Yield of esophagogastroduodenoscopy and colonoscopy in cancer of unknown primary

Objectives: Carcinoma of unknown primary origin (CUP) is heterogeneous group of cancers. Role of gastrointestinal (GI) endoscopy in this entity is under investigated. Aim of this study was to evaluate yield of Colonoscopy and Esophagogastroduodenoscopy (EGD) in localizing primary tumor in patients with CUP. METHODOLOGY: Patients with histopathologically proven CUP who underwent colonoscopy / EGD to find the primary tumor from December 2009 to December 2011 were included in the study. Abdominal symptoms and cytokeratin (CK) 7 and 20 markers were correlated with presence of primary in GI tract. Results: After giving informed consent 86 patients were included in final analysis. All patients underwent colonoscopy while 60(70%) got EGD along with colonoscopy. Mean age was 55.10 +/-11.94 years with 52(60%) male. Abdominal symptoms were present in 50%. CK7+/CK20- in 34(40%); CK7-/CK20+ in 2(2%) while CK7+/20+ in 7(8%) of metastatic tumor samples. Liver was metastatic site in 47(55%), Lymph node 12(14%) and Ascites in 8(9%). Endoscopy detected primary in 6 (7%) patients with 3 each in stomach and colon. No association of abdominal symptoms and cytokeratin markers was found with presence of GI primary site. CONCLUSION: Yield of localizing primary lesion in the GI tract by pan-endoscopy was limited. Abdominal symptoms and cytokeratin markers do not predict presence of gastrointestinal malignancies

Power of GIS Mapping: ATLAS Flood Maps 2022 (Short Paper)

In this paper, we are introducing an efficient method based on the GIS technology, to design data immediate and analysis-ready mapping from open GIS and remote sensing data, vector and raster data into a single visualization to facilitate fast and flexible mapping, also referred to as ATLAS maps. The Google Earth Engine approach is used to pre-process the satellite data, while ArcGIS software is to integrate all the data layers. Since the ArcGIS software is included as a default dependency in GIS and remote sensing data, the proposed method provides a cross-platform and single-technology solution for handling flood mapping. For now, we conducted flood analysis using the latest open data for Pakistan and Nigeria countries, then elaborated on the advantages of each data for flood mapping with respect to inundated areas, rainfall analysis, and affected populations, health, and education facilities. Given a wide range of tasks that can benefit from the method, future work will extend the methodology to heterogeneous geodata (vector and raster) to support seamless and make it automatic interfaces