Zhang Zhen, Yu Yinhua, Xu Fengji, Berchuck Andrew, van Haaften-Day Carolien, Havrilesky Laura J, de Bruijn Henk W A, van der Zee Ate G J, Woolas Robert P, Jacobs Ian J, Skates Steven, Chan Daniel W, Bast Robert C
Center for Biomarker Discovery, Department of Pathology, Johns Hopkins Medical Institutions, CRB-II 3M04, 1550 Orleans Street, Baltimore, MD 21231, USA.
Gynecol Oncol. 2007 Dec;107(3):526-31. doi: 10.1016/j.ygyno.2007.08.009. Epub 2007 Oct 24.
Currently available tumor markers for ovarian cancer are still inadequate in both sensitivity and specificity to be used for population-based screening. Artificial neural network (ANN) as a modeling tool has demonstrated its ability to assimilate information from multiple sources and to detect subtle and complex patterns. In this paper, an ANN model was evaluated for its performance in detecting early stage epithelial ovarian cancer using multiple serum markers.
Serum specimens collected at four institutions in the US, The Netherlands and the United Kingdom were analyzed for CA 125II, CA 72-4, CA 15-3 and macrophage colony stimulating factor (M-CSF). The four tumor marker values were then used as inputs to an ANN derived using a training set from 100 apparently healthy women, 45 women with benign conditions arising from the ovary and 55 invasive epithelial ovarian cancer patients (including 27 stage I/II cases). A separate validation set from 27 apparently healthy women, 56 women with benign conditions and 35 women with various types of malignant pelvic masses was used to monitor the ANN's performance during training. An independent test data set from 98 apparently healthy women and 52 early stage epithelial ovarian cancer patients (38 stage I and 4 stage II invasive cases and 10 stage I borderline ovarian tumor cases) was used to evaluate the ANN.
ROC analysis confirmed the overall superiority of the ANN-derived composite index over CA 125II alone (p=0.0333). At a fixed specificity of 98%, the sensitivities for ANN and CA 125II alone were 71% (37/52) and 46% (24/52) (p=0.047), respectively, for detecting early stage epithelial ovarian cancer, and 71% (30/42) and 43% (18/42) (p=0.040), respectively, for detecting invasive early stage epithelial ovarian cancer.
The combined use of multiple tumor markers through an ANN improves the overall accuracy to discern healthy women from patients with early stage ovarian cancer. Analysis of multiple markers with an ANN may be a better choice than the use of CA 125II alone in a two-step approach for population screening in which a secondary test such as ultrasound is used to keep the overall specificity at an acceptable level.
目前可用于卵巢癌的肿瘤标志物在敏感性和特异性方面仍不足以用于基于人群的筛查。人工神经网络(ANN)作为一种建模工具,已证明其能够整合来自多个来源的信息并检测细微和复杂的模式。本文评估了一种ANN模型使用多种血清标志物检测早期上皮性卵巢癌的性能。
对在美国、荷兰和英国四个机构收集的血清标本进行CA 125II、CA 72 - 4、CA 15 - 3和巨噬细胞集落刺激因子(M - CSF)分析。然后将这四种肿瘤标志物值用作ANN的输入,该ANN是使用来自100名明显健康女性、45名患有卵巢良性疾病的女性和55名浸润性上皮性卵巢癌患者(包括27例I/II期病例)的训练集得出的。使用来自27名明显健康女性、56名患有良性疾病的女性和35名患有各种类型恶性盆腔肿块的女性的单独验证集来监测ANN在训练期间的性能。使用来自98名明显健康女性和52名早期上皮性卵巢癌患者(38例I期和4例II期浸润性病例以及10例I期交界性卵巢肿瘤病例)的独立测试数据集来评估ANN。
ROC分析证实了ANN得出的综合指数总体优于单独的CA 125II(p = 0.0333)。在固定特异性为98%时,ANN和单独的CA 125II检测早期上皮性卵巢癌的敏感性分别为71%(37/52)和46%(24/52)(p = 0.047),检测浸润性早期上皮性卵巢癌的敏感性分别为71%(30/42)和43%(18/42)(p = 0.040)。
通过ANN联合使用多种肿瘤标志物可提高区分健康女性与早期卵巢癌患者的总体准确性。在两步法人群筛查中,其中使用超声等二次检测将总体特异性保持在可接受水平,使用ANN分析多种标志物可能比单独使用CA 125II是更好的选择。