A preoperative clinical staging and metabolic imaging model to predict prognosis in early-stage esophageal adenocarcinoma.

Abstract

63 Background: Accurate prediction of outcome for neo-adjuvant therapy in early stage esophageal adenocarcinoma (EAC) is essential for clinical trial design, patient counseling and treatment decisions. The use of 18F-fluorodeoxyglucose positron emission tomography (PET) has improved the staging of EAC and can predict pathological response and survival. Our aim was to develop a pre-operative prognostic model based on metabolic imaging and clinical parameters. Methods: Between April 2004 and December 2010, 118 patients with early stage adenocarcinoma of the distal esophagus or gastro-esophageal junction were treated with platinum and fluorouracil-based combination chemotherapy prior to surgical resection. Patients were staged by endoscopic ultrasound and PET/CT was performed prior to, and upon completion of, chemotherapy. A metabolic response was defined as a >35% reduction in the maximum standard uptake variable (SUVmax). The final prognostic model was selected by multivariate Cox regression analyses and the concordance-index (c-index) was used to assess the fit of the final multivariate model. Results: In both the univariate and multivariate analysis of patient prognosis post-surgery, five pre-surgery factors were included: clinical T and N staging, PET response, neutrophil-lymphocyte ratio and albumin levels. The majority of the patients were T3 (82.1%) and N1 (73.7%) and 69.8% of patients demonstrated a PET response. In the univariate analysis PET response was the only significant prognostic factor with a median overall survival (OS) of 60.4 months for responders compared to 23.3 months for non-responders (p=0.0241). The final multivariate model of T staging and PET response had a c-index of 0.624 with T3/PET non-response resulting in a poorer prognosis compared to T3/PET response and T2/PET responder or non-responder (median OS 11.5 vs 44.5 vs undefined months; p=0.0284). We seek to validate our model in an independent patient dataset to assess its predictive capability. Conclusions: Metabolic imaging and clinical staging can be combined in a prognostic model in early stage EAC. Our model can inform patient counseling and the development of clinical trial strategies.