Context: Digital pathology has the potential to dramatically alter the way pathologists work, yet little is known about pathologists' viewing behavior while interpreting digital whole slide images. While tracking pathologist eye movements when viewing digital slides may be the most direct method of capturing pathologists' viewing strategies, this technique is cumbersome and technically challenging to use in remote settings. Tracking pathologist mouