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Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma

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Abstract

This study was to explore the application value of back propagation (BP) neural network (BPNN) and genetic algorithm (GA) in the combined detection and prognosis of tumor markers in patients with gallbladder cancer. 446 patients with gallbladder cancer were included in the experimental group, 279 patients with benign gallbladder disease were included in the control group, and 188 healthy people were selected and included in the blank group. Serum tumor markers (CA242, CA199, CEA, and CA125) of the three groups were detected by electrochemical luminescent immune analyzer, and follow-up data for 5 years after surgery were collected. Based on BPNN and GA, an optimization algorithm for multi-tumor markers was constructed and applied to the combined detection of tumor markers in patients. The artificial neural network (ANN), dynamic network biomarker (DNB), auxiliary diagnosis algorithm of the support vector machine (SVM) based on the particle swarm optimization (PSO) (PSO-SVM), matched-pairs feature selection (MPFS) based on the machine learning, and the BPNN were introduced to compare with the algorithm constructed. The diagnostic performances of the algorithms were evaluated with the fivefold cross-validation method. The results showed that the levels of CanAg (CA) 242, carcinoma embryonic antigen (CEA), CA199, and CA125 and positive rates in the experimental group were significantly higher than those in the control group and the blank group (P < 0.05); but the differences between control group and blank group were not visible (P > 0.05). The sensitivity (91.72%) and specificity (87.49%) in detecting CA242 and CA199 based on the proposed algorithm were the highest; the sensitivity (0.9186), specificity (0.8622), and accuracy (94.94%) of the proposed algorithm were higher than those of the conventional algorithms. The postoperative follow-up survival rate of patients in the experimental group was reduced from 41.72% in the first year to 4.28% in the fifth year; tumor node metastasis (TNM) stage IV, neck gallbladder cancer, and CA199 were significantly correlated with the survival rate of patients in the experimental group (P < 0.05). In summary, the combined detection technology of multiple tumor markers based on deep learning algorithms showed excellent diagnostic and prognostic performance for gallbladder cancer. The occurrence of gallbladder cancer was related to the tumor markers CA242, CA199, CEA, and CA125, showing better detection effects by combination of CA242 and CA199. The TNM stage IV, neck gallbladder cancer, and CA199 were independent risk factors for the decrease in survival rate of patients with gallbladder cancer.

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Authors and Affiliations

  1. Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer, Tianjin, 300060, China

    Yigang Chang & Qiang Li

  2. Shanxi Provincial People`S Hospital, Taiyuan, 030012, China

    Yigang Chang & Huaying Huo

  3. Shanxi Tumor Hospital, Taiyuan, 030013, China

    Qian Wu

  4. First Hospital of Shanxi Medical University, Taiyuan, 030001, China

    Limin Chi

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  1. Yigang Chang
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  2. Qian Wu
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Chang, Y., Wu, Q., Chi, L. et al. Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma. J Supercomput 78, 3955–3975 (2022). https://doi.org/10.1007/s11227-021-03843-z

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  • DOI: https://doi.org/10.1007/s11227-021-03843-z

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