钼靶、磁共振及核素显像诊断乳腺癌准确性的系统评价及相关统计学技术探讨
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摘要
第一部分钼靶、磁共振及核素显像诊断乳腺癌准确性的系统评价:Meta分析
目的:通过Meta分析对乳腺癌的几种影像诊断方法——钼靶(mammography,MG)、磁共振(magnetic resonance image,MRI)及核素显像(positron emission tomography/ single photon emission computed tomography, PET/SPECT)进行诊断准确性的对比研究。
方法:检索PUBMED、OVID数据库,按照循证医学诊断试验的评价标准筛选有关MG、MRI及PET/SPECT诊断乳腺癌的文献,提取相关文献数据,采用Excel 2003、RevMan 4.2和Meta test软件分析数据,得到合并的诊断敏感度、特异度、似然比(likelihood ratio,LR)和比值比(odds ratio,OR),并绘制集成受试者工作特性曲线(summary receiver operating characteristic curve,SROC)直观评价各检查方法的诊断准确性。
结果: 30篇文献(部分为比较影像学研究涉及两个或两个以上检测手段),14篇关于MG(2941个病灶),10篇关于MRI( 1428个病灶),17篇关于PET/SPECT(2247个病灶),符合纳入标准。MG、MRI、PET/SPECT合并诊断的敏感度分别为82%(95%CI:76-86%)、86%(95%CI:83-88%)和87%( 95%CI : 83-90% ) ;特异度分别为69% ( 95%CI : 62-75% )、65%(95%CI:62-69%)和82%(95%CI:76-86%);SROC曲线下面积(area under curve,AUC)分别为0.84、0.89和0.90;Q*值分别为0.77、0.81和0.88
结论:三种检查方法的汇总敏感度相似,核素显像的特异度高于其它两项;核素显像和MRI的综合诊断效能大于MG。MG仍然是目前乳腺癌初诊较合适的影像方法,年轻女性可优先考虑MRI检查,核素显像可在其它检查疑似乳癌但不能明确诊断的情况下适当选用。
第二部分集成ROC曲线及其参数估计在Excel2003中的实现
目的:在Excel2003中建立应用程序(VBA)来完成集成ROC曲线(SROC)的绘制及曲线下面积、Q*值的计算。
方法:以Excel2003为平台,由VB工具箱产生窗体及按钮(用户界面)编写相应程序代码,并调用Excel内置函数完成Logit变换、SROC的估计及描绘以及曲线下面积和Q*值的计算,并使程序具有初步的数据存储和管理功能,Meta分析—SROC曲线的数理统计原理及方法参照刘关键,吴泰相的报道。VBA建立后录入刘关键,吴泰相报道中的实例,并多次运行程序,以运行结果是否与报道中的结果一致来评价程序的可靠性和稳定性。
结果:所建立的VBA应用程序具有良好的用户界面,数据录入及修改简便,多次运行程序后结果一致,所描绘的SROC曲线整洁美观,所得曲线下面积及Q*值与文章报道中相符(精确到小数点后的两位)。
结论:本实验中以Excel2003为平台建立的VBA程序能方便可靠地对诊断性试验的二分类数据进行Meta分析,描绘相应的SROC曲线,并准确的估计出相关参数,是循证影像学Meta分析的有用工具。
第三部分利用ReviewManager4.2实现诊断性试验Meta分析的探讨
探讨如何利用ReviewManager4.2软件对诊断性试验进行Meta分析。结合实例介绍如何整理诊断性实验数据并按二分类数据(dichotomous data)录入ReviewManager4.2(Tables/Comparisons and data)中进行分析,得出诊断性试验的汇总阳性似然比、阴性似然比及汇总诊断比值比(diagnostic odds ratio,DOR),并进一步推算出汇总敏感度和特异度。
Part 1 System review of mammography, magnetic resonance image, and radionuclide imaging on breast cancer: a Meta analysis
Objective: To compare the performance of several imaging modalities (mammography, magnetic resonance image, radionuclide imaging) in the detection of breast cancer by Meta analysis.
Materials and Methods: PUBMED, OVID, database (1986-2006) were searched for the related articles. By the evaluation criterion of evidence based medicine(EBM), articles about the diagnosis of breast cancer using MG, MRI, PET and/or SPECT were find out. Related diagnostic index, such as TPR, FPR, FNR, and TNP, was acquired. Using software Excel 2003, RevMan 4.2, the pooled sensitivity, specificity, odds ratio (OR) and likelihood (LR) were worked out. And Summary receiver operating characteristic curve (SROC) was drawn to compare the accuracy of the several imaging modality vividly.
