Wikis, blogs and podcasts: a new generation of Web-based tools for virtual collaborative clinical practice and education

BACKGROUND: We have witnessed a rapid increase in the use of Web-based 'collaborationware' in recent years. These Web 2.0 applications, particularly wikis, blogs and podcasts, have been increasingly adopted by many online health-related professional and educational services. Because of their ease of use and rapidity of deployment, they offer the opportunity for powerful information sharing and ease of collaboration. Wikis are Web sites that can be edited by anyone who has access to them. The word 'blog' is a contraction of 'Web Log' – an online Web journal that can offer a resource rich multimedia environment. Podcasts are repositories of audio and video materials that can be "pushed" to subscribers, even without user intervention. These audio and video files can be downloaded to portable media players that can be taken anywhere, providing the potential for "anytime, anywhere" learning experiences (mobile learning). DISCUSSION: Wikis, blogs and podcasts are all relatively easy to use, which partly accounts for their proliferation. The fact that there are many free and Open Source versions of these tools may also be responsible for their explosive growth. Thus it would be relatively easy to implement any or all within a Health Professions' Educational Environment. Paradoxically, some of their disadvantages also relate to their openness and ease of use. With virtually anybody able to alter, edit or otherwise contribute to the collaborative Web pages, it can be problematic to gauge the reliability and accuracy of such resources. While arguably, the very process of collaboration leads to a Darwinian type 'survival of the fittest' content within a Web page, the veracity of these resources can be assured through careful monitoring, moderation, and operation of the collaborationware in a closed and secure digital environment. Empirical research is still needed to build our pedagogic evidence base about the different aspects of these tools in the context of medical/health education. SUMMARY AND CONCLUSION: If effectively deployed, wikis, blogs and podcasts could offer a way to enhance students', clinicians' and patients' learning experiences, and deepen levels of learners' engagement and collaboration within digital learning environments. Therefore, research should be conducted to determine the best ways to integrate these tools into existing e-Learning programmes for students, health professionals and patients, taking into account the different, but also overlapping, needs of these three audience classes and the opportunities of virtual collaboration between them. Of particular importance is research into novel integrative applications, to serve as the "glue" to bind the different forms of Web-based collaborationware synergistically in order to provide a coherent wholesome learning experience

Automatic Extraction of Concepts to Extend RadLex

RadLex™, the Radiology Lexicon, is a controlled vocabulary of terms used in radiology. It was developed by the Radiological Society of North America in recognition of a lack of coverage of these radiology concepts by other lexicons. There are still additional concepts, particularly those related to imaging observations and imaging observation characteristics, that could be added to the lexicon. We used a free and open source software system to extract these terms from the medical literature. The system retrieved relevant articles from the PubMed repository and passed them through modules in the Apache Unstructured Information Management Architecture. Image observations and image observation characteristics were identified through a seven-step process. The system was run on a corpus of 1,128 journal articles. The system generated lists of 624 imaging observations and 444 imaging observation characteristics. Three domain experts evaluated the top 100 terms in each list and determined a precision of 52% and 26%, respectively, for identification of image observations and image observation characteristics. We conclude that candidate terms for inclusion in standardized lexicons may be extracted automatically from the peer-reviewed literature. These terms can then be reviewed for curation into the lexicon

An Integrated Approach to a Teaching File Linked to PACS

To meet the educational needs of a medical imaging department with a strong teaching commitment, a teaching file that uses digital data supplied by the institutional picture archiving and communications system (PACS) was required. This teaching file had to be easily used by the end users, have a simple submission process, be able to support multiple users, be searchable on all data fields, and implementing the teaching file must not incur any additional software or hardware costs. The teaching file developed to address this problem takes advantage of the database structure and capabilities of several components included in the commercial PACS installed at the hospital. MS Access is used to seamlessly integrate with the digital imaging and communication in medicine (DICOM) database of a normal work station that is part of the PACS. This integration allows relevant patient and study demographics to be copied from images of interest and then to be stored in a separate database as the back-end of the digital teaching file. When images for a particular teaching file case need to be reviewed, they are automatically retrieved and displayed from the main PACS database using an open application programming interface (API) connection defined on the PACS web server. Utilizing this open API connection means the teaching file contains only the relevant demographic information of each teaching file case; no image data is stored locally. The open API connection allows access to imaging data usually not encountered in a teaching file, allowing more comprehensive imaging case files to be developed by the radiologist. Other advantages of this teaching file design are that it does not duplicate image data, it is small allowing simple ongoing backup, and it can be opened with multiple users accessing the database without compromising data access or integrity

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine