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- W4306179211 abstract "INTRODUCTION: Patients diagnosed with pancreatic cancer (PC) suffer an abysmal overall survival despite our advancement in diagnostic techniques. Detection at an early stage is crucial to increase survival rate in PC. Many risk models have been proposed for early detection such as the PancRisk score that uses multivariable regression using 4 urinary biomarkers, REG1A/1B, LYVE1, and TFF1 along with Ca 19-9 to accurately predict the diagnosis of PC. This study aims to use these urinary biomarkers to not only predict PC, but also classify patients based on American Joint Committee on Cancer (AJCC) stage of PC. METHODS: A retrospective analysis of 140 patients with known PC and available measurements of LVYE1, REG1A/1B, and TFF1 were included in this study. Our neural network (NN) was trained using 70% of the data set with the remaining data used for validation testing. The performance characteristics of our NN was analyzed using area under the receiving operating characteristic and sensitivity/specificity measurements. RESULTS: A NN was created using the 4 urinary biomarkers, age, and creatinine with 1 hidden layer to predict AJCC stage I to II vs AJCC stage III to IV PC. Our NN had an area under the curve of 0.78, sensitivity of 70% and 60%, and specificity of 74% and 73% for the training and testing cohorts, respectively. Of the 4 biomarkers, REG1A had the highest normalized importance ratio of 81.7% (Figure).FigureCONCLUSION: NN incorporating biomarkers can be used to stratify and predict locally advanced PC. Further studies must be undertaken to create machine learning algorithms using larger training and testing datasets to facilitate earlier detection of PC." @default.
- W4306179211 created "2022-10-14" @default.
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- W4306179211 date "2022-10-17" @default.
- W4306179211 modified "2023-09-27" @default.
- W4306179211 title "A Novel Neural Network to Predict Locally Advanced Pancreatic Cancer Using 4 Urinary Biomarkers: REG1A/1B, LYVE1, and TFF1" @default.
- W4306179211 doi "https://doi.org/10.1097/01.xcs.0000894076.58208.27" @default.
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