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. 2025 Aug 9;60:101922. doi: 10.1016/j.gore.2025.101922

Risk factors for delayed diagnosis of endometrial cancer among black individuals: Results from the GUIDE-EC study

Maya E Gross a,⁎, Mindy Pike a, Julianna Alson a, Patrice Williams a, Mollie E Wood b, Erica Marsh c, Erin Carey d, Til Stürmer b, Ronit Katz a, Whitney R Robinson e, Kemi M Doll a
PMCID: PMC12359182  PMID: 40831667

Highlights

  • •

    One fifth of Black patients with endometrial cancer undergoing hysterectomy experienced delayed diagnosis of EC.

  • •

    Delayed diagnosis of EC is defined by time to diagnosis of more than 28 days from initial presentation.

  • •

    Use of pelvic ultrasound during the evaluation was the variable most clearly associated with delayed diagnosis of EC.

  • •

    Patients under 50 years old or with class III obesity were at risk for experiencing delayed diagnosis of EC.

Keywords: Endometrial cancer, Racial disparities, Diagnosis, Transvaginal ultrasound, Healthcare quality

Abstract

Objective

Black patients with endometrial cancer (EC) experience disproportionately advanced stage at diagnosis. We aimed to identify variables, beyond race and histologic subtype, which increase risk for delayed diagnosis of EC.

Methods

This is a retrospective study of Black individuals with EC in a large academic-affiliated healthcare system from 2014 to 2020. Primary outcome was delayed diagnosis of EC, defined as prolonged time to diagnosis (>28 days to reach diagnosis). We used descriptive statistics, univariate regression, and factor analysis to identify variables associated with delayed diagnosis, achieve data reduction, and calculate odds ratios for delayed diagnosis.

Results

Of 388 patients with EC included for analysis, one fifth (n = 79, 20 %) experienced delayed diagnosis. Ultrasound had the strongest association with delayed diagnosis in univariate regression (OR 4.4, 95 % CI 2.4, 7.8) and factor analysis (OR 2.2, 95 % CI 1.6, 3.0). BMI ≥ 40 (OR 1.9, 95 % CI 1.1, 3.3) was also associated with delayed diagnosis. Age ≥ 50 was associated with decreased odds of delayed diagnosis (OR 0.3, 95 % CI 0.2, 0.7). Presence of an endometrial biopsy was associated with decreased odds of delayed diagnosis on univariate regression (OR 0.4, 95 % CI 0.2, 1.4).

Conclusions

A fifth of Black patients with EC experienced delayed diagnosis, and preoperative ultrasound was most strongly associated with delayed diagnosis. Providers should consider a tissue-sampling-first approach in Black patients at risk for EC.

1. Introduction

Endometrial cancer (EC) incidence and mortality in the United States disproportionately affects Black patients. In 2023, Black patients had the highest mortality rate of any racial group, dying from EC at twice the rate of their White counterparts (Cancer Stat, 2023, NCI, n.d, Clarke et al., 2022, DeSantis et al., 2019, Cote et al., 2015). Black patients have worse disease-specific survival for all subtypes of EC at all stages of disease, regardless of socioeconomic status, even in those receiving guideline-concordant care (Cote et al., 2015, Karia et al., 2023, Romano and Doll, 2020, Doll et al., 2017, Doll et al., 2021, Gamble et al., 2024). While this disparity is multifactorial, advanced stage at diagnosis—affected by tumor behavior, access to care, and timely diagnosis—is one modifiable factor within our healthcare landscape (Cote et al., 2015, Doll et al., 2018, Huang et al., 2020, Doll et al., 2018, Doll et al., 2020).

