【摘要】 目的 探讨¹⁸F-氟代脱氧葡萄糖正电子发射计算机断层显像(¹⁸F-FDG PET/CT)对乳腺癌预后的预测价值。方法 回顾性分析江门市中心医院2012年10月至2023年12月期间于治疗前进行¹⁸F-FDG PET/CT检查的87例乳腺癌患者的临床资料，根据随访结果划分为进展组（n=34）和无进展组（n=53），采用单因素和多因素Cox比例风险回归模型，分析与乳腺癌进展相关的因素。结果 单因素分析结果显示，两组的癌胚抗原（CEA）阳性率、临床TNM分期、原发灶最大径、原发灶代谢肿瘤体积（MTV）、原发灶总病变糖酵解（TLG）、全身总代谢肿瘤体积（TMTV）、全身总病变糖酵解（TTLG）对比，有统计学差异（P<0.05）。多因素Cox回归分析显示，临床TNM分期[HR（95%CI）=3.109（1.549～6.241）]、原发灶最大径[HR（95%CI）=1.178（1.042～1.330）]、 TMTV[HR（95%CI）=1.002（1.001～1.004）]是乳腺癌疾病进展的独立危险因素（P<0.05）。结论 临床TNM分期以及¹⁸F-FDG PET/CT参数（原发灶最大径、 TMTV）可有效预测乳腺癌进展风险，为临床及时制定治疗方案提供依据。更多还原

【Abstract】 Objective To investigate the prognostic predictive value of¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography(¹⁸F-FDG PET/CT) in breast cancer. Methods The clinical data of 87 patients with breast cancer who underwent pretreatment¹⁸F-FDG PET/CT examination at Jiangmen Central Hospital from October 2012 to December 2023 were retrospectively analyzed. The patients were divided into the disease progression group（n = 34） and the progression-free group（n = 53）according to follow-up results. Univariate and multivariate Cox proportional hazards regression models were used to analyze the factors associated with breast cancer progression. Results Univariate analysis showed that there were statistically significant differences in the positive rate of carcinoembryonic antigen（CEA）, clinical TNM staging, maximum diameter of the primary lesion, metabolic tumor volume（MTV） of the primary lesion, total lesion glycolysis（TLG） of the primary lesion, total metabolic tumor volume（TMTV）, and total tumor lesion glycolysis（TTLG） between the two groups（P <0.05）. Multivariate Cox regression analysis showed that clinical TNM staging [HR（95%CI） = 3.109（1.549 ～ 6.241）], maximum diameter of the primary lesion [HR（95%CI） = 1.178（1.042 ～ 1.330）], and TMTV [HR（95%CI） = 1.002（1.001 ～ 1.004）] were independent risk factors for disease progression in breast cancer（P <0.05）. Conclusions Clinical TNM staging and¹⁸F-FDG PET/CT parameters（maximum diameter of the primary lesion, TMTV） can effectively predict the risk of disease progression in breast cancer, and provide a basis for the timely formulation of clinical treatment strategies.更多还原