Background
Germline genetic testing (GGT) is essential for identifying individuals with or at increased risk for hereditary breast cancer, guiding therapy decisions for patients, and recognizing at-risk relatives. Unaffected women who carry pathogenic germline variants (PVs) in cancer predisposition genes may choose risk-reducing strategies, such as chemoprevention, enhanced screening, or surgery, to prevent cancer or facilitate early detection.1,2 Treatment strategies for women with PVs who are diagnosed with breast cancer vary by gene. For example, the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic recommend discussing risk-reducing mastectomy (RRM) and risk-reducing salpingo-oophorectomy (RRSO) for patients with PVs in BRCA1, BRCA2, and PALB2; however, current evidence is insufficient to recommend discussion of RRM for those with PVs in other breast cancer predisposition genes.1 In addition, radiation therapy is relatively contraindicated in women with PVs in TP53, whereas PARP inhibitors (PARPi) are recommended for patients with high-risk or advanced disease and PVs in BRCA1 and/or BRCA2 (BRCA1/2).3–5
In contrast to clinical management recommendations for women with PVs, the American College of Medical Genetics and Genomics advises that variants of uncertain significance (VUS) should not influence medical management. Instead, patients with VUS—like those with negative findings—should be counseled based on factors such as family and medical history.6,7 Despite these guidelines, some physicians may not fully understand how to manage VUS.8 Prior studies have found that a substantial proportion of surgeons manage patients with BRCA1/2 VUS similarly to those with BRCA1/2 PVs.8–10 Additionally, genetic counselors report challenges in helping patients understand VUS results.10,11
The use of multigene panel testing (MGPT) has largely supplanted single-gene testing for patients with or at risk for breast cancer, with concomitant increased rates of VUS ranging from 25% to 54% in patients with breast cancer.12–15 With this increase, it is important to understand how VUS results influence health care utilization. Studies of risk-reducing surgeries in patients with VUS have shown mixed results. Some found that patients with VUS opted for risk-reducing breast surgeries at significantly higher rates than those with negative GGT results,9,16,17 whereas others reported no significant differences in rates of risk-reducing surgeries and surveillance.18–21 However, some of these studies were limited by small sample sizes and a focus solely on breast surgery. A more recent study of health care utilization in cancer-free women undergoing MGPT found no significant increase in health care utilization or costs among 2,149 patients with VUS compared with those with negative results.22 Using real-world data from a commercial claims database, we evaluated rates of treatment, surgical, and surveillance modalities among >50,000 women who underwent MGPT for breast cancer risk. Our objective was to determine the impact of a VUS result on the utilization and cost of clinical resources.
Methods
Data Sources
This cross-sectional study used deidentified GGT data from women who underwent MGPT between 2015 and 2023 (Invitae Corp., now part of Labcorp). The use of GGT data was approved by the Western-Copernicus Group (WCG) Institutional Review Board (study number 1167406). Health care utilization data linked to insurance claims, including inpatient, outpatient, and pharmacy claims, were extracted from the Komodo Healthcare Map (Komodo Healthcare). GGT and claims data were linked using a unique patient token number, resulting in a merged dataset for each woman that contained no protected health information. This study adhered to the STROBE reporting guidelines.
Study Sample
We included women diagnosed with primary breast cancer ≤120 days before GGT and cancer-free women who underwent hereditary cancer MGPT using panels that analyzed at least 13 genes: BRCA1, BRCA2, CDH1, PALB2, PTEN, STK11, TP53 (designated as high-risk breast cancer susceptibility genes, with PVs conferring >50% lifetime cancer risk), and ATM, BARD1, CHEK2, NF1, RAD51C, and RAD51D (designated as moderate-risk breast cancer susceptibility genes, with PVs conferring 20% to 50% lifetime cancer risk). Patients were categorized as positive if they had a PV in at least one of these genes, uncertain if they had at least one VUS without a PV in any hereditary cancer gene, or negative if they had neither a PV nor a VUS in any hereditary cancer gene. The date of GGT served as the index date. All patients were required to have ≥12 months of continuous enrollment in the claims database before and after the index date. Those with a previous history or diagnosis of any additional cancer types were excluded.
