Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System

Abstract

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

Keywords: Cox regression analysis; Epidemiology; Surveillance; and End Results; logistics regression analysis; nomogram; soft tissue functionality; surgeries.

Figure 1.

The process of patient selection and the study flow.

Figure 2.

Nomogram for predicting the risk of lung metastasis (LM) from soft tissue sarcoma (STS) patients.

Figure 3.

The receiver operating characteristic curve (A), calibration curve (B), and decision curve analysis (C) of the training set; the receiver operating characteristic curve (D), calibration curve (E), and decision curve analysis (F) of the testing set.

Figure 4.

Comparison of the area under the receiver operating characteristic curve between the nomogram and each independent predictor in the training set (A) and the testing set (B).

Figure 5.

Prognostic nomogram for predicting 1-, 2-, and 3-year overall survival (OS) for soft tissue sarcoma (STS) patients with lung metastasis (LM).

Figure 6.

The receiver operating characteristic curve in the training set (A) and in the testing set (B).

Figure 7.

The calibration curves of the nomogram in the training set (A) and in the testing set (C); the decision curve analysis of the nomogram in the training set (B) and in the testing set (D).

Figure 8.

Kaplan-Meier survival curve analysis of the training set (A) and testing set (B); Kaplan-Meier survival status analysis of the training set (C) and testing set (D).