Abstract
Objective
We evaluated the association between the diagnosis–surgery interval on survival in early-stage cervical cancer (CC) and developed a nomogram for survival prediction.
Methods
We analyzed Surveillance, Epidemiology, and End Results data (2000–2019) of 7,827 patients with stage I–II CC who underwent radical or extended hysterectomy as primary treatment. Associations among the diagnosis-surgery interval, prognostic factors, overall survival (OS), and cancer-specific survival (CSS) were examined. A nomogram constructed using a multivariate Cox model was assessed using the concordance index (C-index) and a calibration curve.
Results
Most patients had stage I (95.15%) squamous cell carcinoma (62.21%), tumor size ≤2 cm (39.86%), without lymph node (LN) involvement (86.07%). Adjuvant radiation and chemotherapy were administered to 28.03% and 18.72% of the patients, respectively. The median diagnosis-surgery interval was 1 month (range: 0–17 months). Older age, non-Hispanic black race, higher stage, higher grade, larger tumor size, LN involvement, adjuvant radiation, and surgical delay (>1 month) were associated with worse OS and CSS (p=0.009 and p=0.022, respectively). Adenosquamous carcinoma was associated with poor CSS. The nomogram predicted survival, with areas under the curve of 76.07% and 80.19% for 5-year OS and CSS, respectively. Calibration curves and C-index values (0.735 for OS and 0.777 for CSS) confirmed the nomogram’s accuracy in predicting 3-, 5-, and 10-year OS and CSS in early-stage CC.
Conclusion
Surgical delays of >1 month were associated with worse survival in early-stage CC. The developed nomogram incorporates multiple prognostic factors to enhance personalized risk assessment and survival prediction.
Keywords: Uterine Cervical Neoplasms, Hysterectomy, Time-to-Treatment, Prognosis, Nomograms
Synopsis
Patients with early-stage cervical cancer undergoing radical or extended hysterectomy with surgical delays greater than 1 month have worse survival outcomes. A nomogram incorporating nine risk factors, including surgical delay, was developed to predict 3-, 5-, and 10-year survival, enhancing individualized risk assessment.
INTRODUCTION
Cervical cancer (CC) remains a major global health concern owing to its persistently high incidence and cancer-related mortality rates despite clear evidence that CC screening programs and human papilloma virus (HPV) vaccination have significantly reduced its prevalence [1,2,3]. Comprehensive CC screening methods, including HPV, Papanicolaou testing, and visual inspection with acetic acid, play crucial roles in the early detection of CC [3,4,5]. The mainstay treatments for early-stage CC include radical hysterectomy and pelvic lymphadenectomy [3,6,7].
Delayed treatment of localized solid tumors, such as breast and lung cancers, has been associated with an increase the risk of progression to advanced or metastatic disease. These delays frequently reflect limited access to healthcare and substandard care [3,8,9,10]. A prospective cohort study of 178 patients with stages IIB–IVA CC found that starting radiotherapy 120–179 days post-diagnosis was associated with higher odds of tumor progression, compared with treatment within 60 days (adjusted odds ratio [aOR]=3.30). Similarly, initiating radiotherapy at ≥180 days was associated with an increased risk (aOR=3.06) [11]. Matsuo et al. [12] studied 12,603 patients with stages IB–IIA CC and showed that delayed surgery was associated with an increased risk of pathological parametrial infiltration, especially within 16 weeks of delay. In patients with early-stage CC undergoing surgical treatment, the 5-year overall survival (OS) rate was significantly higher before metastasis but declined in the presence of parametrial or lymph node (LN) involvement [6,7]. Patients with early-stage CC with pathologic parametrial involvement had a lower 5-year OS rate (85.19%) than did those without pathologic parametrial involvement (94.23%) [7]. Therefore, early diagnosis and timely treatment are essential to control CC progression.
In 2020, amid the coronavirus disease pandemic, experts recommended that patients with early-stage CC, which is typically treated with radical trachelectomy or hysterectomy, consider delaying surgery by 6–8 weeks [13]. However, evidence regarding how the time from diagnosis to surgery is associated with survival outcomes remains limited, with studies reporting inconsistent results [3,8,12,14,15,16]. The interpretation of the available data is constrained by small sample sizes, variability in defining surgical delay, and differences in tumor stages and treatment approaches [3,8,12,14,15,16,17,18]. Furthermore, individualized survival predictions based on clinicopathological factors, including the diagnosis-surgery interval, have not been explored in patients with early-stage CC. Thus, an evaluation of the association between this interval on survival and the development of personalized survival predictions using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database was considered essential.
