Cy-TEST - A new platform for training and testing in cytopathology
Introduction/ Background: Clinical training at the European level requires flexible ways to provide education across borders with the goal of a unified way to teach and assess quality. The Cy-TEST project focuses on Cytological Training at European Standard through Telepathology. The project (2014-1-IT01-KA202-002607) has been approved and funded in 2014 by EU within the ERASMUS+ Program. The project consortium is composed of 4 leading university Institutions (COREP and University of Turin, University of Padua, Imperial College of London, IPATIMUP/University of Porto and University of Graz) with technological development and support provided by CRS4. In addition, it benefits from the collaboration of International Organizations (EFCS, Eurocytology, OME) and is open to contributions from additional groups and Societies.
Aims: Our aim was the establishment of a platform for the sharing of digital pathology images and of an auxiliary system that will use the latter platform for the distribution of cytologist training courses with an integrated virtual microscopy capability.
Methods: The Cy-TEST platform is based on the integration between Moodle, an e-learning platform designed to create personalized learning environments, and OME OMERO, a well-known open source software for visualization, management and analysis of biological microscope images. The former is used to provide access to a database of questions produced by specialized trainers and the latter provides access to digital pathology images and related metadata. We chose to base our infrastructure upon Moodle because it is one of the top leading platform for online education with a large community of users across both academic and enterprise level, highly customizable and modular. OMERO was chosen because of its compatibility with a large number of image formats for digital pathology images, its handling of image metadata (i.e., TAGs and Regions of Interest) and its easily extensible web platform.
Results: The web platform can be used with a wide range of devices, it is compatible with most of the image formats produced by digital slides scanners and it can scale to a wide student body. Teachers can create courses, populate the Question Bank and aggregate questions in quizzes, while students can take classes and tests. When creating questions, teachers can choose images previously loaded and annotated. We provide two new types of questions: multiple choice, focused on an image and its ROIs, and interactive, where students identify areas on the image by markers that will be automatically compared to instructor’s specified ROIs. The currently deployed system holds already a set of several hundreds of images classified by categories (e.g., tissue type and diseases) with associated ROIs identified by pathologists. The Cy-TEST platform provides a full technological solution for a more homogeneous training and testing of cytotechnicians and cytopathologists with uniform quality level assurance mechanism. The system could be easily extended to support the teaching of histopathological diagnosis. Moreover, the Cy-TEST platform paves the way to an e-QUATE test, thus providing an efficient and economical way to replicate the test at European scale (see Branca et al., 2000). The sustainability of the platform and the supported educational material (images, questions and evaluation algorithms) will be guaranteed by its integration in EFCS activities. We expect to distribute the Cy-TEST System for validation by October 2016, for further information contact firstname.lastname@example.org.
 Al-Janabi, S., Huisman, A., & Van Diest, P. J., Digital pathology: current status and future perspective. Histopathology 2012, 61(1):1-9.
 Branca, M., Coleman, D.V., Marsan, C., Morosini, P., Quality assurance and continuous quality improvement in laboratories which undertake cervical cytology, 2000 [Internet]. Available from http://goo.gl/k4DnnD
 Cy-TEST Moodle plugins [Computer software] (2016). Available from https://github.com/crs4/moodle.omero-qtypes
 Dee, F. R., Virtual microscopy in pathology education. Human pathology 2009, 40(8):1112-1121.
 Deep Zoom File Format Overview. Available from Microsoft Developer Network website: https://msdn.microsoft.com/en-us/library/cc645077(v=vs.95).aspx
 Herrmann, F. E., Lenski, M., Steffen, J., Kailuweit, M., Nikolaus, M., Koteeswaran, R., Mayr, D., A survey study on student preferences regarding pathology teaching in Germany: a call for curricular modernization. BMC medical education 2015, 15(1):1.
 Kayser, K., Borkenfeld, S., Carvalho, R., Kayser, G., Contribution of Measurement to morphologic Diagnostics. Diagnostic Pathology 2016, 2:105.
 Kunze, K. D., Limits of Morphological Diagnostics. Diagnostic Pathology 2016, 2:103.
 Moodle [Computer software], Moodle project 2016. Retrieve from https://moodle.org
 ome-seadragon [Computer software], 2016. Available from https://github.com/crs4/ome_seadragon
 OpenSeadragon [Computer software], OpenSeadragon contributors, 2013. Available from http://openseadragon.github.io
 Balkan A., Openslide [Computer software], 2015. Available from http://openslide.org
 Goode, A., Gilbert B., Harkes J., Jukic D., Satyanarayanan M., OpenSlide: A vendor-neutral software foundation for digital pathology, Journal of Pathology Informatics 2013, 4(1): 27.
 Ordi, O., Bombí, J. A., Martínez, A., Ramírez, J., Alòs, L., Saco, A., Ordi, J., Virtual microscopy in the undergraduate teaching of pathology. Journal of pathology informatics 2015, 6.
 Lehni J., Puckey J., Paper.js [Computer software], 2011. Available from http://paperjs.org
 Redis [Computer software], RedisLabs 2016. Available from: http://redis.io
 Sinn, H. P., Hosting and managing large sets of virtual microscopy slides on the internet for E-learning and for reference. Diagnostic Pathology 2013, 8(Suppl 1): S19.
 Young, J. A., Diagnostic problems in fine needle aspiration cytopathology of the salivary glands. Journal of clinical pathology 1994, 47(3):193.
 Zioga, C., Destouni, C., Cytology ABCDE: A Practical ABCDE Algorithm for Cytology Diagnosis, Diagnostic Pathology 2015, 1:11.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
4. In case of virtual slide publication the authors agree to copy the article in a structural modified version to the journal's VS archive.