超声征象评价在卵巢良恶性肿瘤鉴别诊断价值的Logistic回归分析
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摘要
目的:建立基于超声征象预测卵巢良恶性肿瘤的Logistic回归模型,并探讨该预测模型在鉴别诊断卵巢良恶性肿瘤中的应用价值。方法:回顾性收集重庆市渝北区人民医院2013年1月至2016年3月经手术病理证实的189例卵巢肿瘤患者,根据病理结果分为良性组(120例)和恶性组(69例),比较两组彩色多普勒超声各项指标特征,以病理诊断作为金标准,建立Logistic回归预测模型,计算预测模型准确率、灵敏度、特异度等指标,绘制ROC曲线并计算曲线下面积。结果:单因素及多因素Logistic回归分析结果显示:形态(OR=7.149)、内部回声(OR=7.085)、血流(OR=8.908)、RI(OR=13.224)是卵巢良恶性肿瘤鉴别诊断的主要超声影像特征指标。Logistic回归模型对卵巢良恶性肿瘤的预测正确率为93.7%(177/189),灵敏度92.5%(111/120),特异度95.7%(66/69),阳性预测价值97.4%(111/114),阴性预测价值88.0%(66/75)。ROC曲线下面积为0.945±0.019,P<0.001,95%CI:0.910～0.976。结论:基于超声征象的Logistic预测模型对于鉴别卵巢良恶性肿瘤具有较高的价值,可用于指导临床实践。
Objective: To establish a Logistic regression model to predict benign and malignant ovarian tumors based on ultrasound images, and evaluate the value of Logistic model in the differential diagnosis of benign and malignant ovarian tumors. Methods: We retrospectively selected 189 cases of ovarian tumors at The People's Hospital of Yubei District in Chongqing from January 2013 to March 2016. Among them, 69 cases was malignant and 120 cases was benign. Ultrasound features of benign and malignant ovarian tumors were compared. With pathologic diagnosis as gold standard, a Logistic model was established to calculate the accuracy, sensitivity, specificity and other indicators of the prediction model. A receiver operating characteristic(ROC) curve was drawn to calculate the area under the curve. Results: Univariate and multivariate Logistic regression analysis showed that morphology(OR=7.149), internal echo(OR=7.085), blood flow(OR=8.908) and RI(OR=13.224) were the main ultrasonographic features in differential diagnosis of benign and malignant ovarian tumors. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of Logistic regression model were 93.7%(177/189), 92.5%(111/120), 95.7%(66/69), 97.4%(111/114) and 88%(66/75), respectively. The area under the ROC curve was 0.945±0.019(P<0.001, 95%CI: 0.910～0.976). Conclusion: Logistic model based on ultrasound features for the differential diagnosis of benign and malignant ovarian tumors is highly valuable, and can be used to guide clinical practice.
Objective: To establish a Logistic regression model to predict benign and malignant ovarian tumors based on ultrasound images, and evaluate the value of Logistic model in the differential diagnosis of benign and malignant ovarian tumors. Methods: We retrospectively selected 189 cases of ovarian tumors at The People's Hospital of Yubei District in Chongqing from January 2013 to March 2016. Among them, 69 cases was malignant and 120 cases was benign. Ultrasound features of benign and malignant ovarian tumors were compared. With pathologic diagnosis as gold standard, a Logistic model was established to calculate the accuracy, sensitivity, specificity and other indicators of the prediction model. A receiver operating characteristic(ROC) curve was drawn to calculate the area under the curve. Results: Univariate and multivariate Logistic regression analysis showed that morphology(OR=7.149), internal echo(OR=7.085), blood flow(OR=8.908) and RI(OR=13.224) were the main ultrasonographic features in differential diagnosis of benign and malignant ovarian tumors. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of Logistic regression model were 93.7%(177/189), 92.5%(111/120), 95.7%(66/69), 97.4%(111/114) and 88%(66/75), respectively. The area under the ROC curve was 0.945±0.019(P<0.001, 95%CI: 0.910～0.976). Conclusion: Logistic model based on ultrasound features for the differential diagnosis of benign and malignant ovarian tumors is highly valuable, and can be used to guide clinical practice.
引文
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[12] 潘玉萍,蔡爱露,赵丹,等. 卵巢腺纤维瘤及囊性腺纤维瘤的超声特征[J]. 中国医学影像技术,2012,28(9):1699-1701.
