The Role of Decision Aids and Soft Tissue Simulations in Breast Reduction Surgery: A Pilot Study

Summary:

In this study, we aimed to assess the impact of soft tissue simulations as a decision aid on BREAST-Q outcomes after breast reduction surgery. A total of 7 patients underwent breast reduction. The mean BREAST-Q scores at baseline and 6 months showed significant differences in the domains of satisfaction with breasts (20.1 ± 10.1 versus 78.3 ± 8.1; P < 0.001), psychosocial well-being (29.2 ± 12.2 versus 81.2 ± 9.0; P < 0.001), sexual well-being (29.0 ± 14.5 versus 74.0 ± 12.3; P < 0.001), and physical well-being (40.2 ± 14.3 versus 78.6 ± 10.2; P < 0.001). When patients were asked, “On a scale of 1 to 10, how challenging do you find the decision-making process for selecting a postoperative breast cup size?” the mean score was 9.6 ± 0.5. When asked, “How helpful are these images in helping you choose your preferred breast cup size?” all respondents selected “extremely helpful” (n = 10, 100.0%). This pilot study supported the use of soft tissue simulations as a decision aid to improve patient satisfaction.

Takeaways

Question: Do decision aids using soft tissue simulations (STSs) improve BREAST-Q outcomes after breast reduction?

Findings: A total of 7 patients underwent breast reduction. Mean BREAST-Q scores at baseline and 6 months showed significant differences in the domains of satisfaction with breasts (20.1 ± 10.1 versus 78.3 ± 8.1; P < 0.001), psychosocial well-being (29.2 ± 12.2 versus 81.2 ± 9.0; P < 0.001), sexual well-being (29.0 ± 14.5 versus 74.0 ± 12.3; P < 0.001), and physical well-being (40.2 ± 14.3 versus 78.6 ± 10.2; P < 0.001). When asked, “How helpful are these [STS] in helping you choose your preferred breast cup size?” all respondents selected “extremely helpful” (n = 10, 100.0%).

Meaning: This pilot study supports the use of STS as a decision aid to improve patient satisfaction.

INTRODUCTION

According to the American Society of Plastic Surgeons (ASPS), 71,364 breast reductions (BRs) were performed in the United States in 2022, marking a 54% increase since 2019.¹ As the popularity of this procedure continues to grow, so, too, should our toolkit for perioperative patient management. Traditionally, plastic surgeons have used a patient’s ideal bra cup size as a benchmark for the patient’s expectations and surgical planning. Yet, studies reveal a 70%–100% discrepancy between the patient’s perceived cup size and the actual measured size.^2–4 This variability complicates the surgeon’s task of accurately meeting patients’ expectations.⁵

Mardinger et al⁶ found that decision aids, such as Breast Reconstruction Decision Aid (BRECONDA) and Alberta Health Services, are valuable in breast reconstruction consultations by helping to decrease decisional conflict and improve patient satisfaction. This highlights the potential for advanced technologies in patient interactions, such as soft tissue simulation (STS), which uses virtual 3-dimensional (3D) modeling to provide a visual framework for both patients and surgeons.

METHODS

Following institutional review board approval (MedStar Health Research Institute [MHRI], Hyattsville, MD; STUDY00005465), a single institution prospective case series of patients undergoing BR from August 2023 to December 2023 was conducted. Patients were included if they (1) were aged 18 years or older, (2) had a primary diagnosis of macromastia, and (3) underwent a BR. Patients who did not meet inclusion requirements, including those who underwent revision BRs, were excluded from the study. Patients completed BREAST-Q preoperatively and at 1, 3, and 6 months postoperatively, and a self-generated survey preoperatively.⁷ (See table, Supplemental Digital Content 1, which displays the STS patient satisfaction survey, https://links.lww.com/PRSGO/E336.) We used MirrorMe3D (New York, NY) to create 3D scans of the patient’s current breast cup size and personalized STS options for reconstruction, which were reviewed at a preoperative visit (Fig. 1).

Fig. 1.

STSs generated by MirrorMe3D. A, Patient’s preoperative scan. B–D, STSs for predicted breast cup sizes.

MirrorMe3D’s STS technology generates 3D visualizations of a patient’s breast anatomy by integrating external imaging, including patient-uploaded photographs via a smartphone app. Patients take standardized images as directed by the app and upload them to the MirrorMe3D platform, where advanced algorithms convert them into high-resolution 3D breast models. During a “Sim Session,” surgeons can manipulate these models to simulate BR outcomes by modifying soft tissue contours and volumes to reflect different potential sizes. A case report is generated, comparing baseline anatomy with the simulated outcomes, which can be reviewed with the patient to guide surgical planning. It is particularly valuable for patients who are skeptical between 2 feasible cup sizes, providing visual simulations to aid patient–surgeon communication and decision-making.

Descriptive statistics were calculated for all patient data. Continuous variables were analyzed using Mann-Whitney tests or unpaired 2-tailed t tests, whereas Pearson chi-square or Fisher exact tests (for n < 5) were used for categorical variables. Statistical analysis was conducted using StataBE (StataCorp LLC, College Station, TX), with significance set at a P value of less than 0.05.

