Cancers, Vol. 11, Pages 57: Prognostic Factors for Advanced Pancreatic Cancer Treated with Gemcitabine Plus S-1: Retrospective Analysis and Development of a Prognostic Model

Cancers, Vol. 11, Pages 57: Prognostic Factors for Advanced Pancreatic Cancer Treated with Gemcitabine Plus S-1: Retrospective Analysis and Development of a Prognostic Model

Cancers doi: 10.3390/cancers11010057

Authors: Ching-Fu Chang Pei-Wei Huang Jen-Shi Chen Yen-Yang Chen Chang-Hsien Lu Pei-Hung Chang Yu-Shin Hung Wen-Chi Chou

Gemcitabine plus S-1 (GS) is commonly used to treat advanced pancreatic cancer (APC) in Asia. Few clinical experiments have demonstrated the clinical efficacy of GS in routine clinical practice. We aimed to identify the prognostic factors and develop a prognostic model for survival prediction in patients with APC, treated with GS. Records of 111 patients with newly diagnosed APC who received first-line palliative GS chemotherapy during 2010–2016 in Taiwan were analyzed retrospectively. Univariate and multivariate analyses were performed for the identification of prognostic factors. A prognostic model using prognosticators from the multivariate analysis was developed for survival prediction. The median overall survival (OS) for the cohort was 9.3 months (95% confidence interval [CI], 8.0–10.6). The prognostic model was constructed based on four independent prognosticators: performance status, tumor stage, pre-treatment albumin level, and neutrophil-to-lymphocyte ratio. Patients were categorized by tertiles into good, intermediate, and poor prognostic groups. The median OS values for each of these groups were 21.1 (95% CI, 8.2–33.9), 9.2 (95% CI, 8.3–10.1), and 5.8 months (95% CI, 4.4–7.1; log-rank p < 0.001), respectively. The bootstrapped corrected C-index of this model was 0.80 (95% CI, 0.71–0.89). The developed model was robust and could accurately predict survival in this population, and can assist clinicians and patients in survival discrimination and the determination of appropriate medical care goals. Additional research is needed to externally validate the model’s performance.

http://bit.ly/2TxpVPC