The optimal management of resectable oesophageal adenocarcinoma is controversial, with many centres using neoadjuvant chemotherapy following the Medical Research Council (MRC) oesophageal working group (OE02) trial and the MRC Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial. The more intensive MAGIC regimen is used primarily in gastric cancer but some also use it for oesophageal cancer. A database of cancer resections (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) provided information on survival of patients following either OE02 or MAGIC-type treatment. The data were compared using Kaplan-Meier analysis. Straight-to-surgery patients were also reviewed and divided into an 'early' cohort (2001( -2006 and a 'late ' cohort (2006' cohort ( -2013 to estimate changes in survival over time. Subgroup analysis was performed for responders (tumour regression grade [TRG] 1-3) versus non-responders (TRG 4 and 5) and for anatomical site (gastro-oesophageal junction [GOJ] vs oesophagus). An OE02 regimen was used for 97 patients and 275 received a MAGIC regimen. Those in the MAGIC group were of a similar age to those undergoing OE02 chemotherapy but the proportion of oesophageal cancers was higher among MAGIC patients than among those receiving OE02 treatment. MAGIC patients had a significantly lower stage following chemotherapy than OE02 patients and a higher median overall survival although TRG was similar. On subgroup analysis, this survival benefit was maintained for GOJ and oesophageal cancer patients as well as non-responders. Analysis of responders showed no difference between regimens. 'Late' group straight-to-surgery patients were significantly older than those in the 'early' group. Survival, however, was not significantly different for these two cohorts. Although the original MAGIC trial comprised few oesophageal cancer cases, our patients had better survival with MAGIC than with OE02 chemotherapy in all anatomical subgroups, even though there was no significant change in operative survival over the time period in which these patients were treated. The use of the MAGIC regimen should therefore be encouraged in cases of operable oesophagogastric adenocarcinoma.

No abstract

15Structure-from-Motion Multi View Stereo (SfM-MVS) photogrammetry is a technique 16 by which volumetric data can be derived from overlapping image sets, using changes of an 17 objects position between images to determine its height and spatial structure. Whilst SfM-MVS 18 has fast become a powerful tool for scientific research, its potential lies beyond the scientific 19 setting, since it can aid in delivering information about habitat structure, biomass, landscape 20 topography, spatial distribution of species in both two and three dimensions, and aid in 21 mapping change over time -both actual and predicted. All of which are of strong relevance for 22 the conservation community, whether from a practical management perspective or 23 understanding and presenting data in new and novel ways from a policy perspective. 24 For practitioners outside of academia wanting to use SfM-MVS there are technical 25 barriers to its application. For example, there are many SfM-MVS software options, but 26 knowing which to choose, or how to get the best results from the software can be difficult for 27 the uninitiated. There are also free and open source software options (FOSS) for processing 28 data through a SfM-MVS pipeline that could benefit those in conservation management and 29 policy, especially in instances where there is limited funding (i.e. commonly within grassroots 30 or community-based projects). This paper signposts the way for the conservation community 31 to understand the choices and options for SfM-MVS implementation, its limitations, current 32 best practice guidelines and introduces applicable FOSS options such as OpenDroneMap, 33MicMac, CloudCompare, QGIS and speciesgeocodeR. It will also highlight why and where 34 this technology has the potential to become an asset for spatial, temporal and volumetric studies 35 of landscape and conservation ecology. 36 37 38 39 Relatively new technologies, such as drones (1,2), advances in computational power 40 (3,4), improvements in digital cameras (5), along with classic remote sensing platforms such 41 as kite aerial photography (KAP) and balloons (6,7), have all combined to help create a new 42 opportunity in remote sensing research utilising SfM-MVS photogrammetry. It is these 43 convergent developments that now position SfM-MVS as a cost-effective and democratic tool 44 for conservation research.45SfM-MVS approaches use parallax (i.e. minor displacements in similar images) in 46 conjunction with computer vision techniques, in order to derive 3D structures from 2D data moving object). Data such as overlapping digital photographs and GPS/GNSS (Global 49 Positioning System/Global Navigation Satellite System) information are stitched together, 50 adding distance (X and Y) and height (Z) values to pixels (points) in the combined data, 51 producing a "point cloud" (Figure 1). From this, SfM-MVS software is able to output two 52 dimensional orthographic images containing detailed geographical location information, 53 alongside 2.5 dimensional reconstruct...