[13] 陈蕾. 彩色多普勒超声对卵巢妊娠的诊断价值及效果观察[J].中国继续医学教育,2017,9(11):81-82.
[14] 张芳,张周龙. 超声检查联合肿瘤标志物检测对卵巢良恶性肿瘤诊断意义研究[J]. 中国妇幼保健,2015,30(31):5482-5485.
[15] U Menon, A Gentry-Maharaj , R Hallett ,et al. Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) [J]. Lancet Oncol, 2009, 10 (4) :327-340.
[16] Timmerman D. Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group[J].Ultrasound Obstet Gynecol,2010,36(2):226.
[17] Alcazar LJ. Tumor angiogenesis assessed by three-dimensional power Doppler ultrasound in early, advanced and metastatic ovarian cancer: a preliminary study[J]. Ultrasound Obstet Gynecol,2006,28(3) :325-329.
[18] Aguirre A, Ardeshirpour Y, Sanders MM,et al. Potential role of coregistered photoacoustic and ultrasound imaging in ovarian cancer detection and characterization[J]. Trans Oncol,2011,4(1) :29.
[2] Jemal A, Siegel R, Xu L, et al. Cancer statistics 2010[J]. CA Cancer J Clin, 2010, 60(5):277-230.
[3] 尹善德,王明凯,王蔼明. 卵巢癌筛查的研究进展[J]. 中华临床医师杂志(电子版),2012,6(23):7691-7694.
[4] 龚巍巍,罗胜兰,胡如英,等. 2005-2010年浙江省女性乳腺癌、宫颈癌与卵巢癌生存率分析[J]. 中华预防医学杂志,2014,48(5):366-369.
[5] 陈铃,袁颂华,石小红. 恶性风险指数、超声特征及CA125对上皮性卵巢癌的预测价值[J]. 暨南大学学报(自然科学与医学版),2012,33(6):633-637.
[6] 刘恩令,周玉秀,李向佩,等. 卵巢癌新辅助化疗监测过程中血清CA125联合经阴道彩色多普勒超声的应用价值[J]. 临床超声医学杂志,2015,17(10):680-682.
[7] 杨钦涵,刘慧,向红. 靶向超声造影剂对卵巢癌新生血管的评价[J].中国超声医学杂志,2014,30(4):368-371.
[8] 张宏涛 ,朱熠 ,张国楠,等. CD44+/MyD88+卵巢癌转移、耐药及预后的研究进展[J].肿瘤预防与治疗, 2017,30(4) :313-318.
[9] 王欣宁. 二维超声、多普勒血流特点及CA125在卵巢恶性肿瘤中的诊断价值[D]. 沈阳:中国医科大学, 2005.
[10] 肖贤,张恒,周盼妍 ,等.三维超声下良恶性卵巢肿瘤的声像图特征及血流参数比较[J]. 实用癌症杂志 , 2014,29(7) :852-854.
[11] 门杰,王开福,曾宁,等. 超声诊断并引导介入性治疗卵巢冠囊肿效果分析[J]. 临床超声医学杂志,2015,17(11):784-785.
[12] 潘玉萍,蔡爱露,赵丹,等. 卵巢腺纤维瘤及囊性腺纤维瘤的超声特征[J]. 中国医学影像技术,2012,28(9):1699-1701.
[13] 陈蕾. 彩色多普勒超声对卵巢妊娠的诊断价值及效果观察[J].中国继续医学教育,2017,9(11):81-82.
[14] 张芳,张周龙. 超声检查联合肿瘤标志物检测对卵巢良恶性肿瘤诊断意义研究[J]. 中国妇幼保健,2015,30(31):5482-5485.
[15] U Menon, A Gentry-Maharaj , R Hallett ,et al. Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) [J]. Lancet Oncol, 2009, 10 (4) :327-340.
[16] Timmerman D. Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group[J].Ultrasound Obstet Gynecol,2010,36(2):226.
[17] Alcazar LJ. Tumor angiogenesis assessed by three-dimensional power Doppler ultrasound in early, advanced and metastatic ovarian cancer: a preliminary study[J]. Ultrasound Obstet Gynecol,2006,28(3) :325-329.
[18] Aguirre A, Ardeshirpour Y, Sanders MM,et al. Potential role of coregistered photoacoustic and ultrasound imaging in ovarian cancer detection and characterization[J]. Trans Oncol,2011,4(1) :29.