Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer

Background: Citizen science, scientific research conducted by non-specialists, has the potential to facilitate biomedical research using available large-scale data, however validating the results is challenging. The Cell Slider is a citizen science project that intends to share images from tumors with the general public, enabling them to score tumor markers independently through an internet-based interface. Methods: From October 2012 to June 2014, 98,293 Citizen Scientists accessed the Cell Slider web page and scored 180,172 sub-images derived from images of 12,326 tissue microarray cores labeled for estrogen receptor (ER). We evaluated the accuracy of Citizen Scientist's ER classification, and the association between ER status and prognosis by comparing their test performance against trained pathologists. Findings: The area under ROC curve was 0.95 (95% CI 0.94 to 0.96) for cancer cell identification and 0.97 (95% CI 0.96 to 0.97) for ER status. ER positive tumors scored by Citizen Scientists were associated with survival in a similar way to that scored by trained pathologists. Survival probability at 15 years were 0.78 (95% CI 0.76 to 0.80) for ER-positive and 0.72 (95% CI 0.68 to 0.77) for ER-negative tumors based on Citizen Scientists classification. Based on pathologist classification, survival probability was 0.79 (95% CI 0.77 to 0.81) for ER-positive and 0.71 (95% CI 0.67 to 0.74) for ER-negative tumors. The hazard ratio for death was 0.26 (95% CI 0.18 to 0.37) at diagnosis and became greater than one after 6.5 years of follow-up for ER scored by Citizen Scientists, and 0.24 (95% CI 0.18 to 0.33) at diagnosis increasing thereafter to one after 6.7 (95% CI 4.1 to 10.9) years of follow-up for ER scored by pathologists. Interpretation: Crowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input

Developing Practical Support Tools using Dashboards of Information

International audienceOver 50 years of research on how to support managers’ decision making, numerous solutions have been proposed under a variety of banners, as discussed in the contributions presented in this book. One of the recent terms to have been proposed is Business Inteligence (BI), which aims at leveraging new technologies for the gathering, presentation, and analysis of up-to-date data about the firm’s operations to top management. BI is largely distinguished from previous concepts by its reliance on new platforms and technologies (for instance web technologies) to provide nimbler solutions, more responsive to managerial needs than earlier types of systems. As part of BI, the concept of dashboards of information or digital dashboards has been revisited, notably by software vendors. This chapter explains in detail what dashboards of information are and how to develop them. It considers where business data come from and how to use them to support decision making with a dashboard. Using the concept of cognitive levels, it differentiates between different types of aplications of the dashboard concept. Finally, the chapter aspects of its activities and concludes presents an illustrative case study of a firm seeking to develop a nimble tool for measuring and understanding the key that it is the content of the dashboard and the context in which it is used that are the key elements in the process of developing a dashboard