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 CA125II, CA72-4, CA15-3, 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 dataset 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 CA125II alone (p=0.0333). At a fixed specificity of 98%, the sensitivities for ANN and CA125II 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 CA125II 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.