Add like
Add dislike
Add to saved papers

Development and Validation of a Prognostic Prediction Model for Postoperative Ovarian Sex Cord-Stromal Tumor Patients.

BACKGROUND We developed a nomogram for prognostic prediction of overall survival (OS) in postoperative ovarian sex cord-stromal tumor (SCST) patients and discuss the effect of chemotherapy at various FIGO stages. MATERIAL AND METHODS SCST patients after surgery from 2004 to 2015 were enrolled from the Surveillance, Epidemiology and End-Results (SEER) database, matched into pairs by propensity score matching (PSM), and divided into a training set and a validation set. Univariate and multivariate Cox analyses were conducted to identify significant variables for the development of the nomogram. The nomogram model was validated by concordance index (C-index), receiver operating characteristics (ROCs) curve, calibration plot, and decision curve analysis (DCA). Survival curves showed the integrative ability of prognostic prediction and the efficacy of chemotherapy. RESULTS A total of 913 SCST patients were initially enrolled, and after PSM, 506 patients were included. Age, marital status, CA125 levels, tumor size, FIGO stage, grade, and chemotherapy were indicators for building the OS nomogram. The C-index was 0.850 in the training set and 0.786 in the validation set. Calibration plots were satisfactory and the nomogram had relatively better clinical utility than FIGO stage. The survival analysis showed that the low-risk group had generally longer survival than the high-risk group based on the prognostic score, and chemotherapy had an overall reverse effect on OS. CONCLUSIONS The nomogram model displays the potential to provide individualized prognosis probability of SCSTs and to aid in clinical decision-making. The unfavorable results of chemotherapy in all stages shows the need for further exploration.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app