage IV 0.03
Probability of mastectomy after recurrence
0.33 20
Probability of reconstruction after mastectomy 28
Age, y
25-34 1
35-44 0.71
45-54 0.58
55-64 0.44
65-74 0.22
75+ 0.07
Mortality
26
Variable Value at 10 Years
Value at 10 Years
Type Source
Breast cancer specific by stage
29
Stage I 0.08
Stage II 0.27
Stage III 0.52
Stage IV 0.88
Non–breast cancer causes US life table 30
Sensitivity analyses
Risk of recurrence 31
Low 0.49 0.31 Adjustment factors
compared with base case
Intermediate 1.11 0.54
High 0.58 1.16
Age-specific recurrence rates Lumpectomy alone (LO)
LO + RT 17
<50 years: 5 years after diagnosis
1.1 1.59
Risk ratios compared with
base case
<50 years: 10 years after diagnosis
1.04 1.43
50+ years: 5 years 0.97 0.82
50+ years: 10 years 0.99 0.84
Abbreviation: HR, hazard ratio; RT, radiation therapy.
a In this table we report only the 10-year cumulative incidence of events. However, in the model we use time-to-event distributions based on data from the National Surgical Adjuvant Breast and Bowel Project B-17 and B-24 trials.9
27
Recurrence Over Time
The risks of recurrence and new primary cancer over time were specified for each of the
6 initial treatment strategies. We used data from the National Surgical Adjuvant Breast and
Bowel Project (NSABP) B-17 and NSABP B-24 trials to inform these parameters for lumpectomy
alone (LO), lumpectomy with radiation (LRT), and lumpectomy with radiation and tamoxifen
(LRT + TAM).9 Because the NSABP trials did not include an arm treated with breast-conserving
surgery with tamoxifen without radiation (LO + TAM), we inferred the effects of tamoxifen from
the UK, Australia, and New Zealand (UK/ANZ) DCIS trial.7 Therefore, we adjusted the LRT + TAM
data from the NSABP B-24 trial using the hazard ratios between the LRT + TAM treatment arm
relative to the LO + TAM arm from the UK/ANZ DCIS trial. Data on the cumulative incidence
were available over a 20-year time horizon for the LO and LRT treatments arms from the NSABP
B-17 and 15-year data for the LRT + TAM arm from the NSABP B-24 trial. We converted the
incidence curves into survival distributions that were then used in the model.
Breast Cancer and Non–Breast Cancer Mortality
We obtained stage-specific invasive breast cancer survival distributions from an
observational study of newly diagnosed patients treated in British Columbia, Canada.31 Each
patient was subject to risk of mortality from non–breast cancer causes based on the 1960 birth
cohort US life tables.30
Dependent Events After a DCIS recurrence in the ipsilateral (third branch in Aim 3, Figure 3) or new DCIS
diagnosis in the contralateral breast (first branch in Aim 3, Figure 3), a next treatment was
selected (Aim 3, Figure 4). If the initial treatment did not include radiation (eg, lumpectomy
alone, lumpectomy with tamoxifen), 33% of women received mastectomy and 67% received
lumpectomy with radiation.20 If the initial treatment did include radiation (eg, lumpectomy with
radiation, lumpectomy with radiation and tamoxifen), all women (100%) received mastectomy
as the next treatment. We used age-dependent probabilities of reconstruction after
mastectomy (Aim 3, Table 1).
28
Aim 3, Figure 4. Determining stage of invasive cancer and potential next treatment
After an invasive recurrence in the ipsilateral or new invasive diagnosis in the contralateral breast, the stage of cancer was
determined (Aim 3, Figure 5) using the stage distribution of women diagnosed with invasive cancer after DCIS between 1995 and
2005 in the SEER limited use database.24 If the cancer stage was I, II, or III, a next treatment was selected based on the same criteria
described above. In the disease-free state after this treatment they would be at risk again for the competing events in Aim 3, Figure
3. If the cancer stage was IV (ie, metastatic), the woman could either die from breast cancer or from other causes and would exit the
model at the earliest of those 2 events.
