Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study

Abstract

BackgroundRetroperitoneal liposarcomas (RPLs), sarcoma of mesenchymal origin, are the most common soft tissue sarcomas (STS) of the retroperitoneum. Given the rarity of RPLs, the prognostic values of clinicopathological features in the patients remain unclear. The nomogram can provide a visual interface to aid in calculating the predicted probability that a patient will achieve a particular clinical endpoint and communication with patients.MethodsWe included a total of 1,392 RPLs patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. For nomogram construction and validation, patients in the SEER database were divided randomly into the training cohort and internal validation cohort at a ratio of 7:3, while 65 patients with RPLs from our center between 2010 and 2016 served as the external validation cohort. The OS curves were drawn using the Kaplan–Meier method and assessed using the log-rank test. Moreover, Fine and Gray’s competing-risk regression models were conducted to assess CSS. Univariate and multivariate analyses were performed to select the prognostic factors for survival time. We constructed a predictive nomogram based on the results of the multivariate analyses.ResultsThrough univariate and multivariate analyses, it is found that age, histological grade, classification, SEER stage, surgery constitute significant risk factors for OS, and age, classification, SEER stage, AJCC M stage, surgery, and tumor size constitute risk factors for CSS. We found that the nomogram provided a good assessment of OS and CSS at 1, 3, and 5 years in patients with RPLs (1-year OS: (training cohort: AUC = 0.755 (95% CI, 0.714, 0.796); internal validation cohort: AUC = 0.754 (95% CI, 0.681, 0.827); external validation cohort: AUC = 0.793 (95% CI, 0.651, 0.935)); 3-year OS: (training cohort: AUC = 0.782 (95% CI, 0.752, 0.811); internal validation cohort: AUC = 0.788 (95% CI, 0.736, 0.841); external validation cohort: AUC = 0.863 (95% CI, 0.773, 0.954)); 5-year OS: (training cohort: AUC = 0.780 (95% CI, 0.752, 0.808); internal validation cohort: AUC = 0.783 (95% CI, 0.732, 0.834); external validation cohort: AUC = 0.854 (95% CI, 0.762, 0.945)); 1-year CSS: (training cohort: AUC = 0.769 (95% CI, 0.717, 0.821); internal validation cohort: AUC = 0.753 (95% CI, 0.668, 0.838); external validation cohort: AUC = 0.799 (95% CI, 0.616, 0.981)); 3-year CSS: (training cohort: AUC = 0.777 (95% CI, 0.742, 0.811); internal validation cohort: AUC = 0.787 (95% CI, 0.726, 0.849); external validation cohort: AUC = 0.808 (95% CI, 0.673, 0.943)); 5-year CSS: (training cohort: AUC = 0.773 (95% CI, 0.741, 0.805); internal validation cohort: AUC = 0.768 (95% CI, 0.709, 0.827); external validation cohort: AUC = 0.829 (95% CI, 0.712, 0.945))). The calibration plots for the training, internal validation, and external validation cohorts at 1-, 3-, and 5-year OS and CSS indicated that the predicted survival rates closely correspond to the actual survival rates.ConclusionWe constructed and externally validated an unprecedented nomogram prognostic model for patients with RPLs. The nomogram can be used as a potential, objective, and supplementary tool for clinicians to predict the prognosis of RPLs patients around the world.

A competing risk-based nomogram to predict cancer-specific survival in patients with retroperitoneal leiomyosarcoma

“…Among all 578 deaths in this study, 28.7% occurred due to causes other than RLMS, which accounts for a great part of total deaths. To avoid overestimating the risk of disease-related deaths, Fine & Gray's method was employed to develop our nomogram for predicting CSS of RLMS, which was also applied by Nazzani et al for surgically treated RPS [ 4 ] and Li et al for retroperitoneal liposarcoma [ 20 ]. After univariate analysis of CSS, five predictors with significance were identified, including grade, tumor size, tumor range, chemotherapy and surgery status.…”

“…However, nomograms to predict the probability of CSS for RLMS patients have not yet been constructed. Compared with traditional Cox analysis, the Fine & Gray method [ 19 ] is more accurate in survival analysis for considering the existence of competing events and has been applied in various tumors, including retroperitoneal liposarcoma [ 20 , 21 ].…”

Development and validation of a prognostic model to predict the prognosis of patients with colorectal gastrointestinal stromal tumor: A large international population-based cohort study

“…However, they used the 5-cm threshold recommended by the AJCC staging system ( 6 , 38 ), whereas the thresholds (6.5-cm) we used were derived from an analysis based on a large database using X-tile software version 3.6.1 (Yale University School of Medicine, US) ( 22 ). Several studies have revealed that the AJCC T-classification system should be interpreted with caution because it still has limited predictive value for the prognosis of multiple tumors ( 50 , 51 ). Therefore, it was necessary to investigate the threshold of tumor size for each type of tumor in greater detail.…”

Correlation Analysis Between Demographic, Surgical, and Pathological Characteristics with Local Recurrence‐Free Survival for Surgical Resected Retroperitoneal Liposarcoma

“…As the most common tissue type of retroperitoneal malignancy, retroperitoneal liposarcoma (RPLS) originates from the adipose tissue in the retroperitoneal space. Owing to the special anatomical structure and high malignancy, RPLS cases are often accompanied by insidious clinical symptoms and rapid progression [1][2][3]. Therefore, most RPLS neoplasms are huge and have a complex relationship with adjacent organs, making the operation difficult and often requiring combined organ resection.…”