Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.

Introduction:Telepathology allows the digital transmission of images for rapid access to pathology experts. Recent technologic advances in smartphones have allowed them to be used to acquire and transmit digital images of the glass slide, representing cost savings and efficiency gains over traditional forms of telepathology. We report our experience with developing an iPhone application (App - Pocket Pathologist) to facilitate rapid diagnostic pathology teleconsultation utilizing a smartphone.Materials and Methods:A secure, web-based portal (http://pathconsult.upmc.com/) was created to facilitate remote transmission of digital images for teleconsultation. The App augments functionality of the web-based portal and allows the user to quickly and easily upload digital images for teleconsultation. Image quality of smartphone cameras was evaluated by capturing images using different adapters that directly attach phones to a microscope ocular lens.Results:The App was launched in August 2013. The App facilitated easy submission of cases for teleconsultation by limiting the number of data entry fields for users and enabling uploading of images from their smartphone's gallery wirelessly. Smartphone cameras properly attached to a microscope create static digital images of similar quality to a commercial digital microscope camera.Conclusion:Smartphones have great potential to support telepathology because they are portable, provide ubiquitous internet connectivity, contain excellent digital cameras, and can be easily attached to a microscope. The Pocket Pathologist App represents a significant reduction in the cost of creating digital images and submitting them for teleconsultation. The iPhone App provides an easy solution for global users to submit digital pathology images to pathology experts for consultation.

Abstract-Clinical documents are vital resources for radiologists to have a better understanding of patient history. The use of clinical documents can complement the often brief reasons for exams that are provided by physicians in order to perform more informed diagnoses. With the large number of study exams that radiologists have to perform on a daily basis, it becomes too time-consuming for radiologists to sift through each patient's clinical documents. It is therefore important to provide a capability that can present contextually relevant clinical documents, and at the same time satisfy the diverse information needs among radiologists from different specialties. In this work, we propose a knowledge-based semantic similarity approach that uses domain-specific relationships such as part-of along with taxonomic relationships such as is-a to identify relevant radiology exam records. Our approach also incorporates explicit relevance feedback to personalize radiologists information needs. We evaluated our approach on a corpus of 6,265 radiology exam reports through study sessions with radiologists and demonstrated that the retrieval performance of our approach yields an improvement of 5% over the baseline. We further performed intra-class and inter-class similarities using a subset of 2,384 reports spanning across 10 exam codes. Our result shows that intra-class similarities are always higher than the inter-class similarities and our approach was able to obtain 6% percent improvement in intra-class similarities against the baseline. Our results suggest that the use of domain-specific relationships together with relevance feedback provides a significant value to improve the accuracy of the retrieval of radiology exam reports.