MATERIALS AND METHODS
1. Data collection
The study was reviewed and approved by the Institutional Ethics and Research Committee, Faculty of Medicine, Prince of Songkla University (code REC 67-398-38-1), which waived the requirement for written informed consent because of the retrospective nature of the study. After obtaining ethics committee approval, CC data were extracted from the SEER Population-Total U.S. database. Using SEER*Stat software version 8.4.3 (National Cancer Institute, Rockville, MD, USA), cases were extracted from Research Plus Data, which comprises 17 registries, covering the period 2000–2019, as submitted in November 2021.
The inclusion criteria were: age of 18–80 years; diagnosis confirmed by histology, including squamous carcinoma (International Classification of Diseases for Oncology, Third Edition [ICD-O-3] codes 8051–8052, 8070–8076, 8082–8083, 8120), adenocarcinoma (8140, 8144, 8263, 8310, 8380, 8480, 8482, 8490, 8441, 8574, 9110), or adenosquamous carcinoma (8015, 8560); having stage I–II CC and having undergone radical/extended hysterectomy as a primary treatment (surgical primary site 50–54, and treatment sequence as postoperative radiation or no/unknown radiation). The exclusion criteria were incomplete LN assessment, unknown TNM status, having more than one primary tumor, and having missing values in the records. Staging was determined by reevaluating the TNM staging system in compliance with the International Federation of Gynecology and Obstetrics (FIGO) 2009 recommendations. The workflow of this study is illustrated in Fig. 1.
Fig. 1. Patient selection flowchart.
FIGO, International Federation of Gynecology and Obstetrics; SEER, Surveillance, Epidemiology, and End Results.
The variables retrieved were age, ICD-O-3 code, race (Hispanic), race (Black, Caucasian, and others), marital status, median household income, months from diagnosis to treatment, stage, histology, grade, tumor size, LN involvement, radiation modality, chemotherapy administration, surgery and radiation sequence, survival month, survival month flag, cause-specific death classification, and other causes of death. Surgical delay comprised the period from diagnosis to surgery, defined as the “months from diagnosis to surgery” variable.
2. Statistical analysis
Clinicopathological characteristics were presented as medians and quartiles for continuous data, and unpaired t-tests were used for significance assessment. Categorical data were shown as percentages, and the χ2 test was used to assess associations and false discovery rate corrections while controlling for type I errors in multiple comparisons. Standardized residuals with absolute values >2 indicated significant deviations if the false discovery rate-adjusted p<0.05. Survival analysis was performed using Kaplan-Meier curves with log-rank tests for significance. Univariate Cox regression was used to assess variable significance, followed by multivariate analysis for factors with a p<0.2 to adjust for confounders. The results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs), and p<0.05 was set as the threshold for statistical significance. A nomogram was constructed using a multivariate Cox model, with a score representing the magnitude of the regression coefficients. The predictive performance of the model was assessed using the concordance index (C-index) and the area under the curve (AUC) of the receiver operating characteristic. Calibration plots were used to assess the ability of the nomogram to predict survival in patients with early-stage CC and to evaluate its discrimination ability and calibration proficiency. All statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria), utilizing the dplyr, ggplot2, rms, survival, survminer, chisq.posthoc.test, and timeROC packages.
RESULTS
From 67,109 patients with CC who were screened, 9,730 met the inclusion criteria. After excluding 505 patients with incomplete LN assessment (5.19%), 14 with unknown TNM status (0.14%), 1,266 with multiple primary tumors (13.01%), and 118 with missing data (1.21%), we included 7,827 patients in the analysis (Fig. 1). The median age of the patients was 42 years. Most participants were non-Hispanic Caucasians (54.98%). The majority had FIGO stage I (95.15%), squamous cell carcinoma (62.21%), grade II histology (38.04%), tumors ≤2 cm (39.86%), and no LN involvement (86.07%). Only 28.03% of the patients received adjuvant radiation, and 18.72% received chemotherapy. Delays >1 month correlated with older age, Hispanic or non-Hispanic Black race, single status, higher stage, higher grade, tumor size 2–4 cm, and adjuvant radiation, whereas adenosquamous carcinoma showed a non-significant trend. Delays ≤1 month were linked to non-Hispanic Caucasian race, married status, and tumor size ≤2 cm. Median household income was not significantly associated with surgical delay. All the clinicopathological characteristics are shown in Table 1.