Women were classified as having breast cancer using ICD-10 codes for invasive breast cancer or ductal carcinoma in situ (Supplementary Table S1, available in the supplementary materials). ICD codes were also used to determine the presence of regional or distant metastasis. Cancer-free women were defined as those without any ICD-10 codes for a cancer diagnosis. All surgical procedures were identified using CPT codes, and imaging modalities and radiation therapy were captured through CPT codes. Pharmaceutical use was extracted using HCPCS and NDC codes. Demographic and clinical data, including family history of cancer, patient insurance type, and geographic location, were extracted from the Komodo Healthcare Map. Age at testing and clinician-reported race and ethnicity were obtained from Invitae’s test requisition form.
Study Variables
In patients with breast cancer, health care utilization variables included the type of definitive breast surgery, RRSO, frequency and type of imaging, use of targeted therapy and chemotherapy, and magnetic resonance cholangiopancreatography (MRCP) and endoscopic ultrasound (EUS) for pancreatic cancer screening. Exclusion criteria for each treatment modality are shown in Supplementary Figure S1. Because tumor stage and biomarker data were unavailable, no additional exclusion criteria were applied for chemotherapy and targeted therapy analyses. Health care utilization variables in cancer-free women included breast surgery, RRSO, imaging, and chemoprevention. Exclusion criteria for this group are shown in Supplementary Figure S2.
Amounts billed to insurance for breast surgeries (including breast reconstruction), imaging, targeted therapy, and RRSO for the 12 months after the index date were extracted from the Komodo Healthcare Map using medical claims data.
Statistical Analysis
Chi-square or Fisher exact tests were used to compare categorical variable proportions across cancer status and GGT results. For variables with >2 levels, post hoc tests were conducted. P values for gene comparisons and definitive surgical treatment were adjusted for multiple testing using the Benjamini-Hochberg method. Two-sample t tests were used for continuous variables. The association of GGT results (VUS, which was the reference group; negative; PV) with health care utilization variables was assessed using multivariable logistic regression. Ethnicity, age at diagnosis, age in years squared (to account for the nonlinear effect of age), family cancer history (breast or ovarian cancer for RRSO, pancreatic for MRCP and EUS, and breast for all other outcomes), lymph node status (regional metastasis), and log-transformed time from diagnosis to GGT were included in the model to correct for confounding factors. For surgical treatment outcomes, P values and 95% confidence intervals were adjusted for multiple testing using the Bonferroni method. All analyses were performed using R Statistical Software (version 4.2.1; R Core Team, 2022), with P<.05 considered statistically significant.
Cost Analysis
Allowed amounts paid by insurance were adjusted for inflation to 2023 levels using the Consumer Price Index for health care services. Missing amounts were estimated using 50 iterations of a multivariate imputation by a chained equations model, with age, insurance type, geographic region, and procedure type as predictors.23 Estimated means for GGT results were adjusted for age, insurance type, and geographic region.
Results
Patient Characteristics
A total of 50,657 women were analyzed, including 22,699 with breast cancer and 27,958 without (Table 1). The average age at genetic testing was 47.7 years. Most participants were White (n=33,833; 66.8%, similar to the US population), had a reported family history of cancer (n=44,067; 87.0%), and were covered by commercial insurance (n=36,026; 71.1%).
