It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.

237 Background: There is a need for prognostic tools in metastatic pancreatic cancer (MPC) because they have the potential to optimize patients’ selection in clinical trail and guide treatment strategies. We address this issue by developing and validating a prognostic nomogram with open clinical trail data. Methods: Data from the comparator arm of three clinical trails (NCT00844649, NCT01124786 and NCT00574275) in MPC treated with gemcitabine as first-line chemotherapy were analyzed. The former two were taken as training cohort while NCT00574275 was used as validation cohort. Cox regression model was used to investigate prognostic factors from twenty-three baseline characteristics in training cohort. Based on these factors, a nomogram was developed and internally validated with Harrell’s Concordance index (C-index) and calibration plots. It was further externally validated in the validation cohort. Results: In the training cohort (n = 445), performance status, liver metastasis, CA19-9 log-value, neutrophil, platelet (PLT) and albumin were independent prognostic factors for overall survival (OS). A nomogram based on these factors was generated to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C-index: 0.683) and good calibration. The prognostic score based on nomogram could stratify patients into three-risk groups with median OS of 11.7, 7.0 and 3.7 months (P < 0.001). The nomogram was further externally validated in validation cohort (n = 273, C-index: 0.699); median OS of 10.6, 7.3 and 4.0 months for the three-risk groups (P < 0.001). Conclusions: The prognostic nomogram can accurately predict OS for MPC treated with gemcitabine as first-line chemotherapy. [Table: see text]

Objective This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy. Methods This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort. Results The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility. Conclusion The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.

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