Guidelines for the diagnosis of EC endorse transvaginal ultrasound as an appropriate first step in postmenopausal patients, with the advantage of avoiding invasive procedures such as endometrial biopsy (EMB) or dilation and curettage (D&C) (Williams and Endometrial, 2020, Committee on Practice Bulletins - Gynecology and the Society of Gynecologic Oncology. ACOG Practice Bulletin: Endometrial Cancer. Published online April, 2017, The American College of Obstetricians and Gynecologists, 2018). The American College of Obstetricians and Gynecologists (ACOG) recommends that ultrasound should be reserved for patients with a low likelihood of requiring additional testing (The American College of Obstetricians and Gynecologists, 2018, Committee on practice bulletins - Gynecology, 2016). A growing body of evidence suggests that ultrasound is an inadequate first diagnostic procedure among Black patients and patients at high risk for EC, due to decreased sensitivity (Williams and Endometrial, 2020, Committee on Practice Bulletins - Gynecology and the Society of Gynecologic Oncology. ACOG Practice Bulletin: Endometrial Cancer. Published online April, 2017, The American College of Obstetricians and Gynecologists, 2018, Committee on practice bulletins - Gynecology, 2016, Wang et al., 2006, Doll et al., 2024, Phillip et al., 2004, Clarke et al., 2020). Type 2 EC, which includes high grade endometrioid and all other histologic subtypes of EC, is also unreliably detected by ultrasound due to variable endometrial thickness (ET) (Wang et al., 2006, What Is Endometrial Cancer | Types of Endometrial Cancer | American Cancer Society. Accessed September 11, 2024, Naftalin et al., 2012). As histologic subtype is a pathologic diagnosis, this knowledge cannot be practically incorporated into diagnostic decision-making. Beyond race and histology, little is known regarding which variables may increase the risk of suboptimal diagnosis of endometrial cancer.

The Guidelines for Transvaginal Ultrasound in the Detection of Early Endometrial Cancer (GUIDE-EC) project was developed to investigate diagnostic accuracy of ultrasound for Black patients. Initial results of the project demonstrated an 11 % false negative (FN) rate of ultrasound detection in this population (Doll et al., 2024). In this analysis restricted to patients from GUIDE-EC with EC, the aim is to investigate risk factors for experiencing delayed diagnosis of EC and describe patterns of care by which Black patients with EC may have delayed diagnoses. Our goal is to determine whether a subset of patients can be identified who are at elevated risk of experiencing this suboptimal pathway to diagnosis.

2. Materials and methods

For the GUIDE-EC study, we utilized a searchable federation of electronic health information and administrative data from a large academic health system, including data from 10 hospitals and hundreds of affiliated practices (Doll et al., 2024). Professional abstractors queried the database for structured clinical data and supplemented this with free text and imaging reports from the Electronic Health Record (EHR). Clinical, sociodemographic and diagnostic process information were collected. This study was determined to have no more than minimal risk by our Institutional Review Board human subjects division (Study #: 21–0289). The multi-step abstraction process, inclusion and exclusion decisions have been reported in detail in the methods and supplementary materials of prior work (Doll et al., 2024).

Patients eligible for GUIDE-EC were over 18 years of age, identified as Black or African American and underwent hysterectomy between April 4, 2014, and December 31, 2020. Individuals were excluded if they were pregnant at time of surgery, had breast cancer diagnoses, and/or had cancers where vaginal bleeding would be non-uterine in origin (eg; cervical, placental site). The analysis for this publication was restricted to those patients with a final diagnosis of EC. Diagnostic procedure information included abstraction of pre-diagnosis procedures performed during our abstraction window, including ultrasound, endometrial biopsy, and D&C. Ultrasounds in which an ET was documented were included, as these ultrasounds indicate quality ultrasounds for screening for endometrial cancer. Endometrial thickness from these ultrasounds was abstracted and reported. Diagnosis of EC was identified from office biopsy or D&C pathology documenting endometrial carcinoma of any subtype, reported diagnosis of EC in the record, and/or ICD code C54.1 appearing in the 24 months prior to hysterectomy. Records were additionally excluded if hysterectomy occurred before or within three months after the EPIC go-live date (launch date of the current EHR) at the surgical site, ensuring ability to capture preoperative events.

Demographic data included date of birth, age, employment status, and insurance type at the time of hysterectomy. Clinical variables included height and weight, surgery date, surgical facility, diagnosis of all malignant neoplasms of the pelvis at the time of hysterectomy, and specified physician- and hospital-billed diagnostic and Common Procedural Terminology (CPT) codes within 24 months prior to hysterectomy. We performed detailed abstraction in the 30 days prior to the initial identified procedure, to identify any symptoms triggering evaluation or previously documented diagnoses (Doll et al., 2024).

Additional clinical history information including reported symptoms, previous diagnoses, sequelae of clinical signs and symptoms, family history, smoking history, mental health diagnoses were abstracted and included in the analysis. Bleeding was classified as postmenopausal if there was specific documentation of postmenopausal bleeding in the chart, regardless of age, and other bleeding if not. Charlson Comorbidity Index (CCI) was calculated for all patients (Charlson et al., 1987). Symptoms were documented as present, reported absent, or missing from the record.

