» Articles » PMID: 33183244

A Nomogram for Predicting Overall Survival in Patients with Ewing Sarcoma: a SEER-based Study

Overview
Publisher Biomed Central
Specialties Orthopedics
Physiology
Date 2020 Nov 13
PMID 33183244
Citations 11
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Ewing sarcoma, the second most frequent bone tumor in children and adolescents, is often presented with localized disease or metastatic-related symptoms. In this study, we aim to construct and validate a nomogram for patients with Ewing sarcoma to predict the 3- and 5-year overall survival (OS) based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods: Demographic and clinic pathological characteristics of patients with Ewing sarcoma diagnosed between 2010 and 2015 were extracted from SEER database. Univariate and multivariate Cox analyses were carried out to identify the independent characteristics. The independent factors were further included into the construction of a nomogram. Finally, c-index and calibration curves were used to validate the nomogram.

Results: A total of 578 patients were enrolled into our analysis. The results of univariate Cox analysis showed that age, 7th AJCC stage, 7th AJCC T stage, 7th AJCC N stage, 7th AJCC M stage, metastatic status to lung, liver and bone were significant factors. Multivariate Cox analysis was performed and it confirmed age, N stage and bone metastasis as independent variables. Next, a nomogram was constructed using these independent variables in prediction to the 3- and 5-year OS. Furthermore, favorable results with c-indexes (0.757 in training set and 0.697 in validation set) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.

Conclusions: The individualized nomogram demonstrated a good ability in prognostic prediction for patients with Ewing sarcoma.

Citing Articles

Prognostic Modeling for Bone Sarcomas Based on a Large Prospective Cohort From a Tertiary Care Cancer Center in India.

Bajpai J, Sarkar L, Rath S, Pawar A, Chandrashekharan A, Panda G JCO Glob Oncol. 2025; 11:e2400142.

PMID: 39913876 PMC: 11892611. DOI: 10.1200/GO.24.00142.


Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study.

Liu X, Yuan J, Wang X, Yu S Am J Cancer Res. 2024; 14(10):4731-4746.

PMID: 39553229 PMC: 11560808. DOI: 10.62347/ZQVY3877.


Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

Zhou C, Li H, Zeng H, Wang P Clin Transl Oncol. 2024; .

PMID: 39333451 DOI: 10.1007/s12094-024-03717-9.


Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis.

Tang W, Li R, Lai X, Yu X, He R Heliyon. 2024; 10(17):e37013.

PMID: 39286090 PMC: 11402751. DOI: 10.1016/j.heliyon.2024.e37013.


The clinical characteristics, novel predictive tool, and risk classification system for primary Ewing sarcoma patients that underwent chemotherapy: A large population-based retrospective cohort study.

Huang C, Yu Q, Ding Z, Zhou Z, Shi X Cancer Med. 2022; 12(5):6244-6259.

PMID: 36271609 PMC: 10028057. DOI: 10.1002/cam4.5379.


References
1.
Bosma S, Ayu O, Fiocco M, Gelderblom H, Dijkstra P . Prognostic factors for survival in Ewing sarcoma: A systematic review. Surg Oncol. 2018; 27(4):603-610. DOI: 10.1016/j.suronc.2018.07.016. View

2.
Chen D, Chen G, Jiang W, Fu M, Liu W, Sui J . Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer. JAMA Surg. 2019; 154(3):e185249. PMC: 6439641. DOI: 10.1001/jamasurg.2018.5249. View

3.
Ren Y, Zhang Z, Shang L, You X . Surgical Resection of Primary Ewing's Sarcoma of Bone Improves Overall Survival in Patients Presenting with Metastasis. Med Sci Monit. 2019; 25:1254-1262. PMC: 6387471. DOI: 10.12659/MSM.913338. View

4.
Iasonos A, Schrag D, Raj G, Panageas K . How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008; 26(8):1364-70. DOI: 10.1200/JCO.2007.12.9791. View

5.
Fukushima T, Ogura K, Akiyama T, Takeshita K, Kawai A . Descriptive epidemiology and outcomes of bone sarcomas in adolescent and young adult patients in Japan. BMC Musculoskelet Disord. 2018; 19(1):297. PMC: 6098838. DOI: 10.1186/s12891-018-2217-1. View