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• W4285139620 abstract "Gastric cancer (GC) is one of the most common cancers in the world. In cancer detection, liquid biopsy, as a noninvasive and rapid method, is growing in importance. Different from traditional liquid biopsy using a single biomarker, this study integrated a variety of blood biochemical indices and established an identification system by means of deep learning under the H2O framework method. Based on data from 2951 samples, 58 routine blood biochemical indices, age and gender were collected as comprehensive indices to establish the identification model. Then, the number of indices was reduced to simplify the model, and 33 indices were utilized to build the final identification tool. A tenfold crossvalidation technique was used to evaluate the performance of the proposed method. The sensitivity, specificity, accuracy, and area under the ROC curve on the cross-validation set were 85.44%, 83.82%, 84.54% and 0.9165, respectively. The identification tool is built free online at http://www.cppdd.cn/GC2. The proposed system provides a new approach to identify GC with advantages of being efficient, noninvasive and economical. The deep learning of the integration of these blood biochemical indices will bring insights into the comprehensive understanding of GC pathology, as well as the prevention, screening, diagnosis, and prognosis of GC." @default.
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• W4285139620 date "2022-01-01" @default.
• W4285139620 modified "2023-09-27" @default.
• W4285139620 title "Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep Learning" @default.
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• W4285139620 doi "https://doi.org/10.1109/access.2022.3172477" @default.
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