Commentary: Creating a patient-centered decision aid for ductal carcinoma in situ

Factors associated with ductal carcinoma in situ (DCIS) treatment patterns and patient-reported outcomes across a large integrated health network

PURPOSE

To examine associations between ductal carcinoma in situ (DCIS) patients' characteristics, treating locations and DCIS treatments received and to pilot assessing quality-of-life (QoL) values among DCIS patients with diverse backgrounds.

METHODS

We performed a retrospective tumor registry review of all patients diagnosed and treated with DCIS from 2018 to 2019 in the UPMC-integrated network throughout central and western Pennsylvania. Demographics, clinical information, and administered treatments were compiled from tumor registry records. We categorized contextual factors such as different hospital setting (academic vs. community), socioeconomic status based on the neighborhood deprivation index (NDI) as well as age and race. QoL survey was administered to DCIS patients with diverse backgrounds via QoL questionnaire breast cancer module 23 and qualitative assessment questions.

RESULTS

A total of 912 patients were reviewed. There were no treatment differences noted for age, race, or NDI. Mastectomy rate was higher in academic sites than community sites (29 vs. 20.4%; p = 0.0045), while hormone therapy (HT) utilization rate was higher in community sites (74 vs. 62%; p = 0.0012). QoL survey response rate was 32%. Only HT side effects negatively affected in QoL scores and there was no significant difference in QoL domains and decision-making process between races, age, NDI, treatment groups, and treatment locations.

CONCLUSION

Our integrated health network did not show chronically noted disparities arising from social determinates of health for DCIS treatments by implementing clinical pathways and system-wide peer review. Also, we demonstrated feasibility in collecting QoL for DCIS women with diverse backgrounds and different socioeconomic statuses.

Prediction Models and Decision Aids for Women with Ductal Carcinoma In Situ: A Systematic Literature Review

Simple Summary

Ductal carcinoma in situ (DCIS) is a potential precursor to invasive breast cancer (IBC). Although in many women DCIS will never become breast cancer, almost all women diagnosed with DCIS undergo surgery with/without radiotherapy. Several studies are ongoing to de-escalate treatment for DCIS. Multiple decision support tools have been developed to aid women with DCIS in selecting the best treatment option for their specific goals. The aim of this study was to identify these decision support tools and evaluate their quality and clinical utility. Thirty-three studies were reviewed, in which four decision aids and six prediction models were described. While some of these models might be promising, most lacked important qualities such as tools to help women discuss their options or good quality validation studies. Therefore, the need for good quality, well validated decision support tools remains unmet.

Abstract

Even though Ductal Carcinoma in Situ (DCIS) can potentially be an invasive breast cancer (IBC) precursor, most DCIS lesions never will progress to IBC if left untreated. Because we cannot predict yet which DCIS lesions will and which will not progress, almost all women with DCIS are treated by breast-conserving surgery +/− radiotherapy, or even mastectomy. As a consequence, many women with non-progressive DCIS carry the burden of intensive treatment without any benefit. Multiple decision support tools have been developed to optimize DCIS management, aiming to find the balance between over- and undertreatment. In this systematic review, we evaluated the quality and added value of such tools. A systematic literature search was performed in Medline(ovid), Embase(ovid), Scopus and TRIP. Following the PRISMA guidelines, publications were selected. The CHARMS (prediction models) or IPDAS (decision aids) checklist were used to evaluate the tools’ methodological quality. Thirty-three publications describing four decision aids and six prediction models were included. The decision aids met at least 50% of the IPDAS criteria. However, most lacked tools to facilitate discussion of the information with healthcare providers. Five prediction models quantify the risk of an ipsilateral breast event after a primary DCIS, one estimates the risk of contralateral breast cancer, and none included active surveillance. Good quality and external validations were lacking for all prediction models. There remains an unmet clinical need for well-validated, good-quality DCIS risk prediction models and decision aids in which active surveillance is included as a management option for low-risk DCIS.