Cancer Center, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Biomol Biomed. 2023 May 1;23(3):535-544. doi: 10.17305/bb.2022.8633.
Desmoplastic small round cell tumor (DSRCT) is a rare undifferentiated malignant soft tissue tumor with a poor prognosis and a lack of consensus on treatment. This study's objective was to build a nomogram based on clinicopathologic factors and an online survival risk calculator to predict patient prognosis and support therapeutic decision-making. A retrospective cohort analysis of the Surveillance, Epidemiology and End Results (SEER) database was performed for patients diagnosed with DSRCT between 2000 and 2019. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to identify the individual variables related to overall survival (OS) and cancer-specific survival (CSS), as well as to construct online survival risk calculators and nomogram survival models. The nomogram was employed to categorize patients into different risk groups, and the Kaplan-Meier method was utilized to determine the survival rate of each risk category. Propensity score matching (PSM) was used to assess survival with different therapeutic approaches. A total of 374 patients were included, and the median OS and CSS were 25 (interquartile range 21.9-28.1) months and 27 (interquartile range 23.6-30.3) months, respectively. The nomogram models demonstrated high predictive accuracy. PSM found that patients with triple-therapy had better CSS and OS than those who received surgery plus chemotherapy (median survival times: 49 vs 34 months and 49 vs 35 months, respectively). The nomogram successfully predicted the DSRCT patients survival rate. This approach could assist doctors in evaluating prognoses, identifying high-risk populations, and implementing personalized therapy.
促结缔组织增生性小圆细胞肿瘤(DSRCT)是一种罕见的未分化恶性软组织肿瘤,预后较差,治疗方法尚未达成共识。本研究旨在基于临床病理因素构建列线图和在线生存风险计算器,以预测患者的预后并为治疗决策提供支持。对 2000 年至 2019 年期间诊断为 DSRCT 的患者进行监测、流行病学和最终结果(SEER)数据库的回顾性队列分析。应用最小绝对收缩和选择算子(LASSO)Cox 回归分析确定与总生存(OS)和癌症特异性生存(CSS)相关的个体变量,并构建在线生存风险计算器和列线图生存模型。列线图用于将患者分为不同的风险组,并使用 Kaplan-Meier 方法确定每个风险组的生存率。采用倾向评分匹配(PSM)评估不同治疗方法的生存情况。共纳入 374 例患者,中位 OS 和 CSS 分别为 25(四分位距 21.9-28.1)个月和 27(四分位距 23.6-30.3)个月。列线图模型具有较高的预测准确性。PSM 发现三联疗法组的 CSS 和 OS 均优于手术加化疗组(中位生存时间:49 与 34 个月,49 与 35 个月)。列线图成功预测了 DSRCT 患者的生存率。该方法可帮助医生评估预后、识别高危人群,并实施个性化治疗。