RESULTS

Seven patients (14 breasts) underwent BR. The median age was 38.4 (interquartile range [IQR], 35.5–55.8) years. Most patients were identified as Black (n = 6, 85.7%), followed by White (n = 1, 14.3%). The median body mass index was 33.4 (IQR, 30.5–35.3) kg/m², with 6 (85.7%) classified as obese. Regarding breast characteristics, median nipple-to-inframammary fold and sternal notch-to-nipple distances were 12.0 (IQR, 9.8–14.8) and 33.5 (IQR, 32.0–36.8) cm, respectively. All BRs used the superomedial pedicle (n = 7, 100.0%). The median operative time was 140.0 (IQR, 106.8–206.0) minutes. The median weight of breast tissue resected per breast was 409.0 (IQR, 284.5–646.3) g. Over a mean follow-up duration of 4.0 ± 2.7 months, 0 (0.0%) patients experienced any complications.

Mean BREAST-Q scores at baseline and 6 months showed significant differences in the domains of satisfaction with breasts (20.1 ± 10.1 versus 78.3 ± 8.1; P < 0.001), psychosocial well-being (29.2 ± 12.2 versus 81.2 ± 9.0; P < 0.001), sexual well-being (29.0 ± 14.5 versus 74.0 ± 12.3; P < 0.001), and physical well-being (40.2 ± 14.3 versus 78.6 ± 10.2; P < 0.001). (See table, Supplemental Digital Content 2, which displays BREAST-Q scores at baseline, 1, 3, and 6 months after surgery, https://links.lww.com/PRSGO/E337) When patients were asked, “On a scale of 1 to 10, how challenging do you find the decision-making process for selecting a postoperative breast cup size?” the mean score was 9.6 ± 0.5. When asked, “How helpful are these images in helping you choose your preferred breast cup size?” all respondents selected “extremely helpful” (n = 7, 100.0%).

DISCUSSION

Decision aids improve patient knowledge, reduce decisional conflict, and increase satisfaction in the decision-making process. In our pilot study, BR patients who used STS during the preoperative period demonstrated high satisfaction. This finding aligns with previous research and is particularly relevant for BR patients, who must select an ideal postoperative cup size.⁸ However, it is well established that cup size is arbitrary, as it does not account for individual variations in body proportions and personal preferences. Our patients reported finding the decision-making process for selecting a breast cup size highly challenging, with a mean difficulty score of 9.6.

We found that STS helps set realistic expectations by providing a visual representation of surgical outcomes. Notably, STS serves as a tool to increase patient engagement and autonomy in the decision-making process. Rather than predicting an exact postoperative cup size, STS provides a personalized visual reference, helping patients set realistic expectations and feel more involved in their surgical planning. In our cohort, patients unanimously found these images extremely helpful in choosing their preferred breast cup size (Fig. 1). STS may also reduce decisional regret. In a randomized control trial, Sherman et al⁹ found that patients undergoing breast reconstruction with access to BRECONDA had less decisional regret compared with controls. STS not only enhances patients’ understanding of potential results but also empowers them by actively involving them in the decision-making process. Our findings suggest that patients valued the ability to see a simulation of their own anatomy and compare possible outcomes, potentially reducing preoperative anxiety and postoperative decisional regret.

A statistically significant improvement was observed across all BREAST-Q domains when measured preoperatively to postoperatively. Notably, the mean changes in BREAST-Q scores at 3 months were higher than those reported by Crittenden et al¹⁰ at 12 months. This suggests that STS not only facilitates immediate postoperative satisfaction but may also contribute to short-term improvements in BREAST-Q scores at 6 months postoperatively. Although a comparison with Crittenden et al¹⁰ highlights variations in BREAST-Q score trajectories, further research, including studies comparing STS with non-STS groups, is required to evaluate the long-term impact of STS on patient satisfaction. Moreover, when asked, “Would you recommend having [similar images] available to patients before they decide on breast reduction surgery?” all respondents selected “yes” (n = 10, 100.0%). Notably, patients reported the following as benefits of using this tool: “The [images] gave me a realistic idea of what to expect, which reduced my anxiety [about the surgery],” “I liked how it compare[d] different [cup size] options,” “I liked how the images were specific to my body type,” and “I feel more confident going into surgery.”

STS is a promising adjunct, helping align the surgeon’s and patient’s goals and reducing operative time. However, STS does have limitations such as high costs, limited accessibility, need for specialized training, and technological dependence. Additionally, generating renderings can take 1 or more weeks to complete. Nonetheless, advances in artificial intelligence can enhance the accuracy of anatomical representations, decrease rendering times, and streamline the planning process. This can help in planning the precise amount of tissue to be removed and the optimal pedicle design of the breast.

Limitations and Future Directions

First, this study lacks side-by-side preoperative, STS-generated, and postoperative images. Although our focus was on STS as a decision aid rather than a predictive tool, visual comparisons could further validate its accuracy. Additionally, future research should focus on conducting a prospective comparative study to assess the impact of STS on patient satisfaction, decisional conflict, and postoperative regret. An interactive decision aid specific to BR should be developed, similar to BRECONDA. This tool should include BR-related information, strategies for managing emotions, and video segments detailing other patients’ experiences. Integrating STS into these decision aids could enhance their effectiveness by providing personalized and accurate visual representations of surgical outcomes.

CONCLUSIONS

BR patients have a significant health burden and reduced quality of life, which significantly improved following surgery. This pilot study supported the use of STS as a decision aid to improve patient satisfaction.

DISCLOSURE

The authors have no financial interest to declare in relation to the content of this article.

ACKNOWLEDGMENTS

The authors acknowledge the support provided by MirrorMe3D, which supplied the STS materials. The conceptualization, methodology, formal analysis, and writing were conducted independently by the authors.