Affiliation:
1. Department of Hepatobiliary Pancreatic Surgery
2. Department of Marine Biomedicine and Polar Medicine, Naval Medical Center, Navy Military Medical University
3. Department of School of Computer Science and Technology, East China Normal University
4. Department of Information, Changhai Hospital
5. Department of Military Health Statistics
6. Department of Pathology, Shanghai Fourth People’s Hospital, Tongji University School of Medicine
7. Department of Pathology, Changhai Hospital, Naval Military Medical University
8. Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai, People’s Republic of China
Abstract
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.
Method:
This retrospective study with 1657 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, the concordance index (C-index), and the area under the 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 vs. 0.54, P<0.001; external validation, 0.64 vs. 0.63, P<0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P<0.001; 0.64 vs. 0.60, P=0.017; 0.69 vs. 0.62, P=0.001; external validation: 0.69 vs. 0.65, P=0.098; 0.68 vs. 0.64, P=0.154; 0.64 vs. 0.55, P=0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P<0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P<0.001; 0.64 vs. 0.58, P<0.001; 0.67 vs. 0.61, P<0.001; respectively) compared with the TNM system.
Conclusion:
These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.
Publisher
Ovid Technologies (Wolters Kluwer Health)