caa a22 4500 605492816 CHVBK 20210128100517.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00261-014-0337-0 doi (NATIONALLICENCE)springer-10.1007/s00261-014-0337-0 Preoperative CT-based nomogram for predicting overall survival in women with non-endometrioid carcinomas of the uterine corpus [Elektronische Daten] [Yulia Lakhman, Derya Yakar, Debra Goldman, Seth Katz, Hebert Vargas, Maura Miccò, Junting Zheng, Chaya Moskowitz, Robert Soslow, Hedvig Hricak, Nadeem Abu-Rustum, Evis Sala] Purpose: To develop a preoperative CT-based nomogram for predicting overall survival (OS) in patients with non-endometrioid carcinomas of the uterine corpus. Methods: Waiving informed consent, the institutional review board approved this HIPAA-compliant, retrospective study of 193 women with histopathologically proven uterine papillary serous carcinomas (UPSC), uterine clear cell carcinomas (UCCC), and uterine carcinosarcomas (UCS) who underwent primary surgical resection between May 1998 and December 2011, and had a preoperative CT≤6weeks before surgery. All CT scans were reviewed for local or/and regional tumor extent, presence of pelvic or/and para-aortic adenopathy, and presence of distant metastases. Univariate survival analysis was performed using log-rank test and Cox regression. Variables shown significant by the univariate analysis were evaluated with the multivariable Cox regression analysis and the results were used to create a nomogram for predicting OS. The predictive accuracy of the nomogram was assessed with the concordance probability index (c-index) and a 3-year calibration plot. Results: Mean patient age was 67.2years (range 49.0-85.9); histologies included UPSC (n=116), UCCC (n=27), and UCS (n=50). Median follow-up was 38.1months (0.9-168.5months). At multivariate analysis, patient age, ascites, and omental implants on CT were significant adverse predictors of OS and were used to build the nomogram. Concordance index for the nomogram was 0.640±0.028. Conclusion: We developed a nomogram with a good concordance probability at predicting OS based on readily available pretreatment clinical and imaging characteristics. This preoperative nomogram has the potential to improve initial treatment planning and patient counseling. Springer Science+Business Media New York, 2014 Nomogram nationallicence CT nationallicence Non-endometrioid carcinoma nationallicence Uterus nationallicence Lakhman Yulia Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Yakar Derya Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Goldman Debra Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut Katz Seth Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Vargas Hebert Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Miccò Maura Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Zheng Junting Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut Moskowitz Chaya Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut Soslow Robert Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA aut Hricak Hedvig Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Abu-Rustum Nadeem Gynecologic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA aut Sala Evis Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut Abdominal Imaging Springer US; http://www.springer-ny.com 40/6(2015-08-01), 1761-1768 0942-8925 40:6<1761 2015 40 261 https://doi.org/10.1007/s00261-014-0337-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00261-014-0337-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lakhman Yulia Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Yakar Derya Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Goldman Debra Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Katz Seth Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Vargas Hebert Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Miccò Maura Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Zheng Junting Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Moskowitz Chaya Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Soslow Robert Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Hricak Hedvig Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Abu-Rustum Nadeem Gynecologic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 700 1- Sala Evis Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA aut NATIONALLICENCE 773 0- Abdominal Imaging Springer US; http://www.springer-ny.com 40/6(2015-08-01), 1761-1768 0942-8925 40:6<1761 2015 40 261