Results: Thirty articles including 14MG (2941 lesions), 10 MRI (1428 lesions), 17 PET/SPECT (2247 lesions) met the inclusion criteria. The pooled sensitivity (mean( 95%CI)) of US,MG,MRI and PET/SPECT were 82%(76-86%), 86%(83-88%), and 87%(83-90%) respectively, pooled specificity, 69%(62-75%), 65%(62-69%)and 82%(76-86%). Area under SROC ( AUC-SROC) was 0.84, 0.89and 0.90 respectively, Q* value, 0.77, 0.81 and 0.88. Conclusion: The pooled sensitivity of the three modalities is similar. The pooled specificity of radionuclide imaging is higher than other two modalities. The pooled diagnostic efficacy of radionuclide imaging and MRI is higher than MG. Therefore, MG is still suitable for preliminary screening of breast cancer, MRI is recommended for young women, and Radionuclide imaging can be used to further confirm a breast cancer suspected by other two modalities.
Part 2 Summary ROC plotting and its parameters estimation using Excel2003
Objective: To set up a VBA based on Excel2003 to estimate and draw the SROC. Methods: Based on Excel2003, first, the forms and buttons of the VBA were produced by VB toolbox. Second, related Excel built-in functions were called to perform the logit transformation, plot the SROC, and work out the area under the SROC as well as the value of the Q*. At the same time initial data storage and management capabilities was established. Principles of mathematical statistics of SROC were based on the article of Guanjian LIU and Taixian WU. After the building of the VBA the data from the reported sample was processed several times to validate the in-house software by comparing its results with the results from Guanjian LIU and Taixian WU.
results:The established VBA has user friendly interface and is very convenient for data entry and revision. Several times processing produced one result. The produced SROC plot was satisfactory and the area under the curve and the value of the Q* were consistent with those reported by Guanjian LIU, Taixian WU. Conclusion: the VBA based on Excel2003 can be used conveniently and reliably for Meta-analysis of the diagnostic tests. It’s a useful tool for Meta-analysis of Evidence-based imaging medicine.
Part 3 Meta analysis of diagnostic test with Review Manager 4.2
The objective of this article was to introduce a new method to perform Meta analysis of diagnostic test with Reviewmanager 4.2. With an instance we demonstrate how to collect, range the raw data from a diagnostic test, and then input it into the Reviewmanager 4.2 for pooled positive likelihood ratio, pooled negative likelihood ratio, and pooled diagnostic odds ratio (DOR). According to these data the pooled sensitivity and specificity can be worked out.
目的:通过Meta分析对乳腺癌的几种影像诊断方法——钼靶(mammography,MG)、磁共振(magnetic resonance image,MRI)及核素显像(positron emission tomography/ single photon emission computed tomography, PET/SPECT)进行诊断准确性的对比研究。
方法:检索PUBMED、OVID数据库,按照循证医学诊断试验的评价标准筛选有关MG、MRI及PET/SPECT诊断乳腺癌的文献,提取相关文献数据,采用Excel 2003、RevMan 4.2和Meta test软件分析数据,得到合并的诊断敏感度、特异度、似然比(likelihood ratio,LR)和比值比(odds ratio,OR),并绘制集成受试者工作特性曲线(summary receiver operating characteristic curve,SROC)直观评价各检查方法的诊断准确性。
