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Updated: Oct 29, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Prognostic stratification based on a novel nomogram for left-sided pancreatic adenocarcinoma after surgical
Zuyi Ma1,2, Bowen Huang3, Shanzhou Huang1
1Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology Guangzhou 510080, China.
Abstract:
Left-sided pancreatic adenocarcinoma (LPAC) has a poorer prognosis and has some distinct features compared to cancer of pancreatic head. A reliable model to predict the prognosis of LPAC following surgery is needed in clinical practice. Our study included 231 patients with resected LPAC from 3 Chinese pancreatic disease centers. Cox-regression analysis was conducted to identify independent risk factors of LAPC. Then we established a nomogram and performed C-index, receiver operating characteristic curve, calibration plot and decision curve analysis to assess its discrimination and calibration. As a result, CA19-9, surgical margin, tumor differentiation, lymph node metastasis, and postoperative adjuvant chemotherapy were identified as significant prognostic factors. Based on these predictors, a novel nomogram was constructed. The nomogram achieved high C-indexes in the training cohort (0.805) and validation cohort (0.719), which were superior than the AJCC-8 staging system and other nomograms. The area under curve of the nomogram for predicting patients survival at 1-, 2-, and 3-year in training cohort were more than 0.8. Kaplan-Meier survival curve for the subgroups stratified based on the nomogram showed a better separation than the AJCC-8 stage I, II, III, indicating a superior ability of risk stratification for our model. In summary, we constructed a nomogram which showed a better predictive ability for patients' survival with LPAC after surgical resection than the AJCC staging system and other predictive models. Our model would be helpful to discriminate high-risk LPAC and facilitate clinical decision making.
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