29
Aim 3, Figure 5. Determining the stage of invasive cancer after mastectomy
After a mastectomy (fifth or sixth branch in Aim 3, Figure 3) we assumed a small chance of an invasive recurrence (ie, a 1%
recurrence risk of stage III or IV invasive disease over a 10-year period).27,28 After determining the stage of the cancer (62.5% stage III
and 37.5% stage IV), women could either die from breast cancer or from other causes and would exit the model (Aim 3, Figure 6).
The descriptions of the expressions used in the model are displayed in Aim 3, Tables 2 and 3.
30
Aim 3, Figure 6. Model schematic depicting the events that may occur in the lumpectomy treatment arm
31
Aim 3, Table 2. Inputs of Decision Model
Aim 3, Table 3. Outputs of Decision Model
32
Stakeholder Engagement
Stakeholders guided the process of designing the web-based decision tool by sending
their feedback upon first seeing the website. They also went through the website on a quarterly
call with the research team. During the teleconference about the first iteration of the decision
tool, stakeholders made suggestions about how to reformat the website to make navigation
more patient friendly. Stakeholders also advised the team on rephrasing advanced medical
terminology, and on including graphics with numbers to facilitate a thorough understanding of
the treatment choice information.
Once the website was near completion, we conducted user experience testing.
Stakeholders navigated the website and used it from an example patient’s perspective. They
used a test case scenario and entered fictional patient information. They were encouraged to
thoroughly read each section of the website and note any issues. Patient stakeholders saw their
own results and gave feedback on these as well. After all stakeholders had completed their
review of the website in this manner, we compiled their comments into a master feedback
document, which allowed investigators to keep track of all changes made to the website.
33
RESULTS
Aim 1
Determine the risk of and risk factors for new contralateral breast cancer after DCIS.
For our primary analysis, we identified 53 693 women with DCIS during the study period
of 1990-2002, and our final cohort size after applying the exclusion criteria mentioned in the
aim 1 Methods section was 46 007 women (Aim 1, Table 3). The average age was 58 years and
most (82%) were white. Of the cases, 6541 (14%) had known ER status, with 75% ER-positive.
Median follow-up time was 14 years and 2 months. During follow-up, 3466 (7.5%) women had
contralateral breast cancer and 2934 (6.4%) had ipsilateral breast events (P < 0.0).
In our multivariable analysis of predictors of contralateral breast cancers among women
with DCIS diagnosed between 1990 and 2002, age, year of diagnosis, race, size, and ER status
were all significant predictors (Aim 1, Table 3). Black women were about 21% (95% CI, 1.08-
1.37) more likely to experience a contralateral breast event compared with white women.
Compared with ER-positive cases, ER-negative cases were about 40% (OR: 0.61; 95% CI, 0.48-
0.78) less likely to have a contralateral breast cancer. Cases with unknown ER status, which
comprised most DCIS diagnoses in the pre-tamoxifen era, had similar risks for the development
of contralateral breast cancer as ER-positive cases. Results did not change appreciably when we
examined invasive contralateral events alone or when restricted to only women with known ER
status (Aim 1, Table 4).
34
Aim 1, Table 3. Adjusted Hazard Ratios for Predictors of Contralateral Breast Eventsa
a Among women diagnosed with ductal carcinoma in situ (DCIS) between 1/1/1990 and 12/31/2002 from multivariable competing risks regression (n = 46,007).
35
Aim 1, Table 4. Sensitivity Analyses for Multivariable Competing Risk Regression Modeling Using Alternative Outcome Measures, Cohorts, or Later Years of Diagnosis
36
Aim 1, Table 4 (cont’d). Sensitivity Analyses for Multivariable Competing Risk Regression Modeling Using Alternative Outcome Measures, Cohorts, or Later Years of Diagnosis
37
In a secondary analysis in women diagnosed between 2003 and 2014, the tamoxifen
era, ER status was no longer a significant predictor of contralateral recurrence (Aim 1, Table 4).
Further, from the beginning to the end of this era, risk of contralateral breast cancer decreased.