Table 1. Clinicopathologic characteristics of patients with cervical cancer in the study cohort.
| Characteristic | Total (n=7,827) | Treatment delay (1 mo) | |||
|---|---|---|---|---|---|
| ≤1 mo (n=5,880, 75.12%) | >1 mo (n=1,947, 24.88%) | p-value | |||
| Age (yr) | 42 (36–51) | 43.72±11.38 | 45.08±11.91 | <0.001* | |
| Race | <0.001 | ||||
| Hispanic | 2,001 (25.57) | 1,345 (17.18) | 656 (8.38) | ||
| Non-Hispanic Caucasian | 4,303 (54.98) | 3,432 (43.85) | 871 (11.13) | ||
| Non-Hispanic American Indian/Alaska Native | 67 (0.86) | 49 (0.63)† | 18 (0.23)† | ||
| Non-Hispanic Asian and Pacific Islander | 849 (10.85) | 649 (8.29)† | 200 (2.56)† | ||
| Non-Hispanic Black | 607 (7.76) | 405 (5.17) | 202 (2.58) | ||
| Marital status | <0.001 | ||||
| Single | 2,028 (25.91) | 1,440 (18.40) | 588 (7.51) | ||
| Married | 4,173 (53.32) | 3,224 (41.19) | 949 (12.12) | ||
| Other | 1,305 (16.67) | 970 (12.39)† | 335 (4.28)† | ||
| Unknown | 321 (4.10) | 246 (3.14)† | 75 (0.96)† | ||
| Median household income | 0.500 | ||||
| <$55,000 | 1,419 (18.13) | 1,076 (13.75) | 343 (4.38) | ||
| ≥$55,000 | 6,408 (81.87) | 4,804 (61.38) | 1,604 (20.49) | ||
| Stage | 0.029 | ||||
| I | 7,447 (95.15) | 5,613 (71.71) | 1,834 (23.43) | ||
| II | 380 (4.85) | 267 (3.41) | 113 (1.44) | ||
| Histology | 0.034 | ||||
| Squamous cell carcinoma | 4,869 (62.21) | 3,695 (47.21)‡ | 1,174 (15.00)‡ | ||
| Adenocarcinoma | 2,470 (31.56) | 1,839 (23.50)† | 631 (8.06)† | ||
| Adenosquamous carcinoma | 488 (6.23) | 346 (4.42)‡ | 142 (1.81)‡ | ||
| Grade | 0.038 | ||||
| I | 1,044 (13.34) | 791 (10.11)† | 253 (3.23)† | ||
| II | 2,977 (38.04) | 2,253 (28.78)† | 724 (9.25)† | ||
| III/IV | 2,474 (31.61) | 1,810 (23.13) | 664 (8.48) | ||
| Unknown | 1,332 (17.02) | 1,026 (13.11)† | 306 (3.91)† | ||
| Tumor size (cm) | <0.001 | ||||
| ≤2 | 3,120 (39.86) | 2,394 (30.59) | 726 (9.28) | ||
| 2–4 | 2,178 (27.83) | 1,538 (19.65) | 640 (8.18) | ||
| >4 | 973 (12.43) | 732 (9.35)† | 241 (3.08)† | ||
| Unknown | 1,556 (19.88) | 1,216 (15.54) | 340 (4.34) | ||
| Lymph node involvement | 0.078 | ||||
| No | 6,737 (86.07) | 5,085 (64.97) | 1,652 (21.11) | ||
| Yes | 1,090 (13.93) | 795 (10.16) | 295 (3.77) | ||
| Adjuvant radiation | 0.003 | ||||
| No/unknown | 5,633 (71.97) | 4,284 (54.73) | 1,349 (17.24) | ||
| Performed | 2,194 (28.03) | 1,596 (20.39) | 598 (7.64) | ||
| Chemotherapy | 0.635 | ||||
| No/unknown | 6,362 (81.28) | 4,787 (61.16) | 1,575 (20.12) | ||
| Performed | 1,465 (18.72) | 1,093 (13.96) | 372 (4.75) | ||
Values are presented as median (Q1–Q3), mean ± standard deviation, or number (%).
*Independent t-test.
†Groups without a common subscript differed significantly.
‡The variable shows trends in deviation but is not significant in post hoc analysis.
The median follow-up period was 99 months, with a median surgical delay of 1 month (range, 0–17 months). The 5-year OS was 91.07% (95% CI=90.39%–91.77%). Univariate analysis showed that worse OS was associated with age >40 years, non-Hispanic black race, higher stage, higher grade, tumor size >2 cm, LN involvement, and receipt of adjuvant radiotherapy or chemotherapy. Adenocarcinoma was associated with a better OS. Surgical delay >1 month correlated with worse OS (χ2=16.575, p<0.001; Fig. 2A). Multivariate analysis confirmed that age >40 years (41–65 vs. ≤40 years, HR=1.35, 95% CI=1.17–1.56, p<0.001; >65 vs. ≤40, HR=4.38, 95% CI=3.59–5.34, p<0.001) and non–Hispanic Black race (HR=1.57, 95% CI=1.24–1.99, p<0.001) were linked to worse OS. Other risk factors included stage II disease (HR=1.38, 95% CI=1.10–1.72, p=0.005), higher grade (II vs. I, HR=1.57, 95% CI=1.17–2.10, p=0.003; III vs. I, HR=1.79, 95% CI=1.33–2.41, p<0.001), tumor size >2 cm (2–4 vs. ≤2, HR=1.81, 95% CI=1.50–2.17, p<0.001; >4 vs. ≤2, HR=2.44, 95% CI=1.99–3.00, p<0.001; unknown vs. ≤2, HR=1.43, 95% CI=1.16–1.77, p=0.001), and LN involvement (HR=1.64, 95% CI=1.37–1.96, p<0.001). Adenocarcinoma was linked to better OS (HR=0.82, 95% CI=0.69–0.97, p=0.022). Adjuvant radiotherapy (HR=1.30, 95% CI=1.08–1.56, p=0.006) was associated with worse OS, whereas chemotherapy did not. Surgical delay >1 month was associated with worse OS (HR=1.22, 95% CI=1.05–1.41, p=0.009; Table 2).
