way in ECC-1, but this inhibition could be abrogated by the additionof Wnt 3a.Conclusion: SFRP1 expression is decreased in both Type I and Type IIendometrial cancer, and appears to exhibit a stage-dependentexpression pattern. Functionally, SFRP1 attenuates the transcriptionalactivity of -catenin. Downregulation of the SFRP1 gene may beresponsible for the activation of the Wnt/-catenin signaling pathwaythat contributes to tumorigenesis in endometrial cancer. Our in vivoand in vitro studies support SFRP1 as a potential biomarker and targetfor a novel biologic agent in endometrial cancer.
Machine learning as a tool to predict survival outcomes forcarcinosarcoma of the female genital tractA. Jonson, E. Dickson, H. Shiao, V. Cherkassky, S. Dhar, L. Downs Jr.University of Minnesota, Minneapolis, MN, USA
Objective: Carcinosarcomas of the female genital tract are rare,exceptionally aggressive tumors, of which uterine carcinosarcomasaccount for 25% of all uterine cancers, and ovarian carcinosarcomasfor 12% of all ovarian cancers. Any mechanisms that could be used tohelp predict survival outcomes in these patients would providepractitioners with valuable tools to help focus treatment recommen-dations and therapies. Historically, cancer research has used basicbiostatistical methods for data analysis to predict survival outcomes.Over the past two decades, machine learning has evolved from adiscipline studied primarily by computer scientists to broaderdisciplines, such as biostatisticians. Our objectives were to 1) deter-mine if machine learning could identify clinical features important inthe prediction of survival and 2) determine if machine learning couldbe used as a predictive tool for determining a patient's status 2 yearsafter diagnosis.Methods: In this retrospective analysis, women diagnosed withcarcinosarcoma of the ovary or uterus from 1952 to the present wereidentified and clinical data was collected for data analysis. In total,198 patients were identified and their clinical data collected. Ourcolleagues at the Department of Electrical and Computer Engineeringused machine learning methodologies to analyze the data.Results: Clinical features included age, BMI, stage, menopausal status,history of diabetes, history of hypertension, parity, prior radiation tothe pelvis, prior hormone therapy, family history, residual tumor leftafter initial surgery, and tumor grade. After modeling was performed,the top three predictive factors were determined to be stage, priorradiation to the pelvis, and parity. The predictive model furthercharacterized those factors, with higher stages being more likely at2 years to be dead of disease, a history of pelvic radiation being morelikely to be alive at 2 years, and those with higher parity more likelyto be dead of disease at 2 years.
The predictive modeling also identified prior hormone therapy,family history, residual tumor, and tumor grade as being valuableclinical features of survival outcomes at 2 years. However, factorssuch as diabetes, hypertension, menopausal status and BMI were notpredictive of survival outcomes at 2 years after diagnosis.
Furthermore, when using the predictive model created withmachine learning, these predictive factors were able to accuratelypredict those patients who were alive 2 years from diagnosis 95% ofthe time. The model was also able to predict those dead of disease at2 years from diagnosis 50% of the time.Conclusion: Machine learning is a relatively new concept in the fieldof cancer research. Using machine learning methodologies, we wereable to identify clinical features that were more likely to predictsurvival outcomes at 2 years from diagnosis. Our model was verygood at identifying those women likely to be alive 2 years after
diagnosis (95% accuracy). Further research is needed to explore theuse of machine learning in the care of cancer patients.
Payer status is associated with surgical interventions foruterine cancerA.C. Fleurya, O.A. Ibeanua, R.E. BristowbaThe Kelly Gynecologic Oncology Service, The Johns Hopkins MedicalInstitutions, Baltimore, MD, USA, bUniversity of California, Irivine-Medical Center, Orange, CA, USA
Objective: To evaluate the association of payer status and surgicalapproach for women who underwent surgical treatment for uterinecancer.Methods: The design was a retrospective cohort study of discharge datafrom nonfederal acute care hospitals in Maryland from 2000 to 2009.Women aged 18 and older who underwent hysterectomy for uterinecancer were included in the study population. The main outcomemeasure was receipt of a minimally-invasive surgical approach. Second-ary outcomes included receipt of lymphadenectomy, and individualsurgeon and individual hospital annual uterine cancer case volume. Theindependent variable was payer status. Medicare-insured patients werethe reference group. We used multivariate logistic regression andadjusted for age, race and APR-DRG mortality risk score, to calculateodds ratios and confidence intervals for each outcome of interest.Results: Among the 5470 women who underwent hysterectomy, 512(9.4%) underwent surgery through a minimally-invasive approach and2727(49.9%) underwent lymphadenectomy. There was no associationbetween payer status and likelihood of undergoing surgery through aminimally-invasive approach. HMO-insured patients were less likely toundergo lymphadenectomy (OR=0.71, 95%CI: 0.580.87). HMO andcommercially-insured patients were less likely to be operated on by ahigh-volume surgeon (OR=0.81, 95%CI: 0.690.94 and OR=0.83,95%CI: 0.790.95, respectively) while Medicaid patients were morelikely to see a high-volume surgeon (OR=1.48, 95%CI: 1.052.08).Commercially-insured patients were less likely to undergo surgery at ahigh-volume hospital (OR=0.84, 95%CI: 0.720.99).Conclusion: In this retrospective analysis of uterine cancer patients,both HMO and commercial insurance coverage were associated witha lower likelihood of undergoing surgery by a high-volume surgeon.HMO coverage was associated with a lower likelihood of undergoinglymphadenectomy. Commercial insurance coverage was associatedwith a lower likelihood of undergoing surgery at a high-volumehospital. Further analysis using prospectively collected data withmore detail regarding peri-operative parameters is needed to clarifypossible reasons for these disparities.
Genetic referral of women at risk for hereditary breast andovarian cancerC.B. Powell, R.D. Littell, F. Sinclair, E. Hoodfar, N. Lovell, A. PressmanKaiser Permanente Northern, CA, USA
Objective: The purpose of this study was two-fold. The first goalwas to retrospectively assess baseline referral rates of patientswith newly diagnosed cancers identifiable as high risk for hereditaryBRCA 1 and BRCA 2 associated malignancies. The second phase was aprospective randomized controlled pilot intervention which con-sisted of sending electronic notification to the managing surgeon/oncologist for each patient identified at risk. In both studies weassessed referral patterns.