Women diagnosed from 2007 to 2009 were about 11% (OR: 0.89; 95% CI, 0.81-0.98) less likely
to experience a contralateral breast event, and women diagnosed from 2010 to 2014 were
about 18% (OR: 0.82; 95% CI, 0.73-0.93) less likely to experience a contralateral breast event
compared with women diagnosed in 2003-2006.
Adjusting for other factors, the multivariate model estimates that at 10 years, 5.3% (95%
CI, 4.8%-5.9%) of women with ER-positive DCIS will have experienced a contralateral breast
event versus 3.4% (95% CI, 2.7%-4%) of women with ER– DCIS on average (Aim 1, Figure 1).
Aim 1, Figure 1. Adjusted cumulative incidence of a contralateral breast event by ER statusa
a Among women diagnosed with ductal carcinoma in situ (DCIS) between 1/1/1990 and 12/31/2002. Women with DCIS found to be estrogen receptor (ER)-positive/borderline (solid black) or ER-unknown (dotted gray) have a higher risk of a subsequent contralateral breast event compared with women whose DCIS was ER-negative (dashed black).
38
Aim 2
Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the effect of regional use of radiation therapy on
this likelihood.
We identified 2679 women in SEER and 757 women in SEER-Medicare with stage 0 to III
breast cancer after DCIS who had received BCS without radiation for initial treatment (Aim 2,
Table 1 and Aim 2, Figures 1 and 2). These patients resided within 1 of 166 HSAs separated into
3 clusters based on use of radiation after BCS at initial DCIS diagnosis in SEER data (Aim 2,
Figure 3) or 97 HSAs in SEER-Medicare data (Aim 2, Table 2).
39
Aim 2, Table 1. Characteristics of Patients Receiving BCS Without Radiation Therapy for Primary DCIS and Who Had Second Breast Event (DCIS or Invasive Cancer)
40
Aim 2, Table 2. Association Between Patient Characteristics and Three-Level Cluster of Treatment Intensity for Primary DCIS
41
Aim 2, Table 2 (cont’d). Association Between Patient Characteristics and Three-Level Cluster of Treatment Intensity for Primary DCIS
42
Patients who lived in HSAs with the highest proportion of radiation use after BCS had
43% increased odds of receiving mastectomy relative to those within HSAs with the lowest
radiation use, corresponding to an adjusted increase in mastectomy use from 40.8% to 49.6% in
SEER (Aim 2, Figure 4). In SEER-Medicare, patients in HSAs with the highest proportion of
radiation use had 90% increased odds of receiving mastectomy relative to those in HSAs with
the lowest use (95% CI, 1.27-2.84), corresponding to an adjusted increase in mastectomy use
from 38.6% to 54.5% (Aim 2, Tables 3 and 4). In addition to treatment culture cluster, women
with younger age, higher income, higher recurrence stage, ER-negative recurrence status,
ipsilateral recurrence, year of secondary diagnosis, and interval to secondary diagnosis had
higher odds of receiving mastectomy for a secondary breast event (Aim 2, Table 3).
Analyses conducted with propensity score matching revealed similar associations, with
corresponding odds ratios of mastectomy after prior radiation: 1.87 (95% CI, 1.14-3.06) in SEER
and 1.56 (95% CI, 0.91-2.65) in SEER-Medicare (Aim 2, Table 5). Restricting the SEER analysis to
patients with an ipsilateral second diagnosis also showed a similar pattern, with a 63%
increased odds in HSAs with the highest proportion of radiation use (OR: 1.63; 95% CI, 1.17-
2.30).22
43
Aim 2, Table 3. SEER Analysis of Treatment for Second Breast Cancer After Receiving BCS Alone for Primary DCISa
a Treatment is defined using SEER variables. Multivariate logistic regression for the outcome of receiving mastectomy (versus BCS+/–RT) for secondary breast event.
44
Aim 2, Table 4. SEER-Medicare Analysis of Treatment for Secondary Breast Cancer After Receiving BCS Alone for Primary DCIS
a Treatment is defined using Medicare claims. Multivariable logistic regression for the outcome of receiving mastectomy (BC+/–RT) for secondary breast event.
