Abstract
Background: Prediction models for the overall survival of pancreatic cancer remain unsatisfactory. We aimed to explore artificial neural networks (ANNs) modeling to predict the survival of unresectable pancreatic cancer patients.Methods: Thirty-two clinical parameters were collected from 221 unresectable pancreatic cancer patients, and their prognostic ability was evaluated using univariate and multivariate logistic regression. ANN and logistic regression (LR) models were developed on a training group (168 patients), and the area under the ROC curve (AUC) was used for comparison of the ANN and LR models. The models were further tested on the testing group (53 patients), and k-statistics were used for accuracy comparison.Results: We built three ANN models, based on 3, 7, and 32 basic features, to predict 8 month survival. All 3 ANN models showed better performance, with AUCs significantly higher than those from the respective LR models (0.811 vs. 0.680, 0.844 vs. 0.722, 0.921 vs. 0.849, all p < 0.05). The ability of the ANN models to discriminate 8 month survival with higher accuracy than the respective LR models was further confirmed in 53 consecutive patients.Conclusion: We developed ANN models predicting the 8 month survival of unresectable pancreatic cancer patients. These models may help to optimize personalized patient management.
Highlights
Pancreatic cancer is one of the leading causes of cancerrelated mortality worldwide (Ferlay et al, 2015)
The ability of the artificial neural networks (ANNs) models to discriminate 8 month survival with higher accuracy than the respective logistic regression (LR) models was further confirmed in 53 consecutive patients
The following clinical and biochemical data were collected before the patient received chemotherapy: age, sex, main vascular invasion, clinical TNM staging, metastasis, ascites, size of the largest tumor in the pancreas and liver, tumor position in the pancreas, stomach invasion, duodenum invasion, liver metastasis number, carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), albuminto-globulin ratio (AGR), alanine transaminase (ALT), aspartate transaminase (AST), creatinine, total bilirubin, direct bilirubin, indirect bilirubin, haemoglobulin, neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, hepatitis B virus, and white blood cell (WBC) count
Summary
Pancreatic cancer is one of the leading causes of cancerrelated mortality worldwide (Ferlay et al, 2015). Previous clinical research has shown the predictive effect of clinical pathological biomarkers such as tumor heterogeneity, main vessel invasion, and complexity at the genomic, epigenetic, and metabolic levels in patients with pancreatic cancer (Kleeff et al, 2016; Neoptolemos et al, 2018; Naito et al, 2019). These predictive biomarkers still have many limitations. Prediction models for the overall survival of pancreatic cancer remain unsatisfactory. We aimed to explore artificial neural networks (ANNs) modeling to predict the survival of unresectable pancreatic cancer patients
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