Sohlich Rita E, Sickles Edward A, Burnside Elizabeth S, Dee Katherine E
Department of Radiology, Box 1667, University of California Medical Center, San Francisco, CA 94143-1667, USA.
AJR Am J Roentgenol. 2002 Mar;178(3):681-6. doi: 10.2214/ajr.178.3.1780681.
The objective of this study was to use mathematic models to aid mammography practices in interpreting outcomes data derived from a combination of screening and diagnostic examinations, and in interpreting diagnostic mammography outcomes data that are not segregated by indication for examination.
We analyzed outcomes from 51,805 consecutive mammography examinations. Screening and diagnostic examinations were audited separately. Diagnostic examinations were audited by indication for examination. Extrapolating from our known mix of screening (79%) and diagnostic (21%) examinations, we determined expected combined outcomes for various mixes that might be encountered in clinical practice. Similarly, we determined the expected overall diagnostic mammography outcomes for various clinically relevant mixes of indications for examination.
Outcomes vary substantially depending on the mix of screening and diagnostic examinations performed. For example, expected outcomes for practices with screening-diagnostic mixes of 90-10% and 50-50% are, respectively: rate of abnormal findings, 6% versus 11%; rate of positive biopsy findings, 38% versus 42%; cancer detection rate, 10 per 1,000 versus 30 per 1,000; mean invasive cancer size, 14.4 mm versus 16.0 mm; nodal metastasis rate, 8% versus 11%; and rate of stage 0 and stage I cancers, 87% versus 82%. Diagnostic outcomes also vary substantially according to indication for examination, with a higher rate of abnormal findings, a higher rate of positive biopsy findings, and a larger mean invasive cancer size expected for mixes involving a high percentage of workups for palpable lesions.
When screening and diagnostic mammography outcomes are not segregated during auditing, and when diagnostic outcomes are not segregated by indication for examination, analysis of combined audit data should be based on extrapolations from known outcomes.
本研究的目的是使用数学模型来辅助乳房X线摄影实践,以解读从筛查和诊断检查相结合中得出的结果数据,以及解读未按检查指征分类的诊断性乳房X线摄影结果数据。
我们分析了连续51805次乳房X线摄影检查的结果。筛查和诊断检查分别进行审核。诊断检查按检查指征进行审核。根据我们已知的筛查(79%)和诊断(21%)检查比例,我们确定了临床实践中可能遇到的各种比例组合的预期综合结果。同样,我们确定了各种临床相关检查指征组合的预期总体诊断性乳房X线摄影结果。
结果因所进行的筛查和诊断检查的比例组合而有很大差异。例如,筛查 - 诊断比例为90 - 10%和50 - 50%的实践的预期结果分别为:异常发现率,6%对11%;活检阳性发现率,38%对42%;癌症检测率,每1000人中有10例对每1000人中有30例;平均浸润性癌大小,14.4毫米对16.0毫米;淋巴结转移率,8%对11%;0期和I期癌症的比例,87%对82%。诊断结果也因检查指征而有很大差异,对于涉及高比例可触及病变检查的组合,预期异常发现率更高、活检阳性发现率更高且平均浸润性癌大小更大。
在审核期间,如果筛查和诊断性乳房X线摄影结果未分开,且诊断结果未按检查指征分类,则合并审核数据的分析应基于已知结果的推断。