45
Aim 2, Figure 4. Adjusted odds ratios for receipt of mastectomy at the time of a second breast event by radiotherapy use for primary DCIS
46
Aim 2, Table 5. Propensity Score Matched Samples: Association Between Patient Characteristics and Three-Level Cluster of Treatment Intensity for Primary DCIS
47
48
Aim 3
Integrate the findings of Aims 1 and 2, to determine the tradeoffs associated with
radiation therapy for DCIS in terms of breast conservation for an individual patient in a web-
based decision aid.
Analysis and Model Outcomes
To achieve stable estimates of model outcomes, a population of 1 million women of a
given age at diagnosis was simulated for each treatment strategy, and the history of events for
each individual was tracked over her lifetime and aggregated. The model outcomes were
disease-free survival, invasive disease-free survival, overall survival, and likelihood of breast
preservation over a 10-year and lifetime horizon. The simulation process was automated to
create the model output tables for the decision tool.
Model Validation
We validated the model in the first stage of development by comparing our model
outputs to data not used in model development obtained from the European Organisation for
Research and Treatment of Cancer (EORTC) randomized trial 10853, which reported 10-year
event-free local recurrence rates of 74% for lumpectomy alone and 85% for the lumpectomy
with radiation arm.14 To replicate this trial, we simulated a population of women aged 53 years
at diagnosis (median age of EORTC trial). Our model predicted 10-year percentages of no
further local event in the ipsilateral breast of 71% for lumpectomy alone and 87% for
lumpectomy with radiation. Although our model projections for the effects of radiation were
larger than those found in the EORTC trial (16% versus 11% difference), the rank ordering of
outcomes across treatment strategies was consistent. The difference between the baseline
model and results from using the EORTC data could be expected given the different input
parameters of these studies.
49
We also compared model estimates of breast cancer–specific and overall survival with
SEER data for 3 ages (45, 60, and 70 years) of US women diagnosed with DCIS from 1995 to
2008.Error! Bookmark not defined. Regarding breast cancer–specific survival, our analysis showed very
similar results to SEER for all age groups. For overall survival, our results showed similar trends
for the 45- and 60-year-old age groups. However, SEER survival 10 years after diagnosis for 70-
year-old women was approximately 8% lower than the model estimate, consistent with known
differences in the SEER population versus the overall US population.24
Creating Lookup Tables for the DCIS Decision Tool
We used population-based distributions for times to event to model events of new DCIS
and invasive events for the contralateral breast, as well as DCIS and risk of ipsilateral invasive
breast cancer. To allow user-selected inputs to these models (ie, age over a range of 40-80
years and DCIS recurrence risk and invasive recurrence risk for the ipsilateral breast over a
range of 0%- 40%), we had to adjust the applied input distributions (eg, of average recurrence
risk) of the model by hazard ratios. We calculated hazard ratios for the input variables for the
lumpectomy arm and used these hazard ratios to adjust the other treatment arms as well. The
input distributions in the base-case model were survival distributions; for example, the survival
probability of invasive recurrence at 10 years for the lumpectomy arm was 0.836. In order to
calculate a 10-year invasive recurrence risk of 40% (= survival probability of 0.6), one of the
values listed above, we used the following formula in Excel: LOG(0.6;0.836) = 2.85. We then
used this hazard ratio of 2.85 to adjust the invasive recurrence risk distributions of the other
treatment arms for a model in which the 10-year invasive recurrence risk was 40%. For
example, the survival probability of invasive recurrence for lumpectomy with radiation was
0.945 at 10 years and was adjusted to 0.85 (0.945 ^ 2.85), for lumpectomy with radiation and
tamoxifen the probability of 0.954 was adjusted to 0.87, for lumpectomy with tamoxifen the
probability of 0.90 was adjusted to 0.74, and for mastectomy the probability of 0.99 was
adjusted to 0.97. We repeated this procedure to calculate hazard ratios for all values of DCIS
recurrence risk and invasive recurrence risk listed above. We then applied these hazard ratios
50
over all time points of the distributions and for all treatment arms. The hazard ratios used in
the model are shown in Aim 3, Table 4.
