Objective

Given that cervical cancer incidence rates do not decline in women >65, there is generally limited screening, and these women have a poor prognosis, it is imperative to better understand this population. We aim to describe the characteristics, treatment, and survival of women >65 diagnosed with cervical cancer.

Methods

SEER-Medicare 2004-2013 data was used to describe 2274 patients >65 diagnosed with cervical cancer. Five-year cancer-specific survival was estimated using the Kaplan-Meier method. Multivariable Poisson and Cox regression analyses identified characteristics associated with treatment and mortality.

Results

The median age was 76.1 years, with nearly one-third of cases occurring in women >80 years. Most patients were non-Hispanic White (64.8%), had comorbidity scores ≥ 1 (53.9%) and squamous histology (66.3%). Most women were diagnosed at stage II or higher (62.7%), including nearly one-quarter at Stage IV (23.1%). Nearly 15% of patients were not treated (14.6%). Lack of treatment was associated with oldest age (>80), comorbidity scores ≥3, and stage IV disease. Five-year cancer-specific survival was 50%. Increasing age and stage at diagnosis were significantly associated with lower cancer-specific survival whereas treatment was strongly associated with increased survival.

Conclusion

Most women >65 with cervical cancer are diagnosed with locally advanced or metastatic disease and many do not receive treatment. Survival is improved with early-stage diagnosis and treatment. These findings, coupled with the fact that women >65 constitute an increasing proportion of the population, highlight the need to re-evaluate screening and treatment practices in this population to detect cervical cancer at earlier stages and increase survival.

Novelty and impact statement

In SEER-Medicare linked data from 2004 to 2013, most women >65 with cervical cancer were diagnosed with locally advanced or metastatic disease. Both receipt of treatment and survival decreased with increasing age. These findings, coupled with the fact that women aged >65 constitute an increasing proportion of the population, highlight the need to re-evaluate screening and treatment practices in older women to detect cervical cancer at earlier stages and increase survival.

A recent ASCO workforce study projects a significant shortage of oncologists in the U.S. by 2020, especially in rural/underserved (R/US) areas. The current study aim was to determine the patterns of distribution of U.S. gynecologic oncologists (GO) and to identify provider-based attitudes and barriers that may prevent GOs from practicing in R/US regions. U.S. GOs (n = 743) were electronically solicited to participate in an on-line survey regarding geographic distribution and participation in outreach care. A total of 320 GOs (43%) responded; median age range was 35-45 years and 57% were male. Most practiced in an urban setting (72%) at a university hospital (43%). Only 13% of GOs practiced in an area with a population < 50,000. A desire to remain in academics and exposure to senior-level mentorship were the factors most influencing initial practice location. Approximately 50% believed geographic disparities exist in GO workforce distribution that pose access barriers to care; however, 39% "strongly agreed" that cancer patients who live in R/US regions should travel to urban cancer centers to receive care within a center of excellence model. GOs who practice within 50 miles of only 0-5 other GOs were more likely to provide R/US care compared to those practicing within 50 miles of ≥ 10 GOs (p < 0.0001). Most (39%) believed the major barriers to providing cancer care in R/US areas were volume and systems-based. Most also believed the best solution was a hybrid approach, with coordination of local and centralized cancer care services. Among GOs, a self-reported rural-urban disparity exists in the density of gynecologic oncologists. These study findings may help address barriers to providing cancer care in R/US practice environments.

Purpose

The Sedlis criteria define risk factors for recurrence warranting post-hysterectomy radiation for early-stage cervical cancer; however, these factors were defined for squamous cell carcinoma (SCC) at an estimated recurrence risk of ≥30%. Our study evaluates and compares risk factors for recurrence for cervical SCC compared with adenocarcinoma (AC) and develops histology-specific nomograms to estimate risk of recurrence and guide adjuvant treatment.

Methods

We performed an ancillary analysis of GOG 49, 92, and 141, and included stage I patients who were surgically managed and received no neoadjuvant/adjuvant therapy. Multivariable Cox proportional hazards models were used to evaluate independent risk factors for recurrence by histology and to generate prognostic histology-specific nomograms for 3-year recurrence risk.

Results

We identified 715 patients with SCC and 105 with AC; 20% with SCC and 17% with AC recurred. For SCC, lymphvascular space invasion (LVSI: HR 1.58, CI 1.12-2.22), tumor size (TS ≥4 cm: HR 2.67, CI 1.67-4.29), and depth of invasion (DOI; middle 1/3, HR 4.31, CI 1.81-10.26; deep 1/3, HR 7.05, CI 2.99-16.64) were associated with recurrence. For AC, only TS ≥4 cm, was associated with recurrence (HR 4.69, CI 1.25-17.63). For both histologies, there was an interaction effect between TS and LVSI. For those with SCC, DOI was most associated with recurrence (16% risk); for AC, TS conferred a 15% risk with negative LVSI versus a 25% risk with positive LVSI.

Conclusions

Current treatment standards are based on the Sedlis criteria, specifically derived from data on SCC. However, risk factors for recurrence differ for squamous cell and adenocarcinoma of the cervix. Histology-specific nomograms accurately and linearly represent risk of recurrence for both SCC and AC tumors and may provide a more contemporary and tailored tool for clinicians to base adjuvant treatment recommendations to their patients with cervical cancer.

The authors regret that the version of Table 3 originally published in this article was incorrect. The online version has now been updated and the correct table can be found below. The authors would like to apologise for any inconvenience caused. Table 3. Comparison of 3 yr RFS, nomogram recurrence risk, and Sedlis criteria for predictor variable combinations. [Table Presented]