Fig. 2. Kaplan-Meier curves with univariable hazard ratio estimates for overall survival (A) and cancer-specific survival (B), stratified by dichotomized surgical delay in patients with early-stage cervical cancer.
Table 2. Overall survival of patients with cervical cancer who received surgery as the primary treatment.
| Characteristic | Total=7,827, No. (%) | Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | p-value | HR | 95% CI | p-value | |||
| Age group (yr) | ||||||||
| ≤40 | 3,425 (43.76) | - | - | - | - | - | - | |
| 41–65 | 3,968 (50.70) | 1.37 | 1.19–1.59 | <0.001 | 1.35 | 1.17–1.56 | <0.001 | |
| >65 | 434 (5.54) | 4.89 | 4.03–5.94 | <0.001 | 4.38 | 3.59–5.34 | <0.001 | |
| Race | ||||||||
| Hispanic | 2,001 (25.57) | - | - | - | - | - | - | |
| Non-Hispanic Caucasian | 4,303 (54.98) | 1.00 | 0.85–1.18 | 0.993 | 1.08 | 0.92–1.28 | 0.349 | |
| Non-Hispanic American Indian/Alaska Native | 67 (0.86) | 0.40 | 0.13–1.26 | 0.118 | 0.41 | 0.13–1.28 | 0.125 | |
| Non-Hispanic Asian and Pacific Islander | 849 (10.85) | 1.16 | 0.91–1.47 | 0.228 | 1.06 | 0.83–1.35 | 0.643 | |
| Non–Hispanic Black | 607 (7.76) | 1.63 | 1.29–2.06 | <0.001 | 1.57 | 1.24–1.99 | <0.001 | |
| Stage | ||||||||
| I | 7,447 (95.15) | - | - | - | - | - | - | |
| II | 380 (4.85) | 2.78 | 2.25–3.44 | <0.001 | 1.38 | 1.10–1.72 | 0.005 | |
| Histology | ||||||||
| Squamous cell carcinoma | 4,869 (62.21) | - | - | - | - | - | - | |
| Adenocarcinoma | 2,470 (31.56) | 0.62 | 0.53–0.73 | <0.001 | 0.82 | 0.69–0.97 | 0.022 | |
| Adenosquamous carcinoma | 488 (6.23) | 1.32 | 1.05–1.66 | 0.016 | 1.23 | 0.98–1.55 | 0.075 | |
| Grade | ||||||||
| I | 1,044 (13.34) | - | - | - | - | - | - | |
| II | 2,977 (38.04) | 2.23 | 1.68–2.96 | <0.001 | 1.57 | 1.17–2.10 | 0.003 | |
| III/IV | 2,474 (31.61) | 3.20 | 2.42–4.23 | <0.001 | 1.79 | 1.33–2.41 | <0.001 | |
| Unknown | 1,332 (17.02) | 1.32 | 0.94–1.85 | 0.105 | 1.17 | 0.83–1.65 | 0.368 | |
| Tumor size (cm) | ||||||||
| ≤2 | 3,120 (39.86) | - | - | - | - | - | - | |
| 2–4 | 2,178 (27.83) | 2.57 | 2.16–3.06 | <0.001 | 1.81 | 1.50–2.17 | <0.001 | |
| >4 | 973 (12.43) | 3.79 | 3.13–4.59 | <0.001 | 2.44 | 1.99–3.00 | <0.001 | |
| Unknown | 1,556 (19.88) | 1.34 | 1.09–1.65 | 0.006 | 1.43 | 1.16–1.77 | 0.001 | |
| Lymph node involvement | ||||||||
| No | 6,737 (86.07) | - | - | - | - | - | - | |
| Yes | 1,090 (13.93) | 2.75 | 2.38–3.17 | <0.001 | 1.64 | 1.37–1.96 | <0.001 | |
| Adjuvant radiation | ||||||||
| No/unknown | 5,633 (71.97) | - | - | - | - | - | - | |
| Performed | 2,194 (28.03) | 2.66 | 2.34–3.03 | <0.001 | 1.30 | 1.08–1.56 | 0.006 | |
| Chemotherapy | ||||||||
| No/unknown | 6,362 (81.28) | - | - | - | - | - | - | |
| Performed | 1,465 (18.72) | 2.53 | 2.20–2.90 | <0.001 | 1.12 | 0.92–1.37 | 0.269 | |
| Treatment delay (mo) | ||||||||
| ≤1 | 5,880 (75.12) | - | - | - | - | - | - | |
| >1 | 1,947 (24.88) | 1.35 | 1.17–1.55 | <0.001 | 1.22 | 1.05–1.41 | 0.009 | |
CI, confidence interval; HR, hazard ratio.