Clinical and Demographic Characteristics
| All Women n (%) |
Positive n (%) |
VUS n (%) |
Negative n (%) |
P Value (Positive to Negative) |
P Value (VUS to Negative) |
P Value (VUS to Positive) | |
|---|---|---|---|---|---|---|---|
| Total, N | 50,657 | 4,001 (7.9) | 6,404 (12.6) | 40,252 (79.5) | |||
| Breast cancer status | 1.8 × 10−4 | .244 | .023 | ||||
| Affected | 22,699 (44.8) | 1,903 (47.6) | 2,898 (45.3) | 17,898 (44.5) | |||
| Unaffected | 27,958 (55.2) | 2,098 (52.4) | 3,506 (54.7) | 22,354 (55.5) | |||
| Age at testing, mean [SD], y | 47.7 [12.8] | 45.0 [13.6] | 47.7 [12.6] | 48.0 [12.7] | <2.2 × 10−16 | .072 | <2.2 × 10−16 |
| Median (Q1, Q3), y | 47 (39, 57) | 45 (35, 55) | 47 (39, 57) | 48 (39, 57) | |||
| Range, y | 18–90 | 18–90 | 18–90 | 18–90 | |||
| Ethnicity | .036 | <2.2 × 10−16a | <2.2 × 10−16b | ||||
| Ashkenazi Jewish | 1,357 (2.7) | 116 (2.9) | 64 (1.0) | 1,177 (2.9) | |||
| Asian | 1,616 (3.2) | 137 (3.4) | 382 (6.0) | 1,097 (2.7) | |||
| Black | 3,300 (6.5) | 218 (5.4) | 579 (9.0) | 2,503 (6.2) | |||
| Hispanic | 2,803 (5.5) | 209 (5.2) | 433 (6.8) | 2,161 (5.4) | |||
| White | 33,833 (66.8) | 2,703 (67.6) | 3,847 (60.1) | 27,283 (67.8) | |||
| Multiple | 4,400 (8.7) | 375 (9.4) | 577 (9.0) | 3,448 (8.6) | |||
| Other/Unknown | 3,348 (6.6) | 243 (6.1) | 522 (8.1) | 2,583 (6.4) | |||
| Family history of breast cancer | 4.2 × 10−15 | .947 | 5.7 × 10−11 | ||||
| Yes | 38,314 (75.6) | 3,232 (80.8) | 4,818 (75.2) | 30,264 (75.2) | |||
| No | 12,343 (24.4) | 769 (19.2) | 1,586 (24.8) | 9,988 (24.8) | |||
| Family history of ovarian cancer | 3.4 × 10−7 | .401 | 2.7 × 10−6 | ||||
| Yes | 10,348 (20.4) | 946 (23.6) | 1,265 (19.8) | 8,137 (20.2) | |||
| No | 40,309 (79.6) | 3,055 (76.4) | 5,139 (80.2) | 32,115 (79.8) | |||
| Family history of pancreatic cancer | 1.8 × 10−4 | .279 | .020 | ||||
| Yes | 17,401 (34.4) | 1,480 (37.0) | 2,224 (34.7) | 13,697 (34.0) | |||
| No | 33,256 (65.6) | 2,521 (63.0) | 4,180 (65.3) | 26,555 (66.0) | |||
| Family history of any cancer | 2.2 × 10−6 | .410 | 5.7 × 10−6 | ||||
| Yes | 44,067 (87.0) | 3,580 (89.5) | 5,536 (86.4) | 34,951 (86.8) | |||
| No | 6,590 (13.0) | 421 (10.5) | 868 (13.6) | 5,301 (13.2) | |||
| Insurance type | 1.8 × 10−5c | .024d | .006e | ||||
| Commercial | 36,026 (71.1) | 2,870 (71.7) | 4,476 (69.9) | 28,680 (71.3) | |||
| Medicaid/Managed Medicaid | 8,749 (17.3) | 747 (18.7) | 1,184 (18.5) | 6,818 (16.9) | |||
| Medicare/Medicare Advantage | 5,719 (11.3) | 368 (9.2) | 725 (11.3) | 4,626 (11.5) | |||
| Other | 99 (0.2) | 5 (0.1) | 12 (0.2) | 82 (0.2) | |||
| Unknown | 64 (0.1) | 11 (0.3) | 7 (0.1) | 46 (0.1) | |||
| Geographic region | |||||||
| Northeast | 11,395 (22.5) | 874 (21.8) | 1,446 (22.6) | 9,075 (22.5) | .002f | 3.4 × 10−4g | .032h |
| Midwest | 12,949 (25.6) | 1,074 (26.8) | 1,547 (24.2) | 10,328 (25.7) | |||
| South | 13,553 (26.7) | 993 (24.8) | 1,657 (25.9) | 10,903 (27.1) | |||
| West | 12,477 (24.6) | 1,041 (26.0) | 1,690 (26.4) | 9,746 (24.2) | |||
| Unknown | 283 (0.6) | 19 (0.5) | 64 (1.0) | 200 (0.5) | |||
| Clinician ordering germline genetic testing | |||||||
| Medical genetics | 5,826 (11.5) | 477 (11.9) | 722 (11.3) | 4,627 (11.5) | .024i | .812 | .045 |
| Oncology | 21,477 (42.4) | 1,769 (44.2) | 2,717 (42.4) | 16,991 (42.2) | |||
| Other specialty | 9,795 (19.3) | 719 (18.0) | 1,259 (19.7) | 7,817 (19.4) | |||
| Unknown | 13,559 (26.8) | 1,036 (25.9) | 1,706 (26.6) | 10,817 (26.9) | |||
Chi-square test or Fisher exact test (depending on cell count) was used for categorical variables and 2 sample t test was used for age.
