Skates S J, Xu F J, Yu Y H, Sjövall K, Einhorn N, Chang Y, Bast R C, Knapp R C
Harvard Medical School, Boston, Massachusetts, USA.
Cancer. 1995 Nov 15;76(10 Suppl):2004-10. doi: 10.1002/1097-0142(19951115)76:10+<2004::aid-cncr2820761317>3.0.co;2-g.
Stored samples from women in the Stockholm screening study were reassayed for CA125II (Centocor, Malvern, PA) and OVX1. The postmenopausal women older than age 50 without ovarian cancer were randomly split into a training set to develop a screening test based on longitudinal marker levels and a second set to validate the test. The CA125II data from each woman is summarized by the slope and intercept from a linear regression of log(CA125II) on time since first sample. The slope versus the intercept for the training set and the ovarian cancer cases formed a bivariate scatter plot. A curve was drawn on the scatter plot that separated most of the women with ovarian cancer from all other women; it delineated a screening test. The specificity of this test was examined on the validation set with a specificity of 99.8%. Bayes' theorem was used to calculate the risk of ovarian cancer (ROC) based on the intercept, slope, and assay variability. It is important to account for assay variability because it can produce large slopes over short periods of time. The maximum risk, which identified 83% (5 of 6) of the ovarian cancers detected within a year of last assay, was applied as a test to the training set and confirmed a high specificity of 99.7%. With this specificity and sensitivity, the ROC algorithm using the CA125II assay has an estimated positive predictive value of 16%, substantially greater than the positive predictive value based on a single assay. Further study is planned to confirm the sensitivity of this approach.
对斯德哥尔摩筛查研究中女性的储存样本重新检测CA125II（Centocor公司，马尔文，宾夕法尼亚州）和OVX1。年龄超过50岁且无卵巢癌的绝经后女性被随机分为一组用于基于纵向标志物水平开发筛查试验的训练集和另一组用于验证该试验的验证集。每位女性的CA125II数据通过log(CA125II)对自首次采样以来时间的线性回归的斜率和截距进行汇总。训练集的斜率与截距以及卵巢癌病例形成了双变量散点图。在散点图上绘制了一条曲线，该曲线将大多数卵巢癌女性与所有其他女性区分开来；它描绘了一种筛查试验。在验证集上检查了该试验的特异性，特异性为99.8%。使用贝叶斯定理根据截距、斜率和检测变异性计算卵巢癌风险（ROC）。考虑检测变异性很重要，因为它可能在短时间内产生较大的斜率。将在末次检测后一年内检测到的83%（6例中的5例）卵巢癌所对应的最大风险用作对训练集的一项检测，并确认其具有99.7%的高特异性。基于这种特异性和敏感性，使用CA125II检测的ROC算法估计阳性预测值为16%，大大高于基于单次检测的阳性预测值。计划进行进一步研究以确认这种方法的敏感性。