For cancer-specific survival (CSS), the 5-year survival rate was 93.38% (95% CI=92.78%–93.98%). Univariate analysis linked worse CSS to age >65 years, non–Hispanic black race, higher stage, adenosquamous histology and higher grade, tumor size >2 cm, LN involvement, and receipt of adjuvant radiotherapy or chemotherapy. Adenocarcinoma was associated with a better CSS. Surgical delay >1 month correlated with worse CSS (χ2=11.383, p<0.001; Fig. 2B). Multivariate analysis indicated that worse CSS was associated with age >65 years (HR=1.47, 95% CI=1.06–2.02, p=0.019) and non-Hispanic Black race (HR=1.50, 95% CI=1.10–2.04, p=0.011). Other factors included stage II disease (HR=1.40, 95% CI=1.06–1.83, p=0.016), adenosquamous histology (HR=1.75, 95% CI=1.35–2.26, p<0.001), higher grade (II vs. I, HR=1.54, 95% CI=1.04–2.28, p=0.033; III/IV vs. I, HR=1.84, 95% CI=1.24–2.75, p=0.003), tumor size >2 cm (2–4 vs. ≤2, HR=2.36, 95% CI=1.82–3.06, p<0.001; >4 vs. ≤2, HR=3.65, 95% CI=2.76–4.83, p<0.001; unknown vs. ≤2, HR=1.40, 95% CI=1.00–1.95, p=0.047), and LN involvement (HR=1.85, 95% CI=1.49–2.30, p<0.001). Adjuvant radiotherapy was associated with worse CSS (HR=1.53, 95% CI=1.19–1.96, p=0.001); however, chemotherapy had no significant effect. Surgical delay >1 month was associated with worse CSS (HR=1.25, 95% CI=1.03–1.51, p=0.022; Table 3). These results indicate that surgical delays >1 month were significantly associated with worse survival outcomes. Multicollinearity was assessed using variance inflation factors (VIFs). In both OS and CSS multivariable Cox models, all predictors showed low VIFs (approximately 1–2), except grade (approximately 5); sensitivity analyses collapsing and excluding grade yielded stable coefficient estimates, indicating stable model estimation despite collinearity.
Table 3. Cancer-specific survival of patients with cervical cancer who received surgery as a primary treatment.
| Characteristic | Total=7,827, No. (%) | Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | p-value | HR | 95% CI | p-value | |||
| Age group (yr) | ||||||||
| ≤40 | 3,425 (43.76) | - | - | - | - | - | - | |
| 41–65 | 3,968 (50.70) | 0.91 | 0.77–1.09 | 0.328 | 0.88 | 0.73–1.05 | 0.160 | |
| >65 | 434 (5.54) | 1.71 | 1.25–2.34 | 0.001 | 1.47 | 1.06–2.02 | 0.019 | |
| Race | ||||||||
| Hispanic | 2,001 (25.57) | - | - | - | - | - | - | |
| Non-Hispanic Caucasian | 4,303 (54.98) | 0.89 | 0.72–1.10 | 0.275 | 1.00 | 0.81–1.24 | 0.984 | |
| Non-Hispanic American Indian/Alaska Native | 67 (0.86) | 0.21 | 0.03–1.50 | 0.119 | 0.18 | 0.02–1.26 | 0.084 | |
| Non-Hispanic Asian and Pacific Islander | 849 (10.85) | 1.00 | 0.73–1.37 | 0.994 | 1.04 | 0.76–1.42 | 0.820 | |
| Non-Hispanic Black | 607 (7.76) | 1.42 | 1.04–1.93 | 0.026 | 1.50 | 1.10–2.04 | 0.011 | |
| Stage | ||||||||
| I | 7,447 (95.15) | - | - | - | - | - | - | |
| II | 380 (4.85) | 3.14 | 2.42–4.08 | <0.001 | 1.40 | 1.06–1.83 | 0.016 | |
| Histology | ||||||||
| Squamous cell carcinoma | 4,869 (62.21) | - | - | - | - | - | - | |
| Adenocarcinoma | 2,470 (31.56) | 0.70 | 0.57–0.86 | 0.001 | 1.03 | 0.82–1.28 | 0.817 | |
| Adenosquamous carcinoma | 488 (6.23) | 2.08 | 1.61–2.68 | <0.001 | 1.75 | 1.35–2.26 | <0.001 | |
| Grade | ||||||||
| I | 1,044 (13.34) | - | - | - | - | - | - | |
| II | 2,977 (38.04) | 2.30 | 1.58–3.36 | <0.001 | 1.54 | 1.04–2.28 | 0.033 | |