Bold indicates statistically significant P value.
Abbreviations: Q, quarter; VUS, variant of uncertain significance.
Significant post hoc results: Ashkenazi Jewish, P<.0001; Asian, P<.0001; Black, P<.0001; Hispanic, P<.0001; White, P<.0001; Other/Unknown, P<.0001.
Significant post hoc results: Ashkenazi Jewish, P<.0001; Asian, P<.0001; Black, P<.0001; Hispanic, P=.021; White, P<.0001; Other/Unknown, P=.001.
Significant post hoc results: Medicaid, P=.038; Medicare, P=.0001.
Significant post hoc results: Medicaid, P=.018.
Significant post hoc results: Medicaid, P=.005.
Significant post hoc results: South, P=.016.
Significant post hoc results: West, P=.001.
Significant post hoc results: Midwest, P=.028.
Significant post hoc results: Other specialty, P=.039.
Genetic Testing Results
A total of 4,316 PVs and 6,798 VUS were detected in 4,001 (7.9%) and 6,404 (12.6%) women, respectively (Table 1). Women with VUS were older than those with positive results (47.7 vs 45.0 years; P<.001), but their age did not significantly differ from those with negative results (47.7 vs 48.0 years; P=.072). Women with VUS were also less likely to be White (60.1%) compared with those with positive (67.6%) or negative (67.8%) results (P<.001). The genes with the highest PV rates were CHEK2 (1.1%) and BRCA2 (0.7%), whereas the highest rates of VUS were observed in ATM (1.4%), BRCA2 (0.9%), and CHEK2 (0.8%) (Supplementary Figure S3).
Breast Cancer Treatment and Surveillance
Among patients with breast cancer, no statistically significant differences were observed in the odds of any cancer treatment, screening, or risk-reducing interventions between those with VUS and those with negative results (Figure 1, Supplementary Table S2). In contrast, significant differences were found between patients with VUS and those with positive results for most treatment modalities. Patients with positive results had higher odds of RRM (OR, 3.1; 95% CI, 2.5–3.8; P<.001), chemotherapy (OR, 1.4; 95% CI, 1.2–1.7; P<.001), PARPi therapy (OR, 82.9; 95% CI, 11.4–600.4; P<.001), MRI (OR, 1.7; 95% CI, 1.4–2.1; P<.001), and RRSO (OR, 8.7; 95% CI, 6.8–11.3; P<.001).
Forest plot depicting the likelihood of uptake of breast cancer treatment among women with breast cancer. Patients with variants of uncertain significance served as the reference group.
Abbreviations: EUS, endoscopic ultrasound; MRCP, magnetic resonance cholangiopancreatography; PARPi, PARP inhibitor; PD-1/PD-L1i, PD-1/PD-L1 inhibitor; RRM, risk-reducing mastectomy; RRSO, risk-reducing salpingo-oophorectomy.
Citation: Journal of the National Comprehensive Cancer Network 2025; 10.6004/jnccn.2025.7011
Breast Cancer Risk-Reducing Interventions in Cancer-Free Women
Among cancer-free women, only imaging use differed between women with VUS and those with negative results. Women with negative results were slightly but significantly less likely than those with VUS to undergo imaging (OR, 0.9; 95% CI, 0.8–1.0; P=.002), which was attributable to lower odds of mammography (OR, 0.9; 95% CI, 0.8–0.9; P<.001) (Figure 2, Supplementary Table S3). In contrast, as observed in women with breast cancer, the uptake of all interventions was significantly higher in cancer-free women with positive results compared with those with VUS, including RRM (OR, 26.6; 95% CI, 8.3–85.7; P<.001), imaging (OR, 2.6; 95% CI, 2.2–2.9; P<.001), RRSO (OR, 3.6; 95% CI, 2.8–4.8; P<.001), MRCP (OR, 2.4; 95% CI, 1.6–3.6; P<.001), EUS (OR, 7.2; 95% CI, 3.1–16.8; P<.001), and chemoprevention (OR, 1.6; 95% CI, 1.0–2.3; P=.028).
