Developing and validating nomograms for predicting survival in patients with pancreatic squamous cell carcinoma based upon machine learning
Objective Pancreatic Squamous Cell Carcinoma(PSCC)has a poor prognosis and it lacks individualized prognostic tools.This study aimed to construct prognostic nomograms for PSCC patients based upon machine learning and using large-scale real-world data from the database of SEER,provide precise and individualized prognostic assessments and offer valuable references for clinical decision-making.Methods From 2000 to 2019,the relevant clinical data of 367 pathologically diagnosed PSCC patients were extracted from the database of SEER.They were randomized by a ratio of 7∶3 into training(n=256)and verification(n=111)sets.Multivariate Cox proportional hazard model,LASSO regression and random survival forest model were utilized for identifying independent prognostic factors for patient survival.These factors were utilized for constructing nomograms for predicting cancer specific survival(CSS)and total survival(OS)at Month 3/6.Subsequently,the models were internally and externally validated in training and validation sets by concordance index(C-index),receiver operating characteristic(ROC)and calibration curves for assessing their accuracy and predictive capacity.Results The median follow-up period in training and verification sets were 3(1,7)and 2(1,8)month.Baseline profiles were comparable between two groups(all P>0.05).Multivariate Cox proportional hazard model analysis indicated that tumor size,M/N stage,surgery and chemotherapy were independent influencing factors for OS/CSS.LASSO regression analysis revealed that M stage,surgery and chemotherapy were associated with OS/CSS.For OS,top four scoring variables for via random survival forest model were chemotherapy,M stage,surgery and age;For CSS,chemotherapy,M stage,surgery and tumor size.Nomograms for predicting OS/CSS at Month 3/6 were developed based upon these independent prognostic factors.Validation results showed that C-index for OS in training and verification sets were 0.753(95%CI:0.720-0.790)and 0.723(95%CI:0.660-0.780)and for CSS 0.749(95%CI:0.720-0.780)and 0.721(95%CI:0.660-0.780).ROC curve analysis indicated that AUC values for OS in training and verification sets were 79.8%and 75.9%at Month 3,78.9%and 76.8%at Month 6 and 78.7%and 77.5%at Month 12;for CSS,79.3%and 76.3%at Month 3,78.6%and 76.9%at Month 6 and 77.4%and 78.4%at Month 12 respectively.Calibration curve analysis demonstrated a decent agreement between predicted and actual OS/CSS.Both were closely situated near ideal 45° reference line,demonstrating a high consistency.Conclusion Age,M stage,tumor size,surgery and chemotherapy are independent prognostic factors for PSCC patients.And the above constructed nomogram prediction models exhibit favorable predictive value and facilitate personalized therapeutics for PSCC patients in clinical practices.
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