Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

Yuyuan Chen; Changxing Chi; Dedian Chen; Sanjun Chen; Binbin Yang; Sijia Huang; Zengpai Zheng

doi:10.1155/2021/4533175

Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

J Oncol. 2021 Dec 22:2021:4533175. doi: 10.1155/2021/4533175. eCollection 2021.

Authors

Yuyuan Chen^{1

2}, Changxing Chi³, Dedian Chen², Sanjun Chen⁴, Binbin Yang⁵, Sijia Huang⁶, Zengpai Zheng¹

Affiliations

¹ Department of Anorectal Surgery, The People's Hospital of Pingyang, Wenzhou, Zhejiang, China.
² The Second Department of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunan, China.
³ Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunan, China.
⁴ Department of Pain Treatment, The First People's Hospital of Chenzhou, Chenzhou, Hunan, China.
⁵ Wenzhou Medical University, Wenzhou, Zhejiang, China.
⁶ Shuitou Town Center Health Center, Wenzhou, Zhejiang, China.

Abstract

Background: The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients.

Methods: Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis.

Results: All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates.

Conclusion: We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.