2.1. Variable definitions

We defined delayed diagnosis as time from initial presentation to diagnosis > 28 days, reflecting the diagnostic interval of the Andersen model of Total Patient Delay (Walter et al., 2012, Andersen and Cacioppo, 1995). Date of initial presentation was identified using manual abstraction for presenting symptoms or diagnoses documented in the medical record during the 30 day period surrounding the initial diagnostic procedure. While prolonged time to reach surgery in endometrial cancer has been previously defined, an accepted definition of diagnostic delay for endometrial cancer does not exist (Strohl et al., 2016, Alhilli, n.d). In our sample, a time from presentation to diagnosis > 23 days defined the upper quartile of patients; utilizing this as a guide, prolonged time to diagnosis was defined as > 28 days or one month of waiting for a cancer diagnosis after presenting with symptoms, which was felt to be clinically relevant. Same day diagnosis was documented for all patients with a diagnosis on the day of first documented procedure or in the 30-day abstraction period prior to first documented procedure. Numerically, this was documented as time to diagnosis of 0 days (indicating diagnosis present on or before date of first symptom/procedure capture). An additional 4 people were excluded from the analysis due to diagnosis documentation outside of the abstraction window.

We described procedure pathways for patients prior to diagnosis as EC. Procedure pathways were categorized into two appropriate categories vs a “multiple procedures” category. Pathways included efficient guideline-concordant pathways, where either ultrasound (ultrasound to tissue) or tissue sampling (EMB or D&C) was performed first, or a multiple procedures pathway in which patients received multiple ultrasounds or multiple EMB or multiple D&C procedures prior to reaching a pathologic diagnosis of EC. Patients who received a single ultrasound followed by a single biopsy prior to diagnosis, for example, were not considered to have undergone multiple procedures.

2.2. Statistical analysis

Descriptive statistics, including counts and proportions for categorical data and mean, standard deviation (SD) or median, interquartile range (IQR) for continuous variables, were used to generate summary tables of demographic and clinical data. The proportion of patients with each clinical sign/symptom was described, stratified by experience of delayed diagnosis. Univariate logistic regression models were performed to analyze associations between risk factors and delayed diagnosis.

To identify underlying patterns across the many clinical signs and symptoms, we conducted a factor analysis, a statistical method used to reduce a large number of related variables into a smaller set of underlying constructs, called factors. These factors represent clusters of symptoms or characteristics that tend to occur together and explain a shared variability in the data. For example, symptoms related to abnormal bleeding may group together into one factor, while imaging or procedural variables may form another. By summarizing information this way, we aimed to more efficiently capture complex patterns in symptom presentation.

We used principal components analysis and parallel analysis to determine the number of factors to retain (America, 2001, Bleicher et al., 2016). Five distinct factors were found to be appropriate for the data. Factor analysis with principal components factors estimation was used and different rotations were examined to determine best fit according to Thurstone’s rules (Ashing-Giwa et al., 2010). We ran a secondary factor analysis containing only variables significantly associated with our identified factors (loading > 0.5) to create standardized regression scores for each factor using the regression scoring method. Logistic regression then evaluated associations between factor scores and prolonged time to diagnosis. Factor scores were continuous and linear predictors. Model 1 adjusted for each score separately. Model 2 additionally adjusted for age. Model 3 additionally adjusted for the CCI.

In a sensitivity analysis, we excluded participants with a previous diagnosis of hyperplasia and re-examined univariate associations between risk factors and delayed diagnosis as well as the associations between factor scores and delayed diagnosis in this group. The rationale for this sensitivity analysis being that for patients with endometrial hyperplasia who do no elect or are not recommended for definitive management with hysterectomy, serial tissue sampling and/or ultrasounds are part of the surveillance. Patients initially diagnosed with hyperplasia but ultimately diagnosed with cancer may experience an inherently longer time to diagnosis from first diagnostic procedure, given that there was no cancer present at the time of initial evaluation.

We suppressed cells with low frequency counts (under 5 participants) to comply with our IRB procedures by collapsing categories and coarsening data where applicable. Statistical analyses were conducted using Stata Version 18.0 (StataCorp LLC, College Station, TX) (Walming, n.d).

