BACKGROUND: We aimed to develop a preoperative prediction model identifying the low-risk group for lymph node metastasis in endometrial cancer. METHODS: In 110 patients who underwent preoperative magnetic resonance imaging and serum CA-125 test, logistic analysis was performed to identify predictors. The coefficients obtained from logistic regression were used to construct a scoring system, and a receiver operator characteristic curve was created. RESULTS: Lymph node metastases were found in 14 (12.7%) of 110 patients. After multivariate logistic regression analysis, histologic grade, preoperative CA-125 levels, disease extent, and myometrial invasion assessed by magnetic resonance imaging were selected as viable predictors. The scoring system was internally validated using bootstrapping (P < 0.001), and receiver operator characteristic curve yielded the area under the curve of 0.902. The patients with the score of 0 or 1 (57.3%) were identified as a low-risk group, and no nodal metastasis was observed among them (negative predictive value, 100%: 95% confidence interval, 94.3%-100%). CONCLUSION: The current study suggests that preoperative prediction system to identify the risk of lymph node metastasis is feasible. This model may be useful in preoperative counseling about cost and benefit of systemic lymph node dissection.
BACKGROUND: We aimed to develop a preoperative prediction model identifying the low-risk group for lymph node metastasis in endometrial cancer. METHODS: In 110 patients who underwent preoperative magnetic resonance imaging and serum CA-125 test, logistic analysis was performed to identify predictors. The coefficients obtained from logistic regression were used to construct a scoring system, and a receiver operator characteristic curve was created. RESULTS: Lymph node metastases were found in 14 (12.7%) of 110 patients. After multivariate logistic regression analysis, histologic grade, preoperative CA-125 levels, disease extent, and myometrial invasion assessed by magnetic resonance imaging were selected as viable predictors. The scoring system was internally validated using bootstrapping (P < 0.001), and receiver operator characteristic curve yielded the area under the curve of 0.902. The patients with the score of 0 or 1 (57.3%) were identified as a low-risk group, and no nodal metastasis was observed among them (negative predictive value, 100%: 95% confidence interval, 94.3%-100%). CONCLUSION: The current study suggests that preoperative prediction system to identify the risk of lymph node metastasis is feasible. This model may be useful in preoperative counseling about cost and benefit of systemic lymph node dissection.
Authors: Casper Reijnen; Joanna IntHout; Leon F A G Massuger; Fleur Strobbe; Heidi V N Küsters-Vandevelde; Ingfrid S Haldorsen; Marc P L M Snijders; Johanna M A Pijnenborg Journal: Oncologist Date: 2019-06-11
Authors: Kristine E Fasmer; Ankush Gulati; Julie A Dybvik; Kari S Wagner-Larsen; Njål Lura; Øyvind Salvesen; David Forsse; Jone Trovik; Johanna M A Pijnenborg; Camilla Krakstad; Ingfrid S Haldorsen Journal: Eur Radiol Date: 2022-06-28 Impact factor: 5.315
Authors: Casper Reijnen; Evangelia Gogou; Nicole C M Visser; Hilde Engerud; Jordache Ramjith; Louis J M van der Putten; Koen van de Vijver; Maria Santacana; Peter Bronsert; Johan Bulten; Marc Hirschfeld; Eva Colas; Antonio Gil-Moreno; Armando Reques; Gemma Mancebo; Camilla Krakstad; Jone Trovik; Ingfrid S Haldorsen; Jutta Huvila; Martin Koskas; Vit Weinberger; Marketa Bednarikova; Jitka Hausnerova; Anneke A M van der Wurff; Xavier Matias-Guiu; Frederic Amant; Leon F A G Massuger; Marc P L M Snijders; Heidi V N Küsters-Vandevelde; Peter J F Lucas; Johanna M A Pijnenborg Journal: PLoS Med Date: 2020-05-15 Impact factor: 11.069