摘要目的:探索基线PET代谢参数联合B淋巴细胞瘤－2(Bcl－2)/细胞－髓细胞瘤病毒癌基因(c－Myc)蛋白双表达(DE)在预测原发胃肠道弥漫性大B细胞淋巴瘤(PGI－DLBCL)患者危险度分层中的价值。方法:回顾性分析2011年3月至2019年11月间南京大学医学院附属鼓楼医院和南京医科大学第一附属医院74例经病理证实为PGI－DLBCL的患者(男33例、女41例，年龄20~87岁)，患者治疗前均接受基线PET/CT检查。利用SUV max≥2.5作为病灶边界进行自动勾画，计算肿瘤代谢体积(MTV)和病灶糖酵解总量(TLG)。利用免疫组织化学法分析患者Bcl－2及c－Myc蛋白表达；建立包含MTV和DE的预后预测模型，将患者分为3组：低危组(低MTV和非DE)、中危组(高MTV或DE)和高危组(高MTV和DE)。采用Kaplan－Meier生存分析、log－rank检验、多因素Cox比例风险回归模型对无进展生存(PFS)及总生存(OS)进行预后分析。 结果:74例患者中，20例复发或进展、13例死亡，29.7%(22/74)的患者DE阳性。多因素分析结果提示，MTV[风险比( HR)＝9.110，95% CI：1.429~18.615， P＝0.012]和DE( HR＝9.837，95% CI：1.690~57.260， P＝0.011)是PFS的独立预测因素，而MTV( HR＝12.470，95% CI：3.356~46.336， P<0.001)是OS的独立预测因素。所构建的PFS预后预测模型中，低危组( n＝42)与中危组( n＝20)的生存曲线差异有统计学意义( χ2＝7.84， P＝0.005)，中危组与高危组( n＝12)的生存曲线差异也有统计学意义( χ2＝18.72， P<0.001)。 结论:MTV和DE能够独立预测PGI－DLBCL患者的PFS，MTV能够独立预测OS。MTV联合DE构建的PFS预后预测模型能够很好地对患者进行危险度分层。

abstractsObjective:To explore whether baseline PET metabolic parameters combined with B-cell lymphoma-2 (Bcl-2)/cellular-myelocytomatosis viral oncogene (c-Myc) dual expression (DE) can improve the prognostic stratification of patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:From March 2011 to November 2019, 74 patients (33 males, 41 females; age: 20-87 years) pathologically diagnosed with PGI-DLBCL prior to treatment in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School and the First Affiliated Hospital of Nanjing Medical University were retrospectively included. Baseline PET/CT scans were calculated automatically using the boundaries of voxels presenting a SUV max≥2.5, and metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were determined. Expressions of Bcl-2 and c-Myc were detected at protein levels by immunohistochemistry (IHC). A predicting model comprised of MTV and DE was constructed and patients were divided into 3 groups, including low-risk group (low MTV and non-DE), mediate-risk group (high MTV or DE) and high-risk group (high MTV and DE). The distributions of progression-free survival (PFS) and overall survival (OS) rates were estimated using the Kaplan-Meier method, log-rank test and Cox proportional hazards model. Results:Of 74 patients, 20 relapsed or progressed, 13 died, and 29.7%(22/74) patients were DE positive. Multivariate analysis revealed that MTV (hazard ratio ( HR)=9.110, 95% CI: 1.429-18.615, P=0.012) and DE ( HR=9.837, 95% CI: 1.690-57.260, P=0.011) were independent predictors of PFS, while MTV ( HR=12.470, 95% CI: 3.356-46.336, P<0.001) was the only independent predictor of OS. In the predicting model for PFS, low-risk group ( n=42) and mediate-risk group ( n=20) exhibited significant difference ( χ2=7.84, P=0.005), and mediate-risk group and high-risk group ( n=12) also exhibited significant difference ( χ2=18.72, P<0.001). Conclusions:MTV and DE can independently predict PFS of patients with PGI-DLBCL, and MTV can independently predict OS. The predicting model for PFS combining MTV with DE may further improve the ability of clinicians to stratify patients in terms of differential prognoses.