CAJ | 학술논문

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.
목적 탐토DTI삼수대유선악성종류적감별진단개치.방법 회고성분석경수술병리증실적54례유선병변환자자료,기중악성종류33례,량성병변21례,공54개병변.환자행DTI검사,측량유선악성종류조직、대측정상유선조직화량성종류조직적최대본정장량치(E1)、평균확산솔(MD)화각향이성분수(FA).채용배대￡검험화독립양본t검험분별비교악성종류여건측정상유선조직、악성종류여량성종류조직상술삼수적차이；이E1、FA、MD작위예측변량,분별진행Logistic 회귀분석,예측최가회귀모형；채용ROC곡선분석E1、FA、MD화회귀모형대유선악성종류적감별능력.결과 악성종류조직적E1、MD화FA치분별위(0.99 ±0.12)×10-3mm2/s、(0.85 ±0.26)×10-3mm2/s화0.20 ±0.08,건측정상유선조직분별위(1.46±0.55)×10-3mm2/s、(1.48±0.44)×10-3 mm2/s화0.29±0.17,량성종류조직분별위(1.80±0.42)×10-3 mm2/s、(1.38±0.52)×10-3mm2/s화0.22 ±0.10;악성종류조직화정상유선조직적상술삼수차이균유통계학의의(t치분별위-4.889、-6.449화-2.842,P치균＜0.01)；량악성종류조직적E1、MD차이유통계학의의(t치분별위-10.476화-4.394,P치균＜0.01),이FA치차이무통계학의의(P＞0.05).E1、MD화FA치균시감별유선악성종류여정상유선조직적독립예측인소,회귀모형감별유선악성종류화정상조직적민감도、특이도화준학도분별위97.0％ (32/33)、97.0％(32/33)화97.0％(64/66),고우기타DTI삼수.E1화회귀모형감별병변량악성적진단효능최고,진단적민감도、특이도화준학도균위97.0％(32/33)、100.0％(21/21)화98.1％(53/54).결론 장E1、MD화FA결합적회귀모형대우유선악성종류적진단개치최고,이E1시감별종류량악성적수선지표.
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.