| III/IV | 2,474 (31.61) | 3.70 | 2.55–5.36 | <0.001 | 1.84 | 1.24–2.75 | 0.003 | |
| Unknown | 1,332 (17.02) | 0.80 | 0.48–1.34 | 0.397 | 0.82 | 0.48–1.37 | 0.444 | |
| Tumor size (cm) | ||||||||
| ≤2 | 3,120 (39.86) | - | - | - | - | - | - | |
| 2–4 | 2,178 (27.83) | 3.84 | 2.99–4.93 | <0.001 | 2.36 | 1.82–3.06 | <0.001 | |
| >4 | 973 (12.43) | 6.75 | 5.20–8.76 | <0.001 | 3.65 | 2.76–4.83 | <0.001 | |
| Unknown | 1,556 (19.88) | 1.28 | 0.92–1.77 | 0.145 | 1.40 | 1.00–1.95 | 0.047 | |
| Lymph node involvement | ||||||||
| No | 6,737 (86.07) | - | - | - | - | - | - | |
| Yes | 1,090 (13.93) | 4.14 | 3.48–4.93 | <0.001 | 1.85 | 1.49–2.30 | <0.001 | |
| Adjuvant radiation | ||||||||
| No/unknown | 5,633 (71.97) | - | - | - | - | - | - | |
| Performed | 2,194 (28.03) | 4.15 | 3.49–4.93 | <0.001 | 1.53 | 1.19–1.96 | 0.001 | |
| Chemotherapy | ||||||||
| No/unknown | 6,362 (81.28) | - | - | - | - | - | - | |
| Performed | 1,465 (18.72) | 3.99 | 3.36–4.73 | <0.001 | 1.27 | 0.99–1.64 | 0.064 | |
| Treatment delay (mo) | ||||||||
| ≤1 | 5,880 (75.12) | - | - | - | - | - | - | |
| >1 | 1,947 (24.88) | 1.38 | 1.14–1.66 | 0.001 | 1.25 | 1.03–1.51 | 0.022 | |
CI, confidence interval; HR, hazard ratio.
We constructed a survival nomogram based on the Cox model (Fig. 3A and B). The weight coefficient, based on the model for each variable, was translated into contribution points ranging from 0 to 10. These cumulative points were then mapped to predict the survival probabilities at 3, 5, and 10 years. The significant factors included histology, race, age group, tumor grade, stage, tumor size, LN involvement, radiotherapy, and surgical delay. The receiver operating characteristic analysis of the nomograms demonstrated AUCs of 76.07% (74.1%–78.3%) and 80.19% (78.21%–82.17%) for the prediction of 5-year OS and CSS (Fig. 3C and D), respectively. The nomogram was validated using 5,000 bootstrap resamples, yielding a C-index of 0.735 for OS and 0.777 for CSS, both of which demonstrated moderate-to-good discriminatory ability. Calibration plots with 2,000 bootstrap resamples were used to assess the predicted and observed probabilities aligned for both OS and CSS at 3, 5, and 10 years (Fig. 3E-J). Based on the calibration plots, the models exhibited good agreement between the predicted and observed outcomes.
Fig. 3. Prognostic nomogram in patients with early-stage cervical cancer for OS (A) and CSS (B). ROC analysis showed an AUC of 76.07% for 5-year OS (C) and 80.19% for 5-year CSS (D). Calibration curves are shown for OS at 3 years (E), 5 years (F), and 10 years (G) as well as CSS at 3 years (H), 5 years (I), and 10 years (J). The dashed and colored lines represent the predicted and observed survival probabilities, respectively.
AC, adenocarcinoma; ASC, adenosquamous carcinoma; AUC, area under the curve; CSS, cancer-specific survival; NHAIAN, non-Hispanic American Indian/Alaska Native; NHAPI, non-Hispanic Asian and Pacific Islander; NHB, non-Hispanic Black; OS, overall survival; ROC, receiver operating characteristic; SCC, squamous cell carcinoma.
DISCUSSION
Our study found that patients with early-stage CC undergoing radical or extended hysterectomy with surgical delays >1 month were associated with worse OS and CSS. We also developed a nomogram using nine risk factors to predict 3-, 5-, and 10-year survival, improving individualized risk assessment.