Forest plot depicting likelihood of uptake of risk-reducing strategies among cancer-free women. Women with variants of uncertain significance served as the reference group.
Abbreviations: EUS, endoscopic ultrasound; MRCP, magnetic resonance cholangiopancreatography; RRM, risk-reducing mastectomy; RRSO, risk-reducing salpingo-oophorectomy.
Citation: Journal of the National Comprehensive Cancer Network 2025; 10.6004/jnccn.2025.7011
Costs
When evaluating costs for breast surgery, RRSO, targeted therapy use, and imaging within 12 months of GGT, no statistically significant differences were found between individuals with VUS and those with negative results (Table 2). In contrast, costs for breast surgery and RRSO were significantly higher for individuals with positive results compared with those with VUS or negative results. Among patients with breast cancer who underwent breast surgery after GGT, the estimated per-person per-year (PPPY) cost was nearly double for those with positive results ($19,229) compared with those with negative ($10,062) or VUS ($10,922) results. For women who had RRSO, the estimated PPPY was significantly higher for those with positive results ($2,231) versus negative ($208) or VUS ($248) results. Similarly, for cancer-free women, no significant cost differences were found between VUS and negative results. However, costs were significantly higher for those with positive results for each risk-reducing strategy. For cancer-free women who underwent breast surgery, the estimated PPPY cost for those with positive results ($532) was 10 times higher than for those with negative ($52) or VUS ($50) results. Similar cost gaps were observed for RRSO (positive: $633; negative: $143; VUS: $123) and imaging (positive: $667; negative: $207; VUS: $253).
Cost of Cancer Screening, Prevention, and Treatment Procedures 12 Months After Genetic Testing by Germline Status
| Subgroup Analysis | Negative | All VUS | Positive | P Value (Negative To VUS) |
P Value (Negative to Positive) |
P Value (Positive to VUS) | |
|---|---|---|---|---|---|---|---|
| Cancer screening, prevention, and treatment procedures in women with breast cancer | |||||||
| Total patients, N | 17,898 | 2,898 | 1,903 | ||||
| Breast surgical procedures, n (%) | 9,134 (51.0) | 1,469 (50.7) | 893 (46.9) | ||||
| Breast surgical cost | |||||||
| Adjusteda mean (SE) | $10,062 (2,369) | $10,922 (2,486) | $19,229 (3,175) | .754 | 1.0 × 10−4 | .002 | |
| 95% CI | 5,400 to 14,724 | 6,030 to 15,813 | 12,952 to 25,507 | ||||
| RRSO surgical procedures, n (%) | 16,180 (90.4) | 2,603 (89.8) | 1,666 (87.5) | ||||
| RRSO surgical cost | |||||||
| Adjusteda mean (SE) | $208 (156) | $248 (168) | $2,231 (572) | .880 | .001 | .001 | |
| 95% CI | −98 to 513 | −81 to 576 | 1,084 to 3,378 | ||||
| Targeted therapy procedures, n (%) | 14,513 (81.1) | 2,350 (81.1) | 1,579 (83.0) | ||||
| Targeted therapy cost | |||||||
| Adjusteda mean (SE) | $1,743 (1,170) | $1,108 (1,292) | $2,820 (1,482) | .623 | .473 | .291 | |
| 95% CI | −551 to 4,037 | −1,426 to 3,642 | −88 to 5,728 | ||||
| Imaging procedures, n (%) | 11,209 (62.6) | 1,754 (60.5) | 659 (34.6) | ||||
| Imaging cost | |||||||
| Adjusteda mean (SE) | $1,113 (260) | $1,044 (293) | $1,099 (356) | .913 | .980 | .998 | |
| 95% CI | 601 to 1,625 | 466 to 1,621 | 398 to 1,800 | ||||
| Cancer screening and prevention procedures in women without breast cancer | |||||||
| Total patients, N | 22,354 | 3,506 | 2,098 | ||||
| Breast surgical procedures, n (%) | 22,347 (>99.9) | 3,501 (99.8) | 2,091 (99.7) | ||||
| Breast surgical cost | |||||||
| Adjusteda mean (SE) | $52 (82) | $50 (88) | $532 (199) | .998 | .043 | .040 | |
| 95% CI | −107 to 212 | −122 to 222 | 137 to 928 | ||||
| RRSO surgical procedures, n (%) | 21,997 (98.4) | 3,449 (98.4) | 2,059 (98.1) | ||||
| RRSO surgical cost | |||||||
| Adjusteda mean (SE) | $143 (111) | $123 (118) | $633 (203) | .915 | .010 | .008 | |
| 95% CI | −75 to 361 | −109 to 355 | 231 to 1,035 | ||||
| Imaging procedures, n (%) | 22,322 (99.8) | 3,498 (99.8) | 2,040 (97.2) | ||||
| Imaging cost | |||||||
| Adjusteda mean (SE) | $207 (44) | $253 (48) | $667 (66) | .161 | <.001 | <.001 | |
| 95% CI | 122 to 393 | 159 to 346 | 536 to 797 | ||||
Bold indicates statistically significant P value.