3. Results

A total of 388 patients with EC were included from the GUIDE-EC sample for this analysis. Patient characteristics are summarized in Table 1. Mean age was 64 (SD 10) years and mean BMI was 36 (SD 9) kg/m2. Most patients (n = 213, 55 %) used Medicare insurance. The majority of patients had a CCI ≥ 1 (51 %), history of fibroids (59 %) and/or postmenopausal bleeding (77 %). Patients were otherwise highly symptomatic with pelvic/abdominal pain (38 %), fatigue/dizziness (24 %), and menopausal symptoms (49 %). A minority of patients (17 %) had a prior diagnosis of endometrial hyperplasia.

Table 1.

Demographics and clinical characteristics of patients with endometrial cancer in the GUIDE-EC Study, overall and by experience delayed diagnosis of EC.

Not Prolonged Prolonged Total
N 309 (79.6 %) 79 (20.4 %) 388 (100.0 %)
Time to diagnosis, days (median, IQR) 0 [0, 5] 65 [38, 122] 0 [0, 22]
Range 0––28 29–––530 0–––530
Age at hysterectomy, years 64.7 (9.6) 62.9 (12.9) 64.4 (10.4)
Range 29.1–––89.8 27.4–––86.1 27.4–––89.8
Body Mass Index 36.1 (9.2) 37.4 (9.4) 36.4 (9.2)
BMI ≥ 40 64 (27.0 %) 27 (40.9 %) 91 (30.0 %)
Fibroids 179 (57.9 %) 51 (64.6 %) 230 (59.3 %)
Postmenopausal bleeding 245 (79.3 %) 54 (68.4 %) 299 (77.1 %)
Other Bleeding 145 (46.9 %) 41 (51.9 %) 186 (47.9 %)
Pelvic/abdominal pain 117 (37.9 %) 32 (40.5 %) 149 (38.4 %)
Enlarged uterus 123 (39.8 %) 24 (30.4 %) 147 (37.9 %)
Ovarian cyst/pelvic mass 98 (31.7 %) 21 (26.6 %) 119 (30.7 %)
Fatigue, dizziness 73 (23.6 %) 19 (24.1 %) 92 (23.7 %)
Abnormal Pap 88 (28.5 %) 14 (17.7 %) 102 (26.3 %)
Urinary symptoms 72 (23.3 %) 25 (31.6 %) 97 (25.0 %)
Menopausal Symptoms 154 (49.8 %) 37 (46.8 %) 191 (49.2 %)
Endometrial Hyperplasia 48 (15.5 %) 18 (22.8 %) 66 (17.0 %)
Anemia 93 (30.1 %) 21 (26.6 %) 114 (29.4 %)
Period longer than 7 days 25 (8.1 %) 6 (7.6 %) 31 (8.0 %)
Disability 8 (2.6 %) 6 (7.6 %) 14 (3.6 %)
Employed 82 (26.5 %) 19 (24.1 %) 101 (26.0 %)
Smoking, current 32 (10.4 %) 7 (8.9 %) 39 (10.1 %)
Blood transfusion 26 (8.4 %) 8 (10.1 %) 34 (8.8 %)
Family history of breast cancer 60 (19.4 %) 14 (17.7 %) 74 (19.1 %)
Family history of gynecologic cancer 26 (8.4 %) 12 (15.2 %) 38 (9.8 %)
Depression 29 (9.4 %) 10 (12.7 %) 39 (10.1 %)
Anxiety 40 (12.9 %) 16 (20.3 %) 56 (14.4 %)
Charlson Comorbidity Index
0 155 (50.2 %) 36 (45.6 %) 191 (49.2 %)
1 81 (26.2 %) 18 (22.8 %) 99 (25.5 %)
2 or more 73 (23.6 %) 25 (31.6 %) 98 (25.3 %)
Insurance
Private insurance 101 (32.7 %) 28 (35.4 %) 129 (33.2 %)
Medicare 170 (55.0 %) 43 (54.4 %) 213 (54.9 %)
Medicaid 19 (6.1 %) 5 (6.3 %) 24 (6.2 %)
Self-pay 16 (5.2 %) 2 (2.5 %) 18 (4.6 %)
Other 3 (1 %) 1 (1 %) 4 (1 %)
US fibroids 125 (60.7 %) 48 (63.2 %) 173 (61.3 %)
Number of fibroids 1.8 (1.3) 1.9 (1.2) 1.8 (1.2)
Biopsy present 260 (84.1 %) 55 (69.6 %) 315 (81.2 %)
D&C present 61 (19.7 %) 36 (45.6 %) 97 (25.0 %)
Hysterectomy location
UNC 262 (84.8 %) 52 (65.8 %) 314 (80.9 %)
UNC Community 47 (15.2 %) 27 (34.2 %) 74 (19.1 %)
ET measurement available 141 (45.6 %) 62 (78.5 %) 203 (52.3 %)
Endometrial thickness (median, range) 15 [1.7–––62] 17.9 [2––111] 16 [1.7–––111]
<3 4 (2.8 %) 3 (4.8 %) 7 (3.5 %)
≥3 to < 4 10 (7.1 %) 2 (3.2 %) 12 (5.9 %)
≥4 to < 5 2 (1.4 %) 2 (3.2 %) 4 (2.0 %)
≥5 125 (88.7 %) 55 (88.7 %) 180 (88.7 %)
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*Note: Values listed as N (%) unless otherwise stated. Delayed Diagnosis is defined as time from symptom presentation to EC diagnosis greater than 4 weeks (28 days), or no preoperative diagnosis of cancer. Family history of gynecologic cancer defined as family history of ovarian, uterine, or cervical cancer; endometriosis, missed days of work, bipolar disorder, and PTSD have less than 5 participants in each group and are not shown.

