Predicting the Impact of Treatment Options on …...DCIS. We sought to study whether the regional...

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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

https://hsrproject.nlm.nih.gov/view_hsrproj_record/20143205

http://clinicaltrials.gov/ct2/show/NCT02248662

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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


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.)

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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).

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Aim 1, Table 2. Variable Mapping

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Aim 2



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).

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Aim 2, Figure 1. Radiotherapy (RT) use after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS)

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Aim 2, Figure 2. Flow diagram for SEER analysis

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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

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Aim 2, Figure 3. Flow diagram for SEER-Medicare cohort

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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



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

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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

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Aim 3, Figure 1. Comparing 6 different treatment strategies

22

Aim 3, Figure 2. Competing events that can occur

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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

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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



Lumpectomy with radiation and tamoxifena



Lumpectomy with tamoxifen 7

Ipsilateral 0.35 0.44 HR

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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


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

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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).

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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.

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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

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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


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



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

48

Aim 3

Integrate the findings of Aims 1 and 2, to determine the tradeoffs associated with


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


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



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



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

https://preview.cornerstonenw.com/dfci-dcis

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


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



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



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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https://www.healthaffairs.org/doi/10.1377/hlthaff.W2.96

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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

https://doi.org/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

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