Cancer medicine · 2026
A Streamlined Protocol for Developing a Clinicopathological Prediction Model for Patient Survival of Post-Resection of Pancreatic Cancer.
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Ma Yi, Lee Eunice, Asadi Khashayar, Nikfarjam Mehrdad, He Hong
Аннотация
BACKGROUND: Pancreatic ductal adenocarcinoma (PDA) is one of the most malignant solid cancers. As surgery is the only cure, prediction of long-term survival post-resection is critical to guide patient selection for the subsequent treatment. Tumour immune evasion plays a key role in PDA tumorigenesis.
MATERIALS AND METHODS: Using a streamlined protocol, we developed a clinicopathological prediction model for the overall survival of patients with PDA after resection by integrating tumour immunological features and clinical data. Multiplex immunohistochemistry was performed using human tumour microarray samples. The results were combined with retrospectively collected clinical data of 79 patients. Variables were selected by least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation to develop the prediction model. The performance of the model was assessed using the concordance index, receiver operating characteristic curve, calibration plot and decision curve analysis. The model was validated by bootstrap resampling.
RESULTS: Cancer cell MHC I intensity, CD4+ T cell to tumour cell ratio, resection margin status and tumour T stage were identified for prediction model development using Cox proportional hazard regression. Discrimination of developed model was moderate on the time-dependent area under curve at one (0.698), three (0.765) and five (0.825) years. A small decrease in the overall c-index from 0.67 to 0.652 was shown in the internal validation by bootstrapping.
CONCLUSION: Our protocol provided a framework for developing a complex model that will significantly contribute to clinical practice.
MATERIALS AND METHODS: Using a streamlined protocol, we developed a clinicopathological prediction model for the overall survival of patients with PDA after resection by integrating tumour immunological features and clinical data. Multiplex immunohistochemistry was performed using human tumour microarray samples. The results were combined with retrospectively collected clinical data of 79 patients. Variables were selected by least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation to develop the prediction model. The performance of the model was assessed using the concordance index, receiver operating characteristic curve, calibration plot and decision curve analysis. The model was validated by bootstrap resampling.
RESULTS: Cancer cell MHC I intensity, CD4+ T cell to tumour cell ratio, resection margin status and tumour T stage were identified for prediction model development using Cox proportional hazard regression. Discrimination of developed model was moderate on the time-dependent area under curve at one (0.698), three (0.765) and five (0.825) years. A small decrease in the overall c-index from 0.67 to 0.652 was shown in the internal validation by bootstrapping.
CONCLUSION: Our protocol provided a framework for developing a complex model that will significantly contribute to clinical practice.