Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study

Abstract

Background: Stage IV pancreatic cancer (PC) has a poor prognosis and lacks individualized prognostic tools. Current survival prediction models are limited, and there is a need for more accurate, personalized methods. The Surveillance, Epidemiology, and End Results (SEER) database offers a valuable resource for studying large patient cohorts, yet machine learning-based nomograms for stage IV PC prognosis remain underexplored. This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival (CSS) and overall survival (OS) with high accuracy in stage IV PC patients.

Aim: To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.

Methods: Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 were extracted from the SEER database. Patients were randomly divided into a training set and a validation set in a 7:3 ratio. Multivariate Cox proportional hazards, Least Absolute Shrinkage and Selection Operator regression, and Random Survival Forest models were used to identify prognostic variables. A nomogram was constructed to predict CSS and OS at 6, 12, and 18 months. The C-index, receiver operating characteristic curves, and calibration curves were used to evaluate the model's predictive performance.

Results: A total of 1662 patients were included (1163 in the training set, 499 in the validation set). The median follow-up times were 4 months [interquartile range (IQR): 1-10 months] for the training set and 4 months (IQR: 1-11 months) for the validation set. Key independent prognostic factors identified included age, race, marital status, tumor location, N stage, grade, surgery, chemotherapy, and liver metastasis. The nomogram accurately predicted OS and CSS at 6, 12, and 18 months, with a C-index of 0.727 (OS) and 0.727 (CSS) in the training set, and 0.719 (OS) and 0.716 (CSS) in the validation set. Calibration curves demonstrated excellent model accuracy.

Conclusion: The nomogram developed using age, grade, chemotherapy, surgery, and liver metastasis as predictors can reliably estimate survival outcomes for stage IV PC patients and offers a potential tool for individualized clinical decision-making.

Keywords: Cancer survival; Machine learning; Prognosis; Prognostic model; Stage IV pancreatic ductal adenocarcinoma; Surveillance Epidemiology, and End Results Program.

Figure 1

Pearson’s correlation coefficients between variable pairs.

Figure 2

Forest plot for overall survival and cancer-specific survival using multivariate Cox regression analysis in patients. A: Overall survival; B: Cancer-specific survival.

Figure 3

Feature selection based on Least Absolute Shrinkage and Selection Operator regression. A: Curve of Least Absolute Shrinkage and Selection Operator (LASSO) regression coefficients with changing Log (λ) for overall survival (OS); B: Curve of 10-fold cross-validated C-index with changing Log (λ) for OS; C: Curve of LASSO regression coefficients with changing Log (λ) for cancer-specific survival (CSS); D: Curve of 10-fold cross-validated C-index with changing Log (λ) for CSS.

Figure 4

Importance scores based on Random Survival Forest for overall survival and cancer-specific survival. A: Overall survival; B: Cancer-specific survival.

Figure 5

Nomogram predicting overall survival and cancer-specific survival for stage IV pancreatic cancer at 6, 12, and 18 months. OS: Overall survival; CSS: Cancer-specific survival.

Figure 6

Receiver operating characteristic curves for 6-, 12-, and 18-month overall survival and cancer-specific survival in the training cohort and validation cohort. A: Overall survival; B: Cancer-specific survival. OS: Overall survival; CSS: Cancer-specific survival; AUC: Area under the curve.

Figure 7

Calibration curves for 6-, 12-, and 18-month overall survival and cancer-specific survival in the training cohort and validation cohort. A: The training cohort; B: Validation cohort. OS: Overall survival; CSS: Cancer-specific survival.

Figure 8

Risk-stratified survival curves of overall survival and cancer-specific survival in the training cohort, and overall survival and cancer-specific survival in the validation cohort. A and B: Risk-stratified survival curves of overall survival (OS) (A) and cancer-specific survival (CSS) (B) in the training cohort; C and D: OS (C) and CSS (D) in the validation cohort.

Figure 9

Comparison of decision curve analysis for the nomogram and tumor-node-metastasis staging system in predicting 6-month, 12-month, and 18-month overall survival and cancer-specific survival in both training and validation sets. A: Training set overall survival (OS); B: Training set cancer-specific survival (CSS); C: Validation set OS; D: Validation set CSS. TNM: Tumor-node-metastasis.