*Time to diagnosis of 0 days reflects a diagnosis made on or before the date of initial documented procedure, representing same day diagnosis.

3.1. Delayed diagnosis

A total of 79 patients (20 %) experienced delayed diagnosis of EC. In this group, the median time to diagnosis was 65 days, with a quarter of patients waiting more than 122 days to obtain a diagnosis of cancer. This group waited a median of two months longer than the group not experiencing delayed diagnosis (in whom the median diagnosis was same day as initial presentation).

Half of people in the sample experienced same-day diagnosis of cancer (median 0 days, IQR 0, 23), reflecting a diagnosis made on or before the initial day of data abstraction (Table 1). Variables associated with a decreased odds of delayed diagnosis on univariate regression included age ≥ 50 years, presence of postmenopausal bleeding and menopausal symptoms, enlarged uterus, history of abnormal pap smear, presence of anemia, and having an office biopsy performed (Table 2). Variables associated with increased odds of delayed diagnosis included BMI ≥ 40 kg/m2, having a disability, and presence of an available ET measurement (i.e., a quality ultrasound). We analyzed procedure pathway (ultrasound to tissue, tissue-first, or multiple procedures prior to diagnosis) as a potential risk factor for delayed diagnosis of EC. Compared to an imaging first pathway, undergoing a tissue first pathway was associated with a significantly decreased odds of experiencing delayed diagnosis of EC (OR 0.16, 95 % CI 0.1, 0.4). Undergoing multiple procedures prior to diagnosis was associated with a four-fold increased odds of delayed diagnosis compared to an imaging to tissue pathway. Undergoing multiple procedures and having an available ET measurement had the strongest associations with prolonged time to diagnosis, with odds ratios of 3.69 (95 % CI 1.6, 8.6) and 4.35 (95 % CI 2.4, 7.8), respectively. Measured endometrial thickness itself was not associated with time to diagnosis.

Table 2.

Univariate associations between risk factors and delayed diagnosis (N = 388).

OR (95 % CI) p-value
Age ≥ 50 years 0.33 (0.16, 0.71) 0.004
BMI ≥ 40 1.87 (1.06, 3.30) 0.031
History of Fibroids 0.69 (0.27, 1.76) 0.439
Endometriosis NA
Postmenopausal bleeding 0.35 (0.19, 0.64) 0.001
Other bleeding 0.84 (0.50, 1.41) 0.512
Pelvic/abdominal pain 1.49 (0.83, 2.65) 0.178
Enlarged uterus 0.55 (0.32, 0.94) 0.029
Ovarian cyst/pelvic mass 0.74 (0.42, 1.29) 0.286
Fatigue, dizziness 0.77 (0.43, 1.38) 0.376
Abnormal Pap 0.46 (0.25, 0.88) 0.018
Urinary symptoms 0.88 (0.43, 1.78) 0.727
Menopausal Symptoms 0.54 (0.32, 0.92) 0.023
Endometrial Hyperplasia 1.00 (0.54, 1.87) 0.994
Anemia 0.56 (0.32, 0.99) 0.049
Period longer than 7 days 0.58 (0.15, 2.27) 0.431
Disability 3.09 (1.04, 9.19) 0.042
Blood transfusion 1.22 (0.53, 2.82) 0.631
Family history of breast cancer 0.89 (0.47, 1.70) 0.732
Family history of gynecologic cancer 1.95 (0.94, 4.06) 0.075
US reported fibroids 1.11 (0.65, 1.91) 0.705
Submucosal
No 1.0 (ref)
Yes 2.21 (0.77, 6.36) 0.143
Biopsy present 0.43 (0.24, 0.76) 0.004
Biopsy or D&C present 0.58 (0.24, 1.38) 0.220
Procedure pathway
Imaging to tissue 1.0 (ref)
Tissue first 0.16 (0.07, 0.35) <0.001
Multiple procedures 3.69 (1.58, 8.58) 0.002
Endometrium visibility
No info 0.53 (0.22, 1.27) 0.153
Not visible 0.66 (0.21, 2.07) 0.479
Partially visible 1.85 (0.87, 3.94) 0.109
Visible 1.0 (ref)
ET measurement available 4.35 (2.43, 7.77) <0.001
Endometrial thickness, mm 1.02 (0.99, 1.04) 0.155
Notes: No is the reference category for each clinical sign/symptom
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Sociodemographic variables including missing days of work, employment and insurance status, smoking status were not significantly associated with prolonged time to diagnosis. Additionally, medical history components not related to gynecologic health, including mental health disorders including depression, bipolar disorder, anxiety, PTSD, and the burden of comorbidities measured by the Charlson comorbidity index were not significantly associated with prolonged time to diagnosis (Supplemental Table 6).

