Zanca F, Chakraborty D P, Marchal G, Bosmans H
Department of Radiology, Leuven University Center of Medical Physics in Radiology, University Hospitals Leuven, 3000 Leuven, Belgium.
Radiat Prot Dosimetry. 2010 Apr-May;139(1-3):52-6. doi: 10.1093/rpd/ncq030. Epub 2010 Feb 16.
Although the receiver operating characteristic (ROC) method is the acknowledged gold-standard for imaging system assessment, it ignores localisation information and differentiation between multiple abnormalities per case. As the free-response ROC (FROC) method uses localisation information and more closely resembles the clinical reporting process, it is being increasingly used. A number of methods have been proposed to analyse the data that result from an FROC study: jackknife alternative FROC (JAFROC) and a variant termed JAFROC1, initial detection and candidate analysis (IDCA) and ROC analysis via the reduction of the multiple ratings on a case to a single rating. The focus of this paper was to compare JAFROC1, IDCA and the ROC analysis methods using a clinical FROC human data set. All methods agreed on the ordering of the modalities and all yielded statistically significant differences of the figures-of-merit, i.e. p < 0.05. Both IDCA and JAFROC1 yielded much smaller p-values than ROC. The results are consistent with a recent simulation-based validation study comparing these and other methods. In conclusion, IDCA or JAFROC1 analysis of FROC human data may be superior at detecting modality differences than ROC analysis.
尽管接收器操作特性（ROC）方法是公认的成像系统评估金标准，但它忽略了定位信息以及每个病例中多个异常之间的差异。由于自由响应ROC（FROC）方法使用了定位信息且更类似于临床报告过程，因此其应用越来越广泛。已经提出了多种方法来分析FROC研究产生的数据：刀切法替代FROC（JAFROC）及其变体JAFROC1、初始检测和候选分析（IDCA）以及通过将病例的多个评分简化为单个评分进行的ROC分析。本文的重点是使用临床FROC人体数据集比较JAFROC1、IDCA和ROC分析方法。所有方法在模态排序上均达成一致，并且所有方法在品质因数上均产生了具有统计学意义的差异，即p < 0.05。IDCA和JAFROC1产生的p值均比ROC小得多。这些结果与最近一项基于模拟的比较这些方法及其他方法的验证研究一致。总之，对FROC人体数据进行IDCA或JAFROC1分析在检测模态差异方面可能优于ROC分析。
