Gynecologic oncologist impact on adjuvant chemotherapy care for stage II-IV ovarian cancer patients

Collaborating with the Centers for Disease Control and Prevention's National Comprehensive Cancer Control Program to Increase Receipt of Ovarian Cancer Care from a Gynecologic Oncologist

Background: Treatment by a gynecologic oncologist is an important part of ovarian cancer care; however, implementation strategies are needed to increase care by these specialists. We partnered with National Comprehensive Cancer Control Programs in Iowa, Michigan, and Rhode Island in a demonstration project to deepen the evidence base for promising strategies that would facilitate care for ovarian cancer by gynecologic oncologists. Methods: Five main implementation strategies (increase knowledge/awareness; improve models of care; improve payment structures; increase insurance coverage; enhance workforce) were identified in the literature and used to develop initiatives. Specific activities were chosen by state programs according to feasibility and needs. Results: Activities included: (1) qualitative interviews with patients to determine barriers to receipt of specialized care; (2) development of patient/provider educational materials; (3) creation of patient/provider checklists to facilitate appropriate referrals; (4) expansion of a toll-free patient navigation hotline for ovarian cancer patients; (5) training of the health care workforce. The programs developed resources (educational handouts, toolkits, 2 webinars, 2 podcasts); trained 167 medical and nursing students during 8 Survivors Teaching Students® workshops; and conducted 3 provider education sessions reaching 362 providers in 45 states. Evaluations showed increases in providers' knowledge, awareness, abilities, and intentions to refer ovarian cancer patients to a gynecologic oncologist. Conclusion: The state program resources we discussed are available for other cancer control programs interested in initiating or expanding activities to improve access/referrals to gynecologic oncologists for ovarian cancer care. They serve as a valuable repository for public health professionals seeking to implement similar interventions.

Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database

Aim

To establish prediction models for 2-year overall survival of ovarian cancer patients with metastasis.

Methods

In total, 4,929 participants from Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into the training set (n = 3,451) and the testing set (n = 1,478). Univariate and multivariable regression were conducted in the training set to identify predictors for 2-year overall survival of metastatic ovarian cancer patients. The C-index was calculated for assessing the performance of the models. The nomogram for the model was plotted. The prediction value of the model was validated in the testing set. Subgroup analysis were performed concerning surgery and chemotherapy status of patients and the metastatic site of ovarian cancer in the testing set. The calibration curves were plotted and the decision curve analysis (DCA) were conducted.

Results

At the end of follow-up, 2,587 patients were survived and 2,342 patients were dead within 2 years. The 2-year survival rate was 52.5%. The prediction models were constructed based on predictors including age, radiation, surgery and chemotherapy, CA125, and bone, liver, and lung metastasis. The prediction model for 2-year overall survival of ovarian cancer patients with metastasis showed good predictive ability with the C-index of the model of 0.719 (95% CI: 0.706–0.731) in the training set and 0.718 (95% CI: 0.698–0.737) in the testing set. In terms of patients with bone metastasis, the C-index was 0.740 (95% CI: 0.652–0.828) for predicting the 2-year overall survival of ovarian cancer patients. The C-index was 0.836 (95% CI: 0.694–0.979) in patients with brain metastasis, 0.755 (95% CI: 0.721–0.788) in patients with liver metastasis and 0.725 (95% CI: 0.686–0.764) in those with lung metastasis for predicting the 2-year overall survival of ovarian cancer patients.

Conclusion

The models showed good predictive performance for 2-year overall survival of metastatic ovarian cancer patients.