Background: Esophageal cancer (EC) is a disease with a poor prognosis. While treatment options have been improved, there is no consensus for surveillance strategies following therapy with curative intent. As the incidence of EC is rising and a large fraction of patients will experience disease recurrence, the need for evidence-based treatment and optimal surveillance is evident.
View Article and Find Full Text PDFThe most common functional challenge after Ivor-Lewis esophagectomy is delayed emptying of the gastric conduit. One of the primary endoscopic treatment strategies is performing a pyloric dilatation. However, the effects of dilation have never been scientifically proven.
View Article and Find Full Text PDFStoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains.
View Article and Find Full Text PDFIntroduction: In esophageal cancer, histopathologic response following neoadjuvant therapy and transthoracic esophagectomy is a strong predictor of long-term survival. At the present, it is not known whether the initial tumor volume quantified by computed tomography (CT) correlates with the degree of pathologic regression.
Methods: In a retrospective analysis of a consecutive patient cohort with esophageal adenocarcinoma, tumor volume in CT prior to chemoradiotherapy or chemotherapy alone was quantified using manual segmentation.