结果: 30篇文献(部分为比较影像学研究涉及两个或两个以上检测手段),14篇关于MG(2941个病灶),10篇关于MRI( 1428个病灶),17篇关于PET/SPECT(2247个病灶),符合纳入标准。MG、MRI、PET/SPECT合并诊断的敏感度分别为82%(95%CI:76-86%)、86%(95%CI:83-88%)和87%( 95%CI : 83-90% ) ;特异度分别为69% ( 95%CI : 62-75% )、65%(95%CI:62-69%)和82%(95%CI:76-86%);SROC曲线下面积(area under curve,AUC)分别为0.84、0.89和0.90;Q*值分别为0.77、0.81和0.88
结论:三种检查方法的汇总敏感度相似,核素显像的特异度高于其它两项;核素显像和MRI的综合诊断效能大于MG。MG仍然是目前乳腺癌初诊较合适的影像方法,年轻女性可优先考虑MRI检查,核素显像可在其它检查疑似乳癌但不能明确诊断的情况下适当选用。
第二部分集成ROC曲线及其参数估计在Excel2003中的实现
目的:在Excel2003中建立应用程序(VBA)来完成集成ROC曲线(SROC)的绘制及曲线下面积、Q*值的计算。
方法:以Excel2003为平台,由VB工具箱产生窗体及按钮(用户界面)编写相应程序代码,并调用Excel内置函数完成Logit变换、SROC的估计及描绘以及曲线下面积和Q*值的计算,并使程序具有初步的数据存储和管理功能,Meta分析—SROC曲线的数理统计原理及方法参照刘关键,吴泰相的报道。VBA建立后录入刘关键,吴泰相报道中的实例,并多次运行程序,以运行结果是否与报道中的结果一致来评价程序的可靠性和稳定性。
结果:所建立的VBA应用程序具有良好的用户界面,数据录入及修改简便,多次运行程序后结果一致,所描绘的SROC曲线整洁美观,所得曲线下面积及Q*值与文章报道中相符(精确到小数点后的两位)。
结论:本实验中以Excel2003为平台建立的VBA程序能方便可靠地对诊断性试验的二分类数据进行Meta分析,描绘相应的SROC曲线,并准确的估计出相关参数,是循证影像学Meta分析的有用工具。
第三部分利用ReviewManager4.2实现诊断性试验Meta分析的探讨
探讨如何利用ReviewManager4.2软件对诊断性试验进行Meta分析。结合实例介绍如何整理诊断性实验数据并按二分类数据(dichotomous data)录入ReviewManager4.2(Tables/Comparisons and data)中进行分析,得出诊断性试验的汇总阳性似然比、阴性似然比及汇总诊断比值比(diagnostic odds ratio,DOR),并进一步推算出汇总敏感度和特异度。
Part 1 System review of mammography, magnetic resonance image, and radionuclide imaging on breast cancer: a Meta analysis
Objective: To compare the performance of several imaging modalities (mammography, magnetic resonance image, radionuclide imaging) in the detection of breast cancer by Meta analysis.
Materials and Methods: PUBMED, OVID, database (1986-2006) were searched for the related articles. By the evaluation criterion of evidence based medicine(EBM), articles about the diagnosis of breast cancer using MG, MRI, PET and/or SPECT were find out. Related diagnostic index, such as TPR, FPR, FNR, and TNP, was acquired. Using software Excel 2003, RevMan 4.2, the pooled sensitivity, specificity, odds ratio (OR) and likelihood (LR) were worked out. And Summary receiver operating characteristic curve (SROC) was drawn to compare the accuracy of the several imaging modality vividly.
Results: Thirty articles including 14MG (2941 lesions), 10 MRI (1428 lesions), 17 PET/SPECT (2247 lesions) met the inclusion criteria. The pooled sensitivity (mean( 95%CI)) of US,MG,MRI and PET/SPECT were 82%(76-86%), 86%(83-88%), and 87%(83-90%) respectively, pooled specificity, 69%(62-75%), 65%(62-69%)and 82%(76-86%). Area under SROC ( AUC-SROC) was 0.84, 0.89and 0.90 respectively, Q* value, 0.77, 0.81 and 0.88. Conclusion: The pooled sensitivity of the three modalities is similar. The pooled specificity of radionuclide imaging is higher than other two modalities. The pooled diagnostic efficacy of radionuclide imaging and MRI is higher than MG. Therefore, MG is still suitable for preliminary screening of breast cancer, MRI is recommended for young women, and Radionuclide imaging can be used to further confirm a breast cancer suspected by other two modalities.
