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结外自然杀伤/T 细胞淋巴瘤,鼻型的新预后模型。

New prognostic model for extranodal natural killer/T cell lymphoma, nasal type.

机构信息

Department of Medical Oncology, Sun Yat-Sen University Cancer Center, 651 Dong Feng RD East, Guangzhou, 510060, People's Republic of China,

出版信息

Ann Hematol. 2014 Sep;93(9):1541-9. doi: 10.1007/s00277-014-2089-x. Epub 2014 Apr 30.

Abstract

Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis, requiring risk stratification in affected patients. We designed a new prognostic model specifically for ENKTL to identify high-risk patients who need more aggressive therapy. We retrospectively reviewed 158 patients who were newly diagnosed with ENKTL. The estimated 5-year overall survival rate was 39.4 %. Independent prognostic factors included total protein (TP) <60 g/L, fasting blood glucose (FBG) >100 mg/dL, and Korean Prognostic Index (KPI) score ≥2. We constructed a new prognostic model by combining these prognostic factors: group 1 (64 cases (41.0 %)), no adverse factors; group 2 (58 cases (37.2 %)), one adverse factor; and group 3 (34 cases (21.8 %)), two or three adverse factors. The 5-year overall survival (OS) rates of these groups were 66.7, 23.0, and 5.9 %, respectively (p < 0.001). Our new prognostic model had a better prognostic value than did the KPI model alone (p < 0.001). Our proposed prognostic model for ENKTL, including the newly identified prognostic indicators, TP and FBG, demonstrated a balanced distribution of patients into different risk groups with better prognostic discrimination compared with the KPI model alone.

摘要

结外 NK/T 细胞淋巴瘤,鼻型(ENKTL)是一种侵袭性疾病,预后不良,需要对受影响的患者进行风险分层。我们设计了一种新的预后模型,专门用于识别需要更积极治疗的高危患者。我们回顾性分析了 158 例新诊断为 ENKTL 的患者。估计的 5 年总生存率为 39.4%。独立的预后因素包括总蛋白(TP)<60g/L、空腹血糖(FBG)>100mg/dL 和韩国预后指数(KPI)评分≥2。我们通过结合这些预后因素构建了一个新的预后模型:组 1(64 例(41.0%)),无不良因素;组 2(58 例(37.2%)),有一个不良因素;组 3(34 例(21.8%)),有两个或三个不良因素。这些组的 5 年总生存率(OS)分别为 66.7%、23.0%和 5.9%(p<0.001)。我们的新预后模型比 KPI 模型具有更好的预后价值(p<0.001)。我们提出的用于 ENKTL 的预后模型,包括新确定的预后指标 TP 和 FBG,与单独的 KPI 模型相比,患者在不同风险组中的分布更均衡,预后预测能力更好。

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