A novel nomogram model to predict the overall survival of patients with retroperitoneal leiomyosarcoma: a large cohort retrospective study

Abstract

Retroperitoneal leiomyosarcomas (RLS) are the second most common type of retroperitoneal sarcoma and one of the most aggressive tumours. The lack of early warning signs and delay in regular checkups lead to a poor prognosis. This study aims to create a nomogram to predict RLS patients' overall survival (OS). Patients diagnosed with RLS in the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were enrolled in this study. First, univariable and multivariable Cox regression analyses were used to identify independent prognostic factors, followed by constructing a nomogram to predict patients' OS at 1, 3, and 5 years. Secondly, the nomogram's distinguishability and prediction accuracy were assessed using receiver operating characteristic (ROC) and calibration curves. Finally, the decision curve analysis (DCA) investigated the nomogram's clinical utility. The study included 305 RLS patients, and they were divided into two groups at random: a training set (216) and a validation set (89). The training set's multivariable Cox regression analysis revealed that surgery, tumour size, tumour grade, and tumour stage were independent prognostic factors. ROC curves demonstrated that the nomogram had a high degree of distinguishability. In the training set, area under the curve (AUC) values for 1, 3, and 5 years were 0.800, 0.806, and 0.788, respectively, while in the validation set, AUC values for 1, 3, and 5 years were 0.738, 0.780, and 0.832, respectively. As evidenced by the calibration curve, the nomogram had high prediction accuracy. Moreover, DCA revealed that the nomogram had high clinical utility. Furthermore, the risk stratification system based on the nomogram could effectively categorise patients into three mortality risk subgroups. Therefore, the developed nomogram and risk stratification system may aid in optimising the treatment decisions of RLS patients to improve treatment prognosis and maximise their healthcare outcomes.

Figure 1

The flowchart of patient selection.

Figure 2

This prognostic nomogram predicts 1, 3, and 5 years OS probability in retroperitoneal leiomyosarcoma patients. Specifically, when a patient with primary retroperitoneal leiomyosarcoma comes to the clinic room for consulting his or her OS probability, we can sum each point of the above four independent prognostic factors to obtain a total point and draw a vertical line from total points row to the bottom timeline to obtain his or her mortality rate at the corresponding time. The survival probability at the corresponding time can be obtained by subtracting the mortality rate from 1. For example, a retroperitoneal leiomyosarcoma patient with a 100 mm localized metastasis and Grade III tumor and underwent surgery. The corresponding nomogram total points of this patients is 87 (100 mm) + 81 (Grade III) + 61 (Localized metastasis) + 0 (Underwent surgery) = 229, and his or her mortality rate at 1, 3, and 5 years were 10.6%, 35%, and 51.7%, respectively, while his or her corresponding OS probabilities at 1, 3, and 5 years were 89.4%, 65%, and 48.3%. Figure was generated by R software version 4.03 (http://www.r-project.org/).

Figure 3

Calibration curves of the nomogram for predicting 1, 3, and 5 years OS in the training set (A–C) and 1, 3, and 5 years OS in the validation set (D–F). Figure was generated by R software version 4.03 (http://www.r-project.org/).

Figure 4

ROC curves for OS prediction of patients with retroperitoneal leiomyosarcomas. ROC curves of 1, 3, and 5 years in the training set (A), and ROC curves of 1, 3, and 5 years in the validation set (B) in this cohort retrospective study. Figure was generated by R software version 4.03 (http://www.r-project.org/).

Figure 5

The comparison of the prediction accuracy between the nomogram and independent prognostic predictors. The ROC curves of nomogram and all independent prognostic predictors at 1 (A), 3 (B), and 5 (C) years in the training set and at 1 (D), 3 (E), and 5 (F) years in the validation set. Figure was generated by R software version 4.03 (http://www.r-project.org/).

Figure 6

DCA of the nomogram for predicting the 1 (A), 3 (B) and 5 (C) year OS in the training set and the 1 (D), 3 (E) and 5 (F) year OS in the validation set. Figure was generated by R software version 4.03 (http://www.r-project.org/).

Figure 7

Kaplan–Meier survival analysis for the training (A) and validation sets (B) in this cohort retrospective study. Figure was generated by R software version 4.03 (http://www.r-project.org/).