3.2. Factor analysis for delayed diagnosis of EC

In the factor analysis of clinical signs and symptoms, five unique factors were found to describe most of the variability in the data (Supplemental Tables 1, 2). Factor one described the majority of the variance in endometrial visibility and ET measurement availability, and was interpreted as a proxy measurement for having had an ultrasound done where the ET was measured (i.e. a quality ultrasound). Factor two described most of the variance in menopausal symptoms and abnormal bleeding (including postmenopausal bleeding and other bleeding), and thus captured patients with these symptoms. Factor 3 described variance in history of fibroids and presence of fibroids on ultrasound, serving as a proxy identification of patients with fibroids. Factor 4 described the variance in anemia and history of transfusion. Variance in having a biopsy prior to hysterectomy and presence of pelvic mass were described by factor five (Fig. 1).

Fig. 1.

Fig. 1

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Five identified factors explaining variability in the data, with significant contributing variables for each factor* * Significant contributing variables represent variables significantly correlated with our identified factor (with loading > 0.5.).

Factor scores were modeled in logistic regression with experience of delayed diagnosis of EC (Table 3). Factor 1, presence of ultrasound, was associated with an increased odds for delayed diagnosis (OR 2.2, 95 % CI 1.6, 3.0). This association persisted in models adjusting for age and medical comorbidities. Other factors were not significantly associated with delayed diagnosis.

Table 3.

Association of factor scores with delayed diagnosis (n = 388).

Model 1 Model 2 Model 3
each factor separately adjusted for age adjusted for age + comorbidities
OR (95 % CI) OR (95 % CI) OR (95 % CI)
Factor 1 2.18 (1.61, 2.95) 2.13 (1.58, 2.89) 2.14 (1.58, 2.89)
Factor 2 0.86 (0.67, 1.10) 1.01 (0.76, 1.35) 1.01 (0.76, 1.35)
Factor 3 1.22 (0.95, 1.57) 1.26 (0.97, 1.63) 1.29 (0.99, 1.67)
Factor 4 0.97 (0.75, 1.24) 0.83 (0.62, 1.18) 0.82 (0.62, 1.10)
Factor 5 0.81 (0.64, 1.03) 0.86 (0.67, 1.10) 0.87 (0.68, 1.11)
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Factor 1: Endometrium visibility and available endometrial thickness measurement (presence of a quality ultrasound).

Factor 2: Menopausal symptoms and abnormal bleeding (including postmenopausal bleeding and other bleeding).

Factor 3: History of fibroids, fibroids on ultrasound.

Factor 4: Anemia and history of transfusion

Factor 5: Biopsy prior to hysterectomy, presence of pelvic mass.