In this study, approximately 90% of the patients experienced surgical delays of ≤2 months, aligning with previous findings [15,17]. The median surgical delay was 1 month. Our analysis identified several factors linked to delays of >1 month, including older age, Hispanic or non-Hispanic Black race, single marital status, higher stage, higher grade, and larger tumor size. These findings align with those of earlier studies, which reported that patients with CC with longer delays were more likely to be older and have more advanced disease [18,19]. The association between higher grade and treatment delay aligns with a previous ovarian cancer study [20]. A correlation between race and surgical delay has been observed, with prior research indicating that black patients experience longer treatment delays for various cancers, including breast and ovarian cancers [20,21]. Although the reasons for this disparity are unclear, factors such as transportation issues, difficulties with medical leave, and financial burdens may have contributed to the delay [20,21]. Marital status has been linked to shorter treatment delays, with married patients often experiencing faster treatment initiation [8,20]. This may result from emotional and financial support from the spouse, which can enhance hospital visits, adherence to medical advice, and timely treatment [20].
After adjusting for prognostic factors and confounders, this study found that a surgical delay of >1 month was associated with worse OS and CSS in patients with early-stage CC. These findings contrast with those of 3 hospital-based studies and one population-based study, which suggested that surgical delay does not affect clinical outcomes in early-stage CC [6,14,15,16]. Umezu et al. [14] analyzed the data of 117 patients stages IA–IIA CC and found no negative survival impact with a surgical delay of >50 days. Similarly, Matsuo et al. [15] studied 217 patients with stages IA–IIA CC and found no significant association between surgical delay (≥8 weeks) and disease-free survival or OS. A Dutch population-based study of 2,755 patients with CC found no significant association between waiting periods >8 weeks and OS [16]. One explanation for these findings is that patients with early-stage CC generally have a favorable prognosis after radical surgery. The smaller sample sizes (117–2,755 cases) and short follow-up periods (4.2–4.6 years) in these studies may have limited their ability to detect subtle effects of surgical delay [6,14,15,16]. In contrast, our study analyzed 7,827 patients with a longer follow-up (the median follow-up period was 8.25 years), providing a more robust assessment. Nevertheless, our findings align with those of Matsuo et al. [15], who studied surgical delays and all-cause mortality in 12,603 patients with stages IB–IIA CC using data from the National Cancer Database. They found that prolonged waiting periods between 2 and 12 weeks were associated with a slight increase in all-cause mortality, with adjusted HRs of 1.11 (95% CI=1.02–1.21) at 6 weeks and 1.19 (95% CI=1.02–1.39) at 12 weeks compared with surgery at 1 week. However, no significant overall association was observed across the entire 18-week study period (p=0.06) [12]. The subtle statistical association in Matsuo et al.’s study [15] at specific timepoints, but not over the full 18-week period, contrasts with the significant association between surgical delay and survival outcomes observed in our study. This discrepancy may be explained by methodological differences. Although Matsuo et al. [15] focused on all-cause mortality and used Cox proportional hazards regression with logistic regression models incorporating restricted cubic spline transformations, our study directly assessed OS and CSS using multivariate Cox regression. Differences in survival outcomes and surgical delay cutoffs (Matsuo et al. [15]: 6, 12, and 18 weeks; our study: >1 month) may contribute to variations in the findings. There are several possible explanations for our results. Treatment delays may be associated with tumor progression or upstaging before therapy begins [11,12]. Prolonged intervals may also be associated with reduced treatment effectiveness, especially in rapidly growing tumors, by increasing tumor burden or hypoxia, thereby reducing radiosensitivity and chemosensitivity in the adjuvant setting [22]. Patient-related factors—such as comorbidities, socioeconomic barriers, or poor compliance—may also contribute [3,23]. These mechanisms may partly account for the observed association between treatment delay and worse OS and CSS. The Kaplan-Meier curves for OS and CSS extend beyond 20 years because SEER provides long-term follow-up data. Although few patients remained under observation after 15 years, the persistent separation between the ≤1 month and >1 month delay groups likely originated from early differences that continued over time. Since most early-stage CC recurrences occur within the first few years after treatment, the later portions of the curves mainly reflect long-term survival of patients with initially different prognoses, rather than new cancer deaths from late recurrence, which have been reported to be relatively rare (approximately 6%) in previous study [24,25]. The relatively young median age of our cohort (42 years) further supports a lower risk of death from comorbidities.
Based on our findings, surgical delay >1 month was associated with worse survival outcomes in patients with early-stage CC. Moreover, in clinically significant situations, particularly among pregnant patients with CC, surgical delay presents unique challenges that require balancing maternal oncologic prognosis with fetal safety. Determining the optimal timing of intervention necessitates a multidisciplinary approach involving a gynecologic oncologist, maternal–fetal medicine specialist, and neonatologist. When surgical delay beyond 1 month is unavoidable, neoadjuvant chemotherapy may serve as an effective bridging strategy to control disease progression while allowing fetal maturation, thereby potentially improving neonatal outcomes [26,27,28,29]. Gynecologic oncologists should remain aware of the adverse impact of treatment delay on early-stage CC survival and prioritize timely surgical management to optimize patient outcomes.