Abbreviations: RRSO, risk-reducing salpingo-oophorectomy; VUS, variant of uncertain significance.
Adjusted for age, insurance type, and geographic region.
Discussion
VUS are common: among 1,122,445 individuals who underwent MGPT for hereditary cancer risk in a commercial laboratory between 2014 and 2022, 376,922 (33.6%) had ≥1 VUS.15 Misinterpretation of VUS by patients and/or clinicians may lead to guideline-discordant care, contributing to overtreatment and increased clinical resource consumption and costs.8–11 Reassuringly, however, in this large real-world cross-sectional study of approximately 50,000 women, VUS results did not lead to guideline-discordant treatment and prevention.
Germline-directed clinical recommendations include consideration of RRM, RRSO, breast MRI, PARPi, MRCP, and EUS for women with PVs in certain breast cancer predisposition genes.1 In this study of 22,699 women with newly diagnosed breast cancer and 27,958 cancer-free women, uptake of each of these interventions was significantly higher in patients with positive results compared with those with VUS or negative results. In contrast, uptake did not differ between women with VUS and those with negative results, except for slightly higher mammography usage, which was closer to but still less than guidelines recommendations, suggesting that VUS results do not drive substantially greater health care utilization. Byfield et al22 previously evaluated all-cause health care costs in cancer-free women who underwent genetic testing between 2014 and 2016, finding no statistically significant difference in costs between women with VUS and those with negative results. Consistent with this prior work, we found that in women with and without breast cancer, costs for breast surgeries, RRSO, and surveillance were no higher in those with VUS versus negative results and significantly lower in those with VUS versus positive results.
Although MRI uptake was not significantly higher among cancer-free women with VUS compared with those with negative results in this study, mammography uptake was slightly higher (and closer to expected rates). One study of women who underwent GGT for breast cancer predisposition found that those with VUS intended to increase cancer screening,24 whereas another study of cancer-free individuals undergoing GGT for cancer predisposition found that those who received inconclusive results, including VUS, became hypervigilant about cancer symptoms.25 These findings may explain the higher mammography rates observed in women with VUS. It is worth noting, however, that we were unable to capture screening recommendations based solely on family history. Therefore, the higher mammography rates in unaffected women with VUS may be attributable to a family history that warranted additional or more frequent screening. Mammography uptake in cancer-free women in this study (49.1% for those with positive results, 45.7% for VUS, and 43.4% for negative results) was slightly higher than in prior studies of cancer-free women (47% positive, 37.6% VUS) and studies including both cancer-diagnosed and cancer-free women (43% positive, 38.9% VUS).26,27
To our knowledge, this is the largest study of breast health care utilization among women undergoing GGT, including 4,686 women with VUS. However, this study has limitations. The absence of pathologic or tumor genomic data prevented us from distinguishing low-risk from high-risk patients or assessing whether breast tumor subtype influenced treatment.28 Additionally, the study was limited to 1 year after GGT, which may be insufficient to capture uptake of surveillance. Although VUS results may have led to slightly higher mammography use, they may have also encouraged women to adhere to recommended mammography screening sooner. A recent US study reported that screening mammography rates within the prior 2 years were 59.1% among women aged 40 to 49 years and 76.5% among those aged 50 to 74 years.29 With longer follow-up, mammography utilization among cancer-free women in this study may resemble these previously reported rates.
Conclusions
In a large real-world sample of women with and without breast cancer, the uptake of breast cancer treatment, surveillance, and risk-reducing strategies did not differ between those with VUS versus negative results. These findings offer reassurance that VUS do not promote overutilization or excess health care costs.
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