A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study
Bo Li, Beilei Wang, Pengjie Zhuang, Hongwei Cao, Shengyong Wu, Zhendong Tan, Suizhi Gao, Penghao Li, Wei Jin, Zhuo Shao, Kailian Zheng, Lele Wu, Bai Gao, Yang Wang, Hui Jiang, Shiwei Guo, Liang He, Yan Yang, Gang Jin- General Medicine
- Surgery
Objective:
To construct a novel Tumor-Node-Morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma (PDAC).
Method:
This retrospective study with 1,657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, concordance index (C-index) and area under receiver operating curve (AUC).
Results:
The precision, recall, and F1 scores of the NLP model were 88.83%, 89.89%, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (Internal validation, 0.58
Conclusion:
These findings suggest that the TNMor system performed better than the TNM system in predicting PDAC prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.