Early distinction of lymph node metastasis in patients with soft tissue sarcoma and individualized survival prediction using the online available nomograms: A population-based analysis - Tong, Yuexin; Pi, Yangwei; Cui, Yuekai; Jiang, Liming; Gong, Yan; Zhao, Dongxu (Frontiers in Oncology, 2022)
Background: This study aimed to construct some predictive models to quantify the probability of lymph node metastasis (LNM) and survival rate of soft tissue sarcoma (STS) patients with LNM. Methods: Research data was extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017, and data for STS patients from our medical institution was collected to form an external testing set. Univariate and multivariate logistics regression analysis were used to determine the independent risk factors. Based on identified variables, we developed a diagnostic nomogram to predict the risk of LNM in STS patients. Those STS patients presented with LNM were further formed a new cohort for identifying dependent prognostic factors using univariate and multivariate Cox regression analysis, then, two nomograms incorporating predictors was developed to predict the overall survival(OS) and cancer specific survival (CSS) for STS patients with LNM. Kaplan–Meier (K-M) survival analysis was conducted to study the survival difference. Besides, validations of these nomograms were performed by receiver operating characteristic (ROC) curves, the area under curves (AUC), calibration curves and decision curve analysis (DCA). Results: A total of 16601 STS patients from SEER database were enrolled into our study, of which 659 (3.97%) had LNM at the initial diagnosis. Kaplan–Meier (K-M) survival analysis indicated that patients with LNM had poorer survival rate. Sex, histology, primary site, grade, M stage and T stage were found to be independently related with development of LNM in STS patients. Age, grade, histology, M stage, T stage, chemotherapy, radiotherapy and surgery were identified as independent prognostic factors for OS of STS patients with LNM, and age, grade, M stage, T stage, radiotherapy and surgery were determined as independent prognostic factors for CSS. Subsequently, three nomograms and their online versions. ROC curves, calibration curves and DCA suggested the excellent perfomance in predicion of LNM in STS patients and survival for patients with LNM. Conclusion: These newly proposed nomograms promise to be useful tools in predicting the risk of LNM for STS patients and individualized survival prediction for STS patients with LNM, which may help to guide clinical practice.