Aim 3, Table 4. Hazard Ratios Used in Model
Recurrence Risk (%)
Invasive Ipsilateral (Hazard Ratios)
DCIS Ipsilateral (Hazard Ratios)
0 1E-14 1E-14
1 0.056107424 0.066126566
2 0.112784477 0.132924479
3 0.170042843 0.200407511
4 0.22789457 0.268589861
5 0.286352086 0.337486176
6 0.345428211 0.407111565
7 0.405136178 0.477481626
8 0.46548965 0.548612458
9 0.526502735 0.620520692
10 0.588190011 0.693223509
11 0.650566546 0.766738664
12 0.713647914 0.841084516
13 0.777450229 0.916280054
14 0.841990158 0.992344923
15 0.907284958 1.06929946
16 0.973352495 1.147164723
17 1.040211279 1.225962526
18 1.107880492 1.305715478
19 1.176380023 1.386447017
51
Recurrence Risk (%)
Invasive Ipsilateral (Hazard Ratios)
DCIS Ipsilateral (Hazard Ratios)
20 1.245730501 1.468181459
21 1.315953336 1.550944033
22 1.387070753 1.634760936
23 1.459105839 1.719659378
24 1.532082587 1.805667634
25 1.606025943 1.892815106
26 1.680961856 1.981132377
27 1.756917337 2.070651281
28 1.833920513 2.161404967
29 1.912000691 2.253427977
30 1.991188426 2.346756321
31 2.071515592 2.441427564
32 2.153015459 2.537480919
33 2.235722775 2.634957338
34 2.319673857 2.733899622
35 2.404906684 2.834352534
36 2.491461003 2.936362917
37 2.579378437 3.039979829
38 2.668702611 3.145254682
39 2.759479273 3.252241396
40 2.851756444 3.360996565
52
Combining 1-year age, 1% DCIS, and invasive recurrence risk increments would result in
40 x 40 x 40 = 64 000 combinations of variables and thus in 64 000 model simulations. Because
of the computation time, we have run the model for 5-year age, 5% DCIS, and invasive
recurrence risk increments (ie, 9 x 9 x 9 = 729 simulations), and have used interpolation to
obtain the other values.
Translating Output Data into Spreadsheets
The outputs we used in the decision tool were disease-free survival; invasive disease-
free survival and overall survival; likelihood of death from breast cancer and other causes;
likelihood of recurrence leading to lumpectomy, mastectomy, mastectomy with reconstruction,
or no further event; and likelihood of chest wall recurrence. Most of these could be directly
obtained from the model output, with some exceptions of extra steps of processing. For the
data over a 10-year time horizon, we wanted to calculate the likelihood of a further event (ie,
recurrence leading to lumpectomy, mastectomy, mastectomy with reconstruction, or no
further event) conditional on the woman being alive after 10 years. Therefore, we divided the
likelihood of these events occurring by the probability of being alive. To calculate the likelihood
of no further event we subtracted the likelihood of recurrence leading to lumpectomy,
mastectomy, and mastectomy with reconstruction from 1 (Stout NK, Cronin AM, Uno H, et al,
unpublished data).
53
DISCUSSION
Aim 1
Determine the risk of and risk factors for new contralateral breast cancer after DCIS.
There is much debate about whether DCIS represents a precursor lesion versus a marker
of increased breast cancer risk.32 Our analysis of SEER data suggests that ER-positive DCIS is
more likely to also be a marker of increased propensity for new contralateral breast cancer, and
treatment may be more accurately described as secondary prevention/prophylaxis. In contrast,
ER-negative DCIS, which is less associated with contralateral cancer and more associated with
ipsilateral cancer, may be a local risk factor or precursor lesion.33 For ER-positive DCIS
diagnoses, systemic adjuvant therapy with an anti-estrogen may be more indicated, whereas
for ER-negative DCIS, local therapy with radiation may be more important. At present, less than
half of all women diagnosed with DCIS, including the more than 70% who are ER-positive,
initiate an adjuvant anti-estrogen therapy.34,35 Women with ER-positive DCIS should be engaged
in a discussion about the side effects of anti-estrogen therapy versus its benefits. Stratification
of DCIS by risk profiles that include ER status and other prognostic factors may help tailor
treatment approaches and ameliorate the concerns about overtreatment for this prevalent
condition. To our knowledge, however, no study has considered the independent effects of ER
status on contralateral breast cancer risk following DCIS.