When excluding patients with a diagnosis of endometrial hyperplasia prior to hysterectomy, there were a total of 61 patients with delayed diagnosis of EC (Supplemental Table 3). Time to diagnosis did not differ significantly. BMI ≥ 40 and age > 50 were no longer significantly associated with prolonged time to diagnosis. Other risk factors were similar to the overall sample, including a strong association between diagnosis pathway and time to diagnosis. Those undergoing a tissue-sampling first pathway has a decreased odds of experiencing prolonged time to diagnosis (OR 0.1, 95 % CI 0.04, 0.3) compared to those with an imaging to tissue pathway, and those undergoing multiple procedures had an increased odds of prolonged time to diagnosis (OR 5.8, 95 % CI 2.2, 15.6). When excluding those with hyperplasia from factor analysis, Factor 1 (endometrial visibility and endometrial measurement) remained associated with delayed diagnoses, and factor 5 (pre-hysterectomy biopsy, presence of a pelvic mass) were newly associated with decreased odds of delayed diagnosis (Supplemental Table 5).

4. Discussion

One fifth of Black endometrial cancer patients in the GUIDE-EC study experienced delayed diagnosis of EC. Diagnostic pathway was an important modifiable influence for delayed diagnosis of EC. Use of ultrasound in the evaluation of endometrial cancer had the strongest association with delayed diagnosis of EC, regardless of endometrial thickness, and those undergoing a diagnostic pathway of imaging followed by tissue sampling were at increased odds of experiencing diagnostic delays. Patients undergoing a tissue-sampling first pathway had the lowest odds of delayed diagnosis. Unsurprisingly, those requiring multiple procedures prior to diagnosis had the highest odds of delayed diagnosis of EC. We additionally identified nonmodifiable (or not easily modifiable) factors which increased the odds of delayed diagnosis of EC, namely age < 50 and BMI ≥ 40 kg/m2. Our study emphasizes that while several paths to diagnosis may represent guideline-concordant care from diligent providers, an ultrasound-first pathway introduces opportunities for prolonging time to diagnosis, by delaying the performance of a definitive diagnostic procedure.

Delays in cancer diagnosis contribute to total patient delay, as described by Andersen et al, and hinder timely access to care—one of the six pillars of quality healthcare delivery (Walter et al., 2012, Andersen and Cacioppo, 1995, Doll et al., 2022, America, 2001). Our study demonstrates that ultrasound is contributing to Total Patient Delay in receiving cancer-directed care (Naftalin et al., 2012, Charlson et al., 1987). The most common way in which patients experienced delayed diagnosis was via a prolonged time from symptom presentation to diagnosis, defined in our study as a wait longer than 28 days. Multiple studies of quality care have demonstrated negative impacts of prolonged time to reach cancer-directed care, including decreased survival and decreased quality of life (Ashing-Giwa et al., 2010, Bleicher et al., 2016, Hanna et al., 2020, Mullins et al., 2019, Strohl et al., 2016, Walming, n.d). Prolonged time to diagnosis after presentation of symptoms may additionally interact reciprocally with medical distrust, contributing to decreased rates of treatment completion in vulnerable populations (Mullins et al., 2019).

Our work echoes multiple studies which have demonstrated that ultrasound is ineffective as a triage strategy in Black patients and other groups, if the goal is cessation of evaluation after visualizing a thin endometrium (Romano and Doll, 2020, Wang et al., 2006, Doll et al., 2024, Phillip et al., 2004, Clarke et al., 2020, Naftalin et al., 2012, Wagar et al., 2025). Considering an ET measurement as a surrogate marker for a previous quality ultrasound –one in which ET is visible and measured—it is disturbing that the performance of a quality ultrasound did not protect against delayed diagnosis of EC. While in some patients, ultrasound may be an effective triage strategy, it is challenging to determine when and in which patients one can rely on the results specifically regarding EC diagnosis. This uncertainty was highlighted in a qualitative study of provider practice patterns in evaluating peri- and post-menopausal bleeding; while ultrasound access was felt to be easier to facilitate, providers stated that even with a “reassuring” ultrasound with a thin endometrial echo, many performed or referred all patients for EMB (Gross et al., 2025). Providers often perform this test despite a lack of reliance on the results, adding an additional procedure in the path to endometrial cancer diagnosis.

Ultrasound is considered a noninvasive procedure which may decrease the need for EMB, however in this sample of Black patients with EC, ultrasound contributed to delayed diagnosis of EC by multiple pathways.

