Callie M Cox Bauer1, Danielle M Greer2, Jessica J F Kram2, Scott A Kamelle3. 1. Department of Obstetrics and Gynecology, Aurora Sinai Medical Center, Aurora Health Care, Milwaukee, WI, United States. Electronic address: callie.cox.bauer@gmail.com. 2. Aurora UW Medical Group, Center for Urban Population, Aurora Sinai Medical Center, Aurora Health Care, Milwaukee, WI, United States. 3. Department of Oncology, Aurora St. Luke''s Medical Center, Aurora Health Care, Milwaukee, WI, United States.
Abstract
OBJECTIVES: To assess the utility of tumor diameter (TD) for predicting lymphatic dissemination (LD) and determining need for lymphadenectomy following diagnosis of endometrioid endometrial cancer. METHODS: Patients diagnosed with stage I-III endometrioid endometrial cancer during 2003-2013 who underwent pelvic or para-aortic lymphadenectomy during hysterectomy were studied. Intraoperative predictors of LD included TD, grade, myometrial invasion (MI), age, body mass index, and race/ethnicity. Candidate logistic regression models of LD were evaluated for model fit and predictive power. RESULTS: Of 737 cancer patients, 68 (9.2%) were node-positive. Single-variable models with only continuous TD (c-statistic 0.77, 95% CI 0.71-0.83) and dichotomous TD with 50-mm cut-off (TD50; c-statistic 0.73, 95% CI 0.67-0.78) were significantly more predictive than with the standard 20-mm cut-off (c-statistic 0.56, 95% CI 0.53-0.59). Overall, the most important LD predictors were TD50 and MI3rds (three-category form). The best candidate model (c-statistic 0.84, 95% CI 0.80-0.88) suggested odds of LD were five times greater for TD >50mm than ≤50mm (OR 4.91, 95% CI 2.73-8.82) and six and ten times greater for MI >33% to ≤66% (OR, 5.70; 95% CI, 2.25-14.5) and >66% (OR 10.2, 95% CI 4.11-25.4), respectively, than ≤33%. Best-model false-negative (0%) and positive (57.2%) rates demonstrated marked improvement over traditional risk-stratification false-negative (1.5%) and positive (76.2%) rates (c-statistic 0.77, 95% CI 0.72-0.82). CONCLUSIONS: Tumor diameter is an important predictor of LD. Our risk model, containing modified forms of MI and TD, yielded a lower false-negative rate and can significantly decrease the number of lymphadenectomies performed on low-risk women. Published by Elsevier Inc.
OBJECTIVES: To assess the utility of tumor diameter (TD) for predicting lymphatic dissemination (LD) and determining need for lymphadenectomy following diagnosis of endometrioid endometrial cancer. METHODS:Patients diagnosed with stage I-III endometrioid endometrial cancer during 2003-2013 who underwent pelvic or para-aortic lymphadenectomy during hysterectomy were studied. Intraoperative predictors of LD included TD, grade, myometrial invasion (MI), age, body mass index, and race/ethnicity. Candidate logistic regression models of LD were evaluated for model fit and predictive power. RESULTS: Of 737 cancerpatients, 68 (9.2%) were node-positive. Single-variable models with only continuous TD (c-statistic 0.77, 95% CI 0.71-0.83) and dichotomous TD with 50-mm cut-off (TD50; c-statistic 0.73, 95% CI 0.67-0.78) were significantly more predictive than with the standard 20-mm cut-off (c-statistic 0.56, 95% CI 0.53-0.59). Overall, the most important LD predictors were TD50 and MI3rds (three-category form). The best candidate model (c-statistic 0.84, 95% CI 0.80-0.88) suggested odds of LD were five times greater for TD >50mm than ≤50mm (OR 4.91, 95% CI 2.73-8.82) and six and ten times greater for MI >33% to ≤66% (OR, 5.70; 95% CI, 2.25-14.5) and >66% (OR 10.2, 95% CI 4.11-25.4), respectively, than ≤33%. Best-model false-negative (0%) and positive (57.2%) rates demonstrated marked improvement over traditional risk-stratification false-negative (1.5%) and positive (76.2%) rates (c-statistic 0.77, 95% CI 0.72-0.82). CONCLUSIONS:Tumor diameter is an important predictor of LD. Our risk model, containing modified forms of MI and TD, yielded a lower false-negative rate and can significantly decrease the number of lymphadenectomies performed on low-risk women. Published by Elsevier Inc.
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