Abstract
Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.
Highlights
Ovarian cancer is one of the most common malignancies worldwide
We showed that the pretrained convolutional neural network (CNN) model AlexNet can recognize early-stage epithelial ovarian cancer (EOC) using PCA-based 2D barcodes at the ROC of 0.881
The ROC further increased to 0.951 when 2D barcodes were generated based on the serum levels of cancer antigen 125 (CA125) and HE4
Summary
Ovarian cancer is one of the most common malignancies worldwide. the 5-year average annual death rate due to ovarian cancer decreased by 2.3% during 2011–2015 [1], epithelial ovarian cancer (EOC) remains the leading cause of death among gynecological cancers [2,3]. Diagnostics for early-stage EOC are urgently needed because more than 50% of symptomatic women are diagnosed at an advanced stage and the 5-year survival rate of patients with advanced stage EOC is less than 30% [5]. Imaging technologies, such as positron emission tomography/computed tomography (PET/CT) with fluorodeoxyglucose, are useful in detecting early-stage EOC that is less than 3 cm in size; it is not widely employed for screening because of examinee’s economical and physical burdens [6,7]. Recent advances in the detection of early-stage EOC, such as micro RNA or cell-free DNA detection, have shown significant improvement in sensitivity and specificity [12,13]
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