Predictors and outcomes of recurrent retroperitoneal liposarcoma: new insights into its recurrence patterns

医学 病态的 比例危险模型 列线图 多元分析 外科肿瘤学 内科学 脂肪肉瘤 肿瘤科 单变量分析 生存分析 多元统计 病理 肉瘤 统计 数学
作者
Huan Deng,Jingwang Gao,Xingming Xu,Gui‐Bin Liu,Liqiang Song,Yisheng Pan,Bo Wei
出处
期刊:BMC Cancer [BioMed Central]
卷期号:23 (1) 被引量:2
标识
DOI:10.1186/s12885-023-11586-8
摘要

Abstract Background The clinical profiles of recurrent retroperitoneal liposarcoma (RLS) need to be explored. The recurrence patterns of RLS are controversial and ambiguous. Methods A total of 138 patients with recurrent RLS were finally recruited in the study. The analysis of overall survival (OS) and recurrence-free survival (RFS) was performed by Kaplan‒Meier analysis. To identify independent prognostic factors, all significant variables on univariate Cox regression analysis ( P ≤ 0.05) were subjected to multivariate Cox regression analysis. The corresponding nomogram model was further built to predict the survival status of patients. Results Among patients, the 1-, 3-, and 5-year OS rates were 70.7%, 35.9% and 30.9%, respectively. The 1-, 3- and 5-year RFS rates of the 55 patients who underwent R0 resection were 76.1%, 50.8% and 34.4%, respectively. The multivariate analysis revealed that resection method, tumor size, status of pathological differentiation, pathological subtypes and recurrence pattern were independent risk factors for OS or RFS. Patients with distant recurrence (DR) pattern usually had multifocal tumors (90.5% vs. 74.7%, P < 0.05); they were prone to experience changes of pathological differentiation (69.9% vs. 33.3%, P < 0.05) and had a better prognosis than those with local recurrence (LR) pattern. R0 resection and combined organ resection favored the survival of patients with DR pattern in some cases. Conclusions Patients with DR pattern had better prognosis, and they may benefit more from aggressive combined resection than those with LR pattern. Classifying the recurrence patterns of RLS provides guidance for individualized clinical management of recurrent RLS.
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