In addition to race and histology, we identified other subgroups of patients at risk for delayed diagnosis based on current recommendations. In our sample, younger patients (age < 50 years) and patients with class III obesity were at risk for delayed diagnosis; previous studies have emphasized the challenge of accurate diagnosis in these groups, in whom rates of endometrial cancer are low but are rising (Clarke et al., 2020, Wise et al., 2016, Soliman et al., 2005, Guo et al., 2021). Young patients and those with class III obesity represent groups in whom obtaining reliable results from diagnostic procedures (including in-office EMB and transvaginal ultrasound) may be more challenging, and in whom the guidelines for tissue sampling are inconsistent (Romano and Doll, 2020, Committee on practice bulletins - Gynecology, 2016, Cusimano et al., 2019). It is disconcerting that patients with class III obesity (a known risk factor for EC) and young patients (in whom rates of EC are rising) are at risk for delayed diagnosis (Wise et al., 2016, Soliman et al., 2005, Guo et al., 2021). Our findings stress the importance of timely tissue sampling in all patients with symptoms suggestive of endometrial cancer, regardless of medical characteristics or comorbidities.

4.1. Strengths and Limitations

A large patient sample and detailed information on presentation, evaluation and diagnosis, not previously available for Black patients, are strengths of the study. Employing detailed data abstracted from multiple hospitals and clinics allowed us to follow patients receiving care in multiple locations over time.

While historically, the majority of research in gynecologic oncology has been conducted in homogeneous populations of mostly White patients, some have suggested that a study population of all-Black patients is a limitation of this work. We advocate that this is actually a pinnacle strength of our study. Black patients have been underrepresented in the majority of research informing guidelines for diagnosis and care of endometrial cancer, and a growing body of work is highlighting, repeatedly, the missed opportunities of delivering equitable cancer care that have resulted from this approach (Cote et al., 2015, Doll et al., 2017, Doll et al., 2024, Phillip et al., 2004, Dubil et al., 2018). This study aims to fill existing gaps in knowledge of endometrial cancer in this group of patients who experience the highest rates of EC mortality (Doll et al., 2021, Phillip et al., 2004, Robinson and Krasnik, 2011, Long et al., 2020).

Limitations of the study exist. Histologic data is not available from the dataset and thus conclusions regarding influence of histologic subtype are unable to be made. All patients included in the GUIDE-EC study underwent hysterectomy, and so there is potential for selection bias with the exclusion of patients who did not undergo hysterectomy for their endometrial cancer. We do not have data on provider specialty for the initial evaluation, so are unable to draw conclusions about how provider specialty or practice setting may influence time to diagnosis. This study identified factors which increased risk of delayed diagnosis among Black patients with EC in the U.S. Future research should focus on validating these initial findings in this population, as well as replicating this investigation in other patient populations of different races and ethnicities, as differences in risk factors may exist between populations.

5. Conclusions

In this sample of Black patients with EC, one fifth of patients experienced delayed diagnosis of cancer. Having an ultrasound done at any point prior to diagnosis was the variable most strongly associated with delays in diagnosis, via delaying definitive tissue sampling required for diagnosis. In Black patients, we recommend keeping EC on the differential diagnosis even in the presence of known benign gynecologic conditions. With this study adding to growing data regarding inadequacy of ultrasound in the evaluation of patients at risk for endometrial cancer, we recommend that providers consider a tissue-sampling-first approach, avoiding potential delays due to false reassurance and challenges with ultrasound access and coordinating follow-up for abnormal results. In the presence of concerning signs and symptoms, ideally, and if feasible based on patient factors, tissue biopsy should occur at the initial visit.

6. Funding Statement

This work was funded by Kuni Discovery Grants for Cancer Research: Advancing Innovation. The sponsor had no role in study design, collection, analysis and interpretation of data, writing of the report, nor the decision to submit the report for publication. This work is supported by the NIH Ruth L. Kirschstein National Research Service Award, T32CA0009515. Dr. Robinson was also funded from NIH R01 MD011680.

CRediT authorship contribution statement

Maya E. Gross: Writing – review & editing, Writing – original draft, Visualization, Investigation. Mindy Pike: Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis. Julianna Alson: Writing – review & editing, Methodology, Funding acquisition, Data curation. Patrice Williams: Writing – review & editing, Resources, Project administration. Mollie E. Wood: Writing – review & editing, Supervision, Methodology, Conceptualization. Erica Marsh: Writing – review & editing, Investigation, Conceptualization. Erin Carey: Writing – review & editing, Methodology, Investigation, Conceptualization. Til Stürmer: Writing – review & editing, Methodology, Formal analysis. Ronit Katz: Writing – review & editing, Writing – original draft, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. Whitney R. Robinson: Writing – review & editing, Supervision, Methodology, Investigation, Conceptualization. Kemi M. Doll: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gore.2025.101922.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (50.5KB, docx)

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