查看更多>>摘要：目的 探讨基于扩散峰度成像（DKI）参数的回归模型预测雌激素受体（ER）阳性、人表皮生长因子受体2（HER-2）阴性早期浸润性乳腺癌患者的复发风险。 方法 该研究为横断面研究。回顾性分析2016年1月至2018年12月在无锡市人民医院接受诊治的ER阳性、HER-2阴性早期浸润性乳腺癌患者50例（50个病灶）的临床病理（年龄、组织学分级、Ki-67水平等）及影像资料。所有患者均为女性，年龄29~81岁，术前均接受常规MRI及DKI检查，评估记录乳腺纤维腺体组织量（FGT）、背景实质强化（BPE）、内部强化特征等，测量并计算强化峰值（PH）、峰值强化率、达峰时间、平均峰度（MK）、平均扩散率（MD）等指标。根据患者21基因复发评分将50例患者分为低复发风险组和中高复发风险组。采用独立样本t检验、Mann-Whitney U检验、χ2检验比较2组间各指标的差异。将年龄、PH、MD、MK为自变量构建的logistic模型为Pre1，以Ki-67、年龄、PH、MD、MK为自变量构建logistic模型为Pre2，采用受试者操作特征曲线及曲线下面积（AUC）评估模型预测患者低复发风险的效能。 结果 低复发风险组25例、中高复发风险组25例。低复发风险组与中高复发风险组间年龄、FGT、PH、MD、MK、Ki-67差异具有统计学意义（P均<0.05），其他指标差异均无统计学意义（P均>0.05）。Pre1预测ER阳性、HER-2阴性早期浸润性乳腺癌低复发风险的AUC为0.87，灵敏度为0.76，特异度为0.88。Pre2预测ER阳性、HER-2阴性早期浸润性乳腺癌低复发风险的AUC为0.92，灵敏度为0.84，特异度为0.92。 结论 基于DKI的多参数模型预测可有效预测ER阳性、HER-2阴性乳腺癌复发风险，联合Ki-67可进一步提高预测效能，有效识别低复发风险患者。 Objective To explore the predictive value of a regression model based on diffusion kurtosis imaging (DKI) parameters for prediction of the recurrence risk in patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER-2)-negative early invasive breast cancer. Methods A retrospective cross-sectional study was designed. The clinicopathological (age, histological grade, Ki-67 level, etc.) and imaging data of 50 patients (50 lesions) with ER-positive, HER-2 negative early invasive breast cancer who underwent treatment at Wuxi People′s Hospital from January 2016 to December 2018 were retrospectively analyzed. All patients were female, aged 29 to 81 years, and underwent pre-operation conventional MRI and DKI examinations. The volume of breast fibroglandular tissue (FGT), background parenchymal enhancement (BPE), and internal enhancement features were recorded the peak enhancement (PH), peak enhancement rate, time to peak, mean kurtosis (MK), and mean diffusivity (MD) were calculated. Based on the 21-gene recurrence risk scores, patients were divided into low recurrence risk group and medium-high recurrence risk group. Independent sample t test, Mann-Whitney U test, χ2 test were used to compare the differences of various indicators between the two groups. Two logistic models were constructed with age, PH, MD, and MK as independent variables (Pre1), and with Ki-67, age, PH, MD, and MK as independent variables (Pre2), respectively. The efficacy of the models in predicting low recurrence risk in patients was assessed using receiver operating characteristic curve and area under the curve (AUC). Results There were 25 cases in the low recurrence risk group and 25 cases in the medium-high recurrence risk group. The differences in age, FGT, PH, MD, MK, and Ki-67 between the low recurrence risk group and the medium-high recurrence risk group were statistically significant (all P<0.05), while other indexes showed no statistically significant differences (allP>0.05). The AUC of Pre1 in predicting low recurrence risk of ER-positive, HER-2 negative early invasive breast cancer was 0.87, with a sensitivity of 0.76 and specificity of 0.88. The AUC of Pre2 for predicting the low recurrence risk of ER-positive, HER-2 negative early invasive breast cancer was 0.92, with a sensitivity of 0.84, and specificity of 0.92. Conclusions A multi-parameter model based on DKI can effectively predict the recurrence risk of ER-positive and HER-2 negative breast cancer. The model with combination of Ki-67 can further improve the predictive efficacy, and help effectively identify patients at low recurrence risk.
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