Part 2 Summary ROC plotting and its parameters estimation using Excel2003
Objective: To set up a VBA based on Excel2003 to estimate and draw the SROC. Methods: Based on Excel2003, first, the forms and buttons of the VBA were produced by VB toolbox. Second, related Excel built-in functions were called to perform the logit transformation, plot the SROC, and work out the area under the SROC as well as the value of the Q*. At the same time initial data storage and management capabilities was established. Principles of mathematical statistics of SROC were based on the article of Guanjian LIU and Taixian WU. After the building of the VBA the data from the reported sample was processed several times to validate the in-house software by comparing its results with the results from Guanjian LIU and Taixian WU.
results:The established VBA has user friendly interface and is very convenient for data entry and revision. Several times processing produced one result. The produced SROC plot was satisfactory and the area under the curve and the value of the Q* were consistent with those reported by Guanjian LIU, Taixian WU. Conclusion: the VBA based on Excel2003 can be used conveniently and reliably for Meta-analysis of the diagnostic tests. It’s a useful tool for Meta-analysis of Evidence-based imaging medicine.
Part 3 Meta analysis of diagnostic test with Review Manager 4.2
The objective of this article was to introduce a new method to perform Meta analysis of diagnostic test with Reviewmanager 4.2. With an instance we demonstrate how to collect, range the raw data from a diagnostic test, and then input it into the Reviewmanager 4.2 for pooled positive likelihood ratio, pooled negative likelihood ratio, and pooled diagnostic odds ratio (DOR). According to these data the pooled sensitivity and specificity can be worked out.
引文
1. Li YP, ed. Evidence-based medicine. Beijing: Higher Education Press.2003.57-58李幼平主编.循证医学.北京:高等教育出版社.2003.57-58.
2. Sackett DI, Straus SE, Richardson WS, et aI. Evidence based medicine:How to practice and teach EBM[M].2 ed.London:Churchil1 Livingstone,2000:235-237,24l-244.
3. Moses L E, Shapiro D , Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve : data - analytic approaches and some additional consideration[J ] . Statistics in Medicine 1993 ;12 :1293 - 1316.
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3. Masaaki Inoue, Toshiko Sano, Ryousuke Watai. Dynamic Multidetector CT of Breast Tumors: Diagnostic Features and Comparison with Conventional Techniques. American Journal of Roentgenology 2003; 181:679-686.
4. Perrone A, Lo Mele L, Sassi S. MDCT of the breast. American Journal of Roentgenology 2008;190:1644-51
5. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991; 64:409-412.
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1. Zhang M, Kono M. Solitary pulmonary nodules: evaluation of blood flow patterns with dynamic CT. Radiology 1997; 205:471-478.
2. Shu-Hua Ma, Hong-Bo Le, Bao-hui Jia. Peripheral pulmonary nodules: Relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer 2008; 8: 186.
3. Masaaki Inoue, Toshiko Sano, Ryousuke Watai. Dynamic Multidetector CT of Breast Tumors: Diagnostic Features and Comparison with Conventional Techniques. American Journal of Roentgenology 2003; 181:679-686.
4. Perrone A, Lo Mele L, Sassi S. MDCT of the breast. American Journal of Roentgenology 2008;190:1644-51
5. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991; 64:409-412.
6. Axel L. Cerebral blood flow determination by rapid-sequence computed tomography: a theoretical analysis. Radiology 1980;137:679-686
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8. Roberts GW, Larson KB. The interpretation of mean transit time measurements for multi-phase tissue systems. J Theor Biol 1973;39:447-475
9. Frieder Langenbucher.Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution European Journal of Pharmaceutics and Biopharmaceutics.2003,56(9):429–437.
10. Axel L.Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique.Invest Radiol.1983,18(2):94-99.
11. Cuenod CA, Leconte I, Siauve N,et al.Deconvolution technique for measuring tissue perfusion by dynamic CT:application to normal and metastatic liver. Acad Radiol.2002,9(1):205–211.
12. Cenic A, Nabavi DG, Craen RA, et al. Dynamic CT measurement of cerebral blood flow: a validation study. Am. J. Neuroradiol 1999; 20: 63–73.
13. Kyongtae T B, Jay RH, James AB, Aortic and Hepatic Contrast Medium Enhancement at CT Part I. Prediction with a Computer Model. Radiology 1998; 207:647-655.
14. Wang JH, Min PQ, Wang PJ, et al. Dynamic CT Evaluation of Tumor Vascularity in Renal Cell Carcinoma. American Journal of Roentgenology 2006; 186:1423-1430.
15. Aleksa Cenic, Darius G. Nabavi, Rosemary A. Craen, et al. A CT Method to Measure Hemodynamics in Brain Tumors: Validation and Application of Cerebral Blood Flow Maps. American Journal of Neuroradiology 2000; 21:462-470.
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