Journal Article

Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

Sun, Wei, Wang, Yanlei, Tan, Mingsheng, Wu, Xinjie

2021

Overview

Introduction. We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group (n=572) and a validation group (n=241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.

• Publisher: Hindawi,John Wiley & Sons, Inc
• Subject: Adult / Age / Bone Neoplasms - mortality / Bones / Calibration / Cancer therapies / Chemotherapy / Chondrosarcoma / Chondrosarcoma - mortality / Chondrosarcoma - pathology / Clinical Decision-Making / Decision making / Disease / Epidemiology / Extremities - pathology / Female / Hazard identification / Histology / Humans / Kaplan-Meier Estimate / Male / Medical prognosis / Middle Aged / Multivariate Analysis / Nomograms / Nomography (Mathematics) / Patients / Population studies / Population-based studies / Prediction models / Prognosis / Radiation therapy / Regression analysis / Regression models / Software / Statistical analysis / Statistical models / Surgery / Survival / Survival Analysis / Training / Tumors / United States - epidemiology / Variables