Nomogram-based prediction of survival in patients with advanced oesophagogastric adenocarcinoma receiving first-line chemotherapy: a multicenter prospective study in the era of trastuzumab

Background: To develop and validate a nomogram and web-based calculator to predict overall survival (OS) in Caucasian-advanced oesophagogastric adenocarcinoma (AOA) patients undergoing first-line combination chemotherapy. Methods: Nine hundred twenty-four AOA patients treated at 28 Spanish teaching hospitals from January 2008 to September 2014 were used as derivation cohort. The result of an adjusted-Cox proportional hazards regression was represented as a nomogram and web-based calculator. The model was validated in 502 prospectively recruited patients treated between October 2014 and December 2016. Harrell's c-index was used to evaluate discrimination. Results: The nomogram includes seven predictors associated with OS: HER2-positive tumours treated with trastuzumab, Eastern Cooperative Oncology Group performance status, number of metastatic sites, bone metastases, ascites, histological grade, and neutrophil-to-lymphocyte ratio. Median OS was 5.8 (95% confidence interval (CI), 4.5–6.6), 9.4 (95% CI, 8.5–10.6), and 14 months (95% CI, 11.8–16) for high-, intermediate-, and low-risk groups, respectively (P<0.001), in the derivation set and 4.6 (95% CI, 3.3–8.1), 12.7 (95% CI, 11.3–14.3), and 18.3 months (95% CI, 14.6–24.2) for high-, intermediate-, and low-risk groups, respectively (P<0.001), in the validation set. The nomogram is well-calibrated and reveals acceptable discriminatory capacity, with optimism-corrected c-indices of 0.618 (95% CI, 0.591–0.631) and 0.673 (95% CI, 0.636–0.709) in derivation and validation groups, respectively. The AGAMENON nomogram outperformed the Royal Marsden Hospital (c-index=0.583; P=0.00046) and Japan Clinical Oncology Group prognostic indices (c-index=0.611; P=0.03351). Conclusions: We developed and validated a straightforward model to predict survival in Caucasian AOA patients initiating first-line polychemotherapy. This model can contribute to inform clinical decision-making and optimise clinical trial design