Background: In the recent years, the advances in digital methods in pathology have resulted in the use of telecytology in the immediate assessment of fine needle aspiration (FNA) specimens. However, there is a need for organ-based and body site-specific studies on the use of telecytology for the immediate assessment of FNA to evaluate its pitfalls and limitations. We present our experience with the use of telecytology for on-site evaluation of ultrasound-guided FNA (USG-FNA) of axillary lymph nodes in a remote breast care center. Materials and Methods: Real-time images of Diff-Quik-stained cytology smears were obtained with an Olympus digital camera attached to an Olympus CX41 microscope and transmitted via ethernet by a cytotechnologist to a pathologist who rendered preliminary diagnosis while communicating with the on-site cytotechnologist over the Vocera system. The accuracy of the preliminary diagnosis was compared with the final diagnosis, retrospectively. Results: A total of 39 female patients (mean age: 50.5 years) seen at the breast care center underwent USG-FNA of 44 axillary nodes. Preliminary diagnoses of benign, suspicious/malignant, and unsatisfactory were 41, 52, and 7%, respectively. Only one of the 23 cases that were initially interpreted as benign was reclassified as suspicious on final cytologic diagnosis. Seventeen of 18 suspicious/malignant cases on initial cytology corresponded with a malignant diagnosis on final cytology. One suspicious case was reclassified as benign on final cytologic diagnosis. All unsatisfactory cases remained inadequate for final cytologic interpretation. The presence of additional material in the cell block and interpretative error were the main reasons for discrepancy, accounting for the two discrepant cases. Conclusions: This retrospective study demonstrates that the on-site telecytology evaluation of USG-FNA of axillary lymph nodes in patients at a remote breast care center was highly accurate compared with the final cytologic evaluation. It allows pathologists to use their time more efficiently and makes on-site evaluation at a remote site possible.

Background: For decades anatomic pathology (AP) workflow have been a highly manual process based on the use of an optical microscope and glass slides. Recent innovations in scanning and digitizing of entire glass slides are accelerating a move toward widespread adoption and implementation of a workflow based on digital slides and their supporting information management software. To support the design of digital pathology systems and ensure their adoption into pathology practice, the needs of the main users within the AP workflow, the pathologists, should be identified. Contextual inquiry is a qualitative, user-centered, social method designed to identify and understand users' needs and is utilized for collecting, interpreting, and aggregating in-detail aspects of work. Objective: Contextual inquiry was utilized to document current AP workflow, identify processes that may benefit from the introduction of digital pathology systems, and establish design requirements for digital pathology systems that will meet pathologists' needs. Materials and Methods: Pathologists were observed and interviewed at a large academic medical center according to contextual inquiry guidelines established by Holtzblatt et al. 1998. Notes representing user-provided data were documented during observation sessions. An affinity diagram, a hierarchal organization of the notes based on common themes in the data, was created. Five graphical models were developed to help visualize the data including sequence, flow, artifact, physical, and cultural models. Results: A total of six pathologists were observed by a team of two researchers. A total of 254 affinity notes were documented and organized using a system based on topical hierarchy, including 75 third-level, 24 second-level, and five main-level categories, including technology, communication, synthesis/preparation, organization, and workflow. Current AP workflow was labor intensive and lacked scalability. A large number of processes that may possibly improve following the introduction of digital pathology systems were identified. These work processes included case management, case examination and review, and final case reporting. Furthermore, a digital slide system should integrate with the anatomic pathologic laboratory information system. Conclusions: To our knowledge, this is the first study that utilized the contextual inquiry method to document AP workflow. Findings were used to establish key requirements for the design of digital pathology systems.

Background: One of the drawbacks of studying pathology in the second year of medical school in a classroom setting is the relatively limited exposure to patient encounters/clinical rotations, making it difficult to understand and fully appreciate the significance of the course material, specifically the molecular and tissue aspects of disease. In this study, we determined if case vignettes incorporating pathologist-clinician encounters with whole slide imaging (WSI) and narrated/annotated videos of whole slide (WS) scans in addition to clinical data improved student understanding of pathologic disease processes. Materials and Methods: Case vignettes were created for several genitourinary disease processes that utilized clinical data including narratives of pathologist-clinician encounters, WSI, and annotated video tutorials of WS scans (designed to simulate "double-heading"). The students were encouraged to view the virtual slide first, with the video tutorials being provided to offer additional assistance. The case vignettes were created to be interactive with a detailed explanation of each correct and incorrect question choice. The cases were made available to all second year medical students via a website and could be viewed only after completing a 10 question pre-test. A post-test could be completed after viewing all cases followed by a brief satisfaction survey. Results: Ninety-six students completed the pre-test with an average score of 7.7/10. Fifty-seven students completed the post-test with an average score of 9.4/10. Thirty-six students completed the satisfaction survey. 94% agreed or strongly agreed that this was a useful exercise and 91% felt that it helped them better understand the topics. Conclusion: The development of interactive case vignettes incorporating simulated pathologist-clinician encounters with WSI and video tutorials of WS scans helps to improve student enthusiasm to learn and grasp pathologic aspects of disease processes that lead to clinical therapeutic decision making.

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.