Aim 1 Limitations
While our study was made possible by the large numbers of patients in the population-
based SEER cancer database, there are several limitations. There may be underascertainment of
contralateral breast cancer diagnoses in SEER (Aim 1, Table 1). However, ascertainment is
unlikely to vary by ER status, and is thus unlikely to affect our primary finding. Importantly,
SEER does not contain information about use of anti-estrogen therapy known to reduce the risk
of contralateral breast cancer. The first report of the NSABP B-2410 trial documenting this effect
54
was published in June 1999, and documentation that the main benefit from tamoxifen was seen
in patients with ER-positive DCIS came at the end of 2002. We therefore controlled for year in
our analysis to account for secular effects and restricted our primary analysis to diagnoses
before 2002i, as the presentation of documenting the differential effects of tamoxifen by ER
status was delivered at the end of 2002. In this pre-tamoxifen era for DCIS, testing for ER status
was infrequent, and only 14% of women in our sample had known values. Adjustment for
potential confounders (eg, race, tumor characteristics, socioeconomic status) did not
appreciably change the univariate relationship between ER and contralateral cancer
(unadjusted analysis not shown). Further, our findings are consistent with an increase in
contralateral risk with ER-positive DCIS suggested in a subgroup analysis of the NSABP B-24
trial.10 This lends support for our findings despite the high level of missingness in the ER status
variable.
Aim 2
Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the effect of regional use of radiation therapy on
this likelihood.
The decision between whether to pursue more or less aggressive treatment for a
medical condition is ideally made by a patient who weighs the pros and cons of each approach
regarding the outcomes valued by the patient. However, regional treatment paradigms also
influence these decisions, leading to regional variation in use of therapies instead of use
directed by patient preferences, which can be a marker of poor quality of care.36
We demonstrated that patients who had a second breast event were more likely to
have a mastectomy instead of BCS if they lived in an area with greater use of radiation. Because
we wanted to study surgical choice at time of second diagnosis (as opposed to studying upfront
treatment for DCIS), we restricted our analysis to patients who underwent BCS without RT for
DCIS, who might be candidates for repeat BCS at time of second diagnosis. Local treatment
55
intensity for DCIS, defined by the proportion of women who undergo RT after BCS, was
associated with an increased likelihood of mastectomy at time of second diagnosis among
women who have not received radiation for their initial DCIS. This association persisted after
adjustment for a large number of demographic, regional, and clinical factors that might be
important in treatment choices. To ensure MRI before diagnosis of secondary breast cancer did
not confound results, we added this as a covariate in the SEER-Medicare model. Our
conclusions were unchanged (data not shown).
Aim 2 Limitations
Limitations of the SEER database include the lack of sensitivity for capturing radiation
use37 and second breast events. However, our results were consistent among such patients in
the SEER-Medicare database when we used claims linked to reimbursement to identify
radiation and second events. No dataset can capture all the complexity surrounding surgical
decision making at time of second diagnosis. As this is an epidemiologic study, we did not have
information about patient preferences, availability of breast tissue for good cosmetic results
after repeat BCS, or clinical characteristics (beyond stage) about the second breast event, which
could affect surgical choice. Nevertheless, we did control for stage in our analyses, and our
results surrounding the effect of local treatment intensity on mastectomy use at second
diagnosis were stable when limiting the analyses to ipsilateral events in SEER. Additionally, we
did not find evidence to suggest these characteristics would vary systematically by region,
which is the only way they would bias our findings.22
56
Aim 3
Integrate the findings of aims 1 and 2, to determine the tradeoffs associated with
radiation therapy for DCIS in terms of breast conservation for an individual patient in a web-
based decision aid.
The web-based decision tool that we developed is located at the following website:
https://preview.cornerstonenw.com/dfci-dcis (Aim 3, Screen captures 1-5). This website allows
patients to enter information about themselves (eg, age, risk estimate), choose the types of
treatments in which they are interested, and see the predicted consequence. Once patients
select the types of treatments for which they want more information, they can see different
health predictions. For example, if a 40-year-old patient with ER-positive DCIS indicates she
wants more information about lumpectomy only and mastectomy, the website will show her
chance of not having cancer in the breast in the next 10 years with lumpectomy only versus
mastectomy. The website also gives patients a discussion guide about which treatment is best
for them and provides information about next steps. Patients can also print a summary of the
information they received from this website.
