扩散张量成像各向异性参数对乳腺恶性肿瘤的鉴别诊断价值

Anisotropic parameters of diffusion tensor imaging in breast： a preliminary study for detection and differentiation of malignant tumors

目的 探讨DTI参数对乳腺恶性肿瘤的鉴别诊断价值.方法 回顾性分析经手术病理证实的54例乳腺病变患者资料,其中恶性肿瘤33例,良性病变21例,共54个病变.患者行DTI检查,测量乳腺恶性肿瘤组织、对侧正常乳腺组织和良性肿瘤组织的最大本征张量值（E1）、平均扩散率（MD）和各向异性分数（FA）.采用配对￡检验和独立样本t检验分别比较恶性肿瘤与健侧正常乳腺组织、恶性肿瘤与良性肿瘤组织上述参数的差异；以E1、FA、MD作为预测变量,分别进行Logistic 回归分析,预测最佳回归模型；采用ROC曲线分析E1、FA、MD和回归模型对乳腺恶性肿瘤的鉴别能力.结果 恶性肿瘤组织的E1、MD和FA值分别为（0.99 ±0.12）×10-3mm2/s、（0.85 ±0.26）×10-3mm2/s和0.20 ±0.08,健侧正常乳腺组织分别为（1.46±0.55）×10-3mm2/s、（1.48±0.44）×10-3 mm2/s和0.29±0.17,良性肿瘤组织分别为（1.80±0.42）×10-3 mm2/s、（1.38±0.52）×10-3mm2/s和0.22 ±0.10;恶性肿瘤组织和正常乳腺组织的上述参数差异均有统计学意义（t值分别为-4.889、-6.449和-2.842,P值均＜0.01）；良恶性肿瘤组织的E1、MD差异有统计学意义（t值分别为-10.476和-4.394,P值均＜0.01）,而FA值差异无统计学意义（P＞0.05）.E1、MD和FA值均是鉴别乳腺恶性肿瘤与正常乳腺组织的独立预测因素,回归模型鉴别乳腺恶性肿瘤和正常组织的敏感度、特异度和准确度分别为97.0％ （32/33）、97.0％（32/33）和97.0％（64/66）,高于其他DTI参数.E1和回归模型鉴别病变良恶性的诊断效能最高,诊断的敏感度、特异度和准确度均为97.0％（32/33）、100.0％（21/21）和98.1％（53/54）.结论 将E1、MD和FA结合的回归模型对于乳腺恶性肿瘤的诊断价值最高,而E1是鉴别肿瘤良恶性的首选指标.

Objective To investigate the diagnostic value of DTI anisotropy parameters in breast malignant tumors.Methods Fifty four patients,including 33 patients with malignant tumors and 21 patients with benign lesions,were retrospectively analyzed.The E1,MD and FA of lesions were measured and compared by paired t test between the malignant tumors and the contralateral healthy breast tissue.The difference between malignant tumors and benign lesions was analyzed by independent sample t test.Logistic regression analysis was made using E1,FA,MD as predictors in detecting and differentiating the malignant tumors,ROC curve analysis was performed to compare diagnostic performance based on the area under the curve （AUC）.Results E1,MD and FA in malignant tumors were （0.99 ± 0.12） × 10-3mm2/s,（0.85 ±0.26） × 10-3mm2/s and 0.20 ±0.08 respectively,and those in normal breast tissues were（1.46 ± 0.55） × 10-3 mm2/s、（1.48 ± 0.44） × 10-3 mm2/s and 0.29 ± 0.17 respectively.Those parameters in benign lesions were （1.80 ±0.42） × 10-3mm2/s,（1.38 ±0.52） × 10-3mm2/s and 0.22 ± 0.10 respectively.Significant statistic differences were found between malignant tumors and normal breast tissues in E1,MD and FA （t =-4.889,-6.449,-2.842 ; P 〈 0.01）.Significant statistic differences were also found between malignant tumors and benign lesions in E1 and MD （t =-10.476,-4.394; P 〈 0.01） with no difference found in FA （P 〉 0.05）.E1,MD and FA are independent predictors in malignant tumors＇ detection,and the combination of E1,MD and FA significantly improved discrimination between cancer and normal tissue over each one alone with the sensitivity 97.0% （32/33）,specificity 97.0% （32/33）,accuracy 97.0% （64/66）.Combination of E1 and MD had a similar AUC with E1 and a more AUC than MD and FA,with the sensitivity 97.0% （32/33）,specificity 100.0% （21/21）,accuracy 98.1% （53/54）.Conclusion The regression model combining E1,MD and FA is most valuable in breast cancer detection and E1 is the preferred index for the differentiation of breast cancers from benigin lesions.