Prior studies suggest that many clinicopathologic parameters, such as age [30,31], race [30,31], marital status [30,31], histology [30,32], grade [30,31,32], tumor size [30,31,32], stromal invasion [3], LN status [3,31,32], stage [30,31], number of LN involvement [32], and log of odds between the number of positive LNs and the number of negative LNs [30] were correlated significantly with survival outcomes in early-stage CC treated radical surgery. This study established age, race, stage, grade, tumor size, LN status, histology, adjuvant radiation, and surgical delay as independent predictive variables. The poorer survival observed among CC patients receiving radiotherapy may be attributable to confounding by indication, as radiotherapy is typically administered to patients with higher-risk disease characteristics. Surprisingly, the results of our study showed that adenocarcinoma was associated with improved OS compared with squamous cell carcinoma, whereas prior studies reported a similar or poorer prognosis for adenocarcinoma [33,34]. This discrepancy may be explained by variations in patient characteristics, such as underlying comorbidities, and competing causes of death, which appear to disproportionately affect patients with squamous cell carcinoma. These factors are more likely to influence OS without affecting CSS.
A nomogram is a valuable and reliable tool for predicting individual illness risk and prognosis using statistical models that combine independent predictive factors [22,23]. In this study, we developed a robust nomogram incorporating key prognostic variables (histology, race, age group, tumor grade, stage, tumor size, LN involvement, radiotherapy, and surgical delay) to estimate the 3-, 5-, and 10-year survival probabilities of patients with early-stage CC who underwent radical surgery. Our models accurately predicted OS and CSS, with strong validation, showing an AUC of 76.07% for the 5-year OS and 80.19% for the 5-year CSS. With C-index values of 0.735 for OS and 0.777 for CSS, the nomogram demonstrated an excellent predictive ability. Overall, the models exhibited good calibration, discrimination, and accuracy, highlighting their clinical value. To our knowledge, this is the first predictive model that includes surgical delays in this patient group. Unlike fixed pathological markers such as tumor size, surgical delay is a process-driven factor we can actually change. By adding it to our nomogram, we show that patient outcomes depend not only on tumor biology but also on how quickly the healthcare system responds. This makes the nomogram more than a prediction tool—it becomes a practical reminder to shorten wait times. In real clinical practice, these findings support prioritizing earlier surgery to reduce risks that extend beyond the tumor’s pathology.
Our study’s strengths include the utilization of a large nationwide cohort of patients with early-stage CC treated with radical surgery, long-term follow-up, and effective nomograms incorporating multiple prognostic factors for personalized survival predictions. However, this study has some limitations. Selection bias may exist as this was a retrospective cohort study. Retrospective designs are prone to temporal ambiguity and immortal time bias, particularly when surgical delays are defined retrospectively. To mitigate this, we performed a landmark sensitivity analysis with landmark times ranging from 1 to 17 months, while defining treatment delay as >1 month versus ≤1 month. Across these analyses, the association between surgical delay and both OS and CSS remained in the same direction and retained statistical significance, supporting the robustness of our findings. Unmeasured confounders, such as performance status and comorbidities, were not fully addressed. Information on the type of surgical procedure (abdominal versus laparoscopic) and the reasons for surgical delay were unavailable, as the database lacks such detailed data. Additionally, the SEER database records the time in months, limiting the evaluation of delays to <1 month. Finally, although the nomogram demonstrated moderate predictive accuracy, its application was limited to the U.S. population; therefore, multicenter external validation in non-U.S. populations with larger samples is needed. This study underscores the clinical relevance of timely surgical management and comprehensive pretreatment evaluation. We also created a nomogram using nine risk factors to estimate 3-, 5-, and 10-year survival, enhancing individualized risk assessment.
Our analysis of SEER data suggests that patients with early-stage CC undergoing radical or extended hysterectomy with surgical delays >1 month were associated with worse survival outcomes. These findings may have important clinical implications for the timing of surgery and pretreatment evaluation.
ACKNOWLEDGEMENTS
The authors would like to thank the Office of International Affairs, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand, for assistance with the English language editing of the manuscript.
Footnotes
Conflict of Interest: No potential conflict of interest relevant to this article was reported.
Data Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.
- Conceptualization: T.V., K.L., J.H.
- Data curation: T.V., K.L.
- Formal analysis: T.V.
- Investigation: T.V., K.L., J.H.
- Methodology: T.V., K.L., J.H.
- Project administration: J.H.
- Supervision: J.H.
- Validation: T.V., K.L., J.H.
- Writing - original draft: T.V., K.L., J.H.
- Writing - review & editing: J.H.
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