Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer

被引:96
作者
Zhang, Zhen [1 ]
Yu, Yinhua [2 ]
Xu, Fengji [2 ]
Berchuck, Andrew [3 ]
van Haaften-Day, Carolien [4 ]
Havrilesky, Laura J. [3 ]
de Bruijn, Henk W. A. [4 ]
van der Zee, Ate G. J. [5 ]
Woolas, Robert P. [6 ]
Jacobs, Ian J. [7 ]
Skates, Steven [8 ]
Chan, Daniel W. [1 ]
Bast, Robert C., Jr. [2 ]
机构
[1] Johns Hopkins Med Inst, Dept Pathol, Dept Biomarker Discovery, Baltimore, MD 21231 USA
[2] Anderson Canc Ctr, Houston, TX 77030 USA
[3] Duke Univ, Med Ctr, Durham, NC 27710 USA
[4] Royal Hosp Women, Sydney, NSW, Australia
[5] Univ Groningen, Univ Med Ctr Groningen, NL-9700 RB Groningen, Netherlands
[6] St Marys Hosp, Portsmouth, Hants, England
[7] Queen Marys Sch Med, Barts & London, London, England
[8] Massachusetts Gen Hosp, Harvard Med Sch, Boston, MA USA
关键词
ovarian cancer; tumor marker; early detection; multivariate model; artificial neural network;
D O I
10.1016/j.ygyno.2007.08.009
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objective. 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. Methods. Serum specimens collected at four institutions in the US, the Netherlands and the United Kingdom were analyzed for CA 125II, CA 724, 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. Results. 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. Conclusions. 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. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:526 / 531
页数:6
相关论文
共 27 条
[1]   ELEVATION OF SERUM CA 125 PRIOR TO DIAGNOSIS OF AN EPITHELIAL OVARIAN-CARCINOMA [J].
BAST, RC ;
SIEGAL, FP ;
RUNOWICZ, C ;
KLUG, TL ;
ZURAWSKI, VR ;
SCHONHOLZ, D ;
COHEN, CJ ;
KNAPP, RC .
GYNECOLOGIC ONCOLOGY, 1985, 22 (01) :115-120
[2]   A RADIOIMMUNOASSAY USING A MONOCLONAL-ANTIBODY TO MONITOR THE COURSE OF EPITHELIAL OVARIAN-CANCER [J].
BAST, RC ;
KLUG, TL ;
STJOHN, E ;
JENISON, E ;
NILOFF, JM ;
LAZARUS, H ;
BERKOWITZ, RS ;
LEAVITT, T ;
GRIFFITHS, CT ;
PARKER, L ;
ZURAWSKI, VR ;
KNAPP, RC .
NEW ENGLAND JOURNAL OF MEDICINE, 1983, 309 (15) :883-887
[3]  
Bast Robert C Jr, 2002, Cancer Treat Res, V107, P61
[4]   Prospective validation of artificial neural network trained to identify acute myocardial infarction [J].
Baxt, WG ;
Skora, J .
LANCET, 1996, 347 (8993) :12-15
[5]  
BEREK JS, 2000, OVARIAN CANC, P1687
[6]  
Bishop CM., 1995, Neural networks for pattern recognition
[7]   Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions [J].
Bottaci, L ;
Drew, PJ ;
Hartley, JE ;
Hadfield, MB ;
Farouk, R ;
Lee, PWR ;
Macintyre, IMC ;
Duthie, GS ;
Monson, JRT .
LANCET, 1997, 350 (9076) :469-472
[8]   SCREENING FOR EARLY FAMILIAL OVARIAN-CANCER WITH TRANSVAGINAL ULTRASONOGRAPHY AND COLOR BLOOD-FLOW IMAGING [J].
BOURNE, TH ;
CAMPBELL, S ;
REYNOLDS, KM ;
WHITEHEAD, MI ;
HAMPSON, J ;
ROYSTON, P ;
CRAYFORD, TJB ;
COLLINS, WP .
BRITISH MEDICAL JOURNAL, 1993, 306 (6884) :1025-1029
[9]  
EINHORN N, 1986, OBSTET GYNECOL, V67, P414
[10]  
JACOBS I, 1989, HUM REPROD, V4, P1