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

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). 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.

[1]  A. Kabakov,et al.  Molecular Chaperones in Cancer Stem Cells: Determinants of Stemness and Potential Targets for Antitumor Therapy , 2020, Cells.

[2]  N. Winer,et al.  Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review , 2019, Journal of Ovarian Research.

[3]  Comments on “Should we consider gallbladder cancer a new smoking‐related cancer? A comprehensive meta‐analysis focused on dose‐response relationships” , 2020, International journal of cancer.

[4]  Zhanguo Zhang,et al.  Synchronous cancers of gallbladder carcinoma and combined hepatocellular cholangiocarcinoma: an unusual case and literature review , 2018, BMC Cancer.

[5]  Francisco Javier de Cos Juez,et al.  A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks , 2018, Neural Computing and Applications.

[6]  Yanrong Wang,et al.  Construction of the first high-density genetic linkage map and identification of seed yield-related QTLs and candidate genes in Elymus sibiricus, an important forage grass in Qinghai-Tibet Plateau , 2019, BMC Genomics.

[7]  H. Yamawaki,et al.  Development of Prostate Cancer Organoid Culture Models in Basic Medicine and Translational Research , 2020, Cancers.

[8]  Qiang Li,et al.  Effectiveness of Olaparib Treatment in a Patient with Gallbladder Cancer with an ATM-Inactivating Mutation. , 2020, The oncologist.

[9]  Xu’an Wang,et al.  Betulinic acid induces apoptosis of gallbladder cancer cells via repressing SCD1. , 2020, Acta biochimica et biophysica Sinica.

[10]  Francisco Javier de Cos Juez,et al.  A Methodology for the Detection of Relevant Single Nucleotide Polymorphism in Prostate Cancer by Means of Multivariate Adaptive Regression Splines and Backpropagation Artificial Neural Networks , 2020, SOCO-CISIS-ICEUTE.

[11]  M. Manns,et al.  Potent Antitumor Activity of Liposomal Irinotecan in an Organoid- and CRISPR-Cas9-Based Murine Model of Gallbladder Cancer , 2019, Cancers.

[12]  C. Conrad,et al.  Author response to: Is laparoscopic re‐resection of incidental gallbladder cancer really non‐inferior to the open approach? , 2020, The British journal of surgery.

[13]  K. Jhaveri,et al.  Risk of gallbladder cancer in patients with primary sclerosing cholangitis and radiographically detected gallbladder polyps , 2019, Liver international : official journal of the International Association for the Study of the Liver.

[14]  Xiaocheng Li,et al.  Matrix Metalloproteinase 2 Knockdown Suppresses the Proliferation of HepG2 and Huh7 Cells and Enhances the Cisplatin Effect , 2019, Open medicine.

[15]  Pilar García-Díaz,et al.  Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data. , 2019, Genomics.

[16]  U. Motosugi,et al.  Carcinosarcoma (adenocarcinoma, neuroendocrine carcinoma, undifferentiated carcinoma and chondrosarcoma) of the gallbladder , 2020, Clinical journal of gastroenterology.

[17]  Lingqing Xu,et al.  Comparative Study of Back Propagation Artificial Neural Networks and Logistic Regression Model in Predicting Poor Prognosis after Acute Ischemic Stroke , 2019, Open medicine.

[18]  Yang Wang,et al.  Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. , 2018, Cancer genomics & proteomics.

[19]  H. Yamawaki,et al.  Emerging Roles of Cancer Stem Cells in Bladder Cancer Progression, Tumorigenesis, and Resistance to Chemotherapy: A Potential Therapeutic Target for Bladder Cancer , 2020, Cells.

[20]  N. S. Kumar,et al.  Antiquorum sensing and antibiofilm potential of biosynthesized silver nanoparticles of Myristica fragrans seed extract against MDR Salmonella enterica serovar Typhi isolates from asymptomatic typhoid carriers and typhoid patients , 2019, Environmental Science and Pollution Research.

[21]  Chin-Ming Huang,et al.  Colectomy influences the radial pulse parameters of traditional Chinese medicine pulse diagnosis in patients with colorectal cancer , 2020 .

[22]  Yanqing Niu,et al.  LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination , 2019, BMC Genomics.

[23]  O. Topolcan,et al.  Evaluation of Tumor Markers and Their Impact on Prognosis in Gallbladder, Bile Duct and Cholangiocellular Carcinomas - A Pilot Study. , 2017, Anticancer research.

[24]  H. Völzke,et al.  Copy number variants in lipid metabolism genes are associated with gallstones disease in men , 2019, European Journal of Human Genetics.

[25]  Gurpreet Singh,et al.  Intelligent Skin Cancer Detection Mobile Application Using Convolution Neural Network , 2019 .

[26]  Didier Mutter,et al.  High intensity focused ultrasound (HIFU) applied to hepato-bilio-pancreatic and the digestive system-current state of the art and future perspectives. , 2016, Hepatobiliary surgery and nutrition.

[27]  Y. Jin,et al.  Comment on: Comparison of oncological outcomes after open and laparoscopic re‐resection of incidental gallbladder cancer , 2020, The British journal of surgery.

[28]  Bradley J Erickson,et al.  A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. , 2019, Journal of the American College of Radiology : JACR.

[30]  Y. Chou,et al.  Algorithmic approaches to the diagnosis of gallbladder intraluminal lesions on ultrasonography. , 2018, Journal of the Chinese Medical Association : JCMA.

[31]  T. Wakai,et al.  Long-term outcomes of surgical resection for T1b gallbladder cancer: an institutional evaluation , 2020, BMC Cancer.

[32]  Manoj Kumar,et al.  A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering , 2020, Mob. Networks Appl..

[33]  Dong Zhang,et al.  Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma , 2019, World journal of gastroenterology.

[34]  Amr Tolba,et al.  Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks , 2019, Neural Computing and Applications.

[35]  Jian Wang,et al.  Glycochenodeoxycholate promotes the metastasis of gallbladder cancer cells by inducing epithelial to mesenchymal transition via activation of SOCS3/JAK2/STAT3 signaling pathway , 2020, Journal of cellular physiology.