Stakeholders guided the process of designing the web-based decision tool. During the
initial teleconference discussing the first iteration of the decision tool, stakeholders made
suggestions about how to reformat the website so it looks more patient friendly. Stakeholders
also advised how to rephrase confusing medical terminology, and to include graphics with
numerical information to facilitate a better understanding of all the treatment choice
information.
Once the website was near completion, each stakeholder went through the website and
viewed it from an example patient’s perspective through user experience testing. They
thoroughly read each section of the website, saw their own results, and noted any feedback
throughout the process. After all stakeholders completed their review of the website in this
57
manner, we compiled their comments into a master feedback document, which allowed
investigators to incorporate and keep track of all changes made to the website.
Aim 3 Limitations
Our analysis has limitations inherent to all modeling analyses. It was necessary to make
a limited number of assumptions about the natural history and treatment of DCIS in order to
specify a finite number of clinical states. However, we used estimates derived from the
literature or databases to inform our baseline analysis and allowed for flexibility of the model
for additional refinement of personalized recurrence risk.
58
CONCLUSIONS
Aim 1
Determine the risk of and risk factors for new contralateral breast cancer after DCIS.
We demonstrated that DCIS characterized by expression of the estrogen receptor is
associated with a statistically increased risk of having a contralateral breast cancer diagnosis.
Moreover, this association was also significant when studying invasive contralateral cancers,
which carry the concomitant risk of spread to the lymph nodes and distant sites.
Aim 2
Determine the likelihood of mastectomy at time of recurrence or new diagnosis after
DCIS in a previously unirradiated breast and the effect of regional use of radiation therapy on
this likelihood.
Our study showed that geographic areas with more RT use for DCIS had increased use of
mastectomy at the time of a second breast event even among patients eligible for breast
conservation. This association suggests factors beyond patient preference and clinical
determinants (eg, provider biases) are affecting the likelihood of breast preservation.22
Aim 3
Integrate the findings of aims 1 and 2, to determine the tradeoffs associated with
radiation therapy for DCIS in terms of breast conservation for an individual patient in a web-
based decision aid.
Our study developed a DES model that integrates data from the published literature to
simulate the clinical events after 6 treatments (lumpectomy alone, lumpectomy with radiation,
lumpectomy with radiation and tamoxifen, lumpectomy with tamoxifen, and mastectomy with
and without breast reconstruction) for women with newly diagnosed DCIS. We successfully
59
quantified the tradeoffs in terms of long-term survival and breast preservation of 6 treatment
scenarios for DCIS that are considered standard practice. This led to the development of a web-
based decision aid (the DCIS Decision Tool) that patients can visit to explore the treatment
options and expected outcomes related to their disease.
60
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NOTE ON PUBLISHED MATERIAL
One of the aims of this project (aim 2) was published in JAMA Oncology. We received
permission from the publisher to use excerpts of text, tables, and figures from the published
article:
Punglia RS, Cronin AM, Uno H, et al. Association of regional intensity of ductal
carcinoma in situ treatment with likelihood of breast preservation. JAMA Oncol. 2017;3(1):101-
104. doi: 10.1001/jamaoncol.2016.2164
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APPENDIX
Screen capture 1. Homepage of decision tool
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Screen capture 2. Patient- or family member-facing “Tell us about yourself” page
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Screen capture 3. Patient- or family member-facing “Available treatments” page
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Screen capture 4. Example results for a 60-year-old, ER+ woman
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Screen capture 5. Provider-facing “Treatment outcomes” page
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Copyright© 2020. Dana-Farber Cancer Institute. All Rights Reserved.
Disclaimer:
The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.
Acknowledgement:
Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#CE-12-11-4173) Further information available at: https://www.pcori.org/research-results/2013/predicting-impact-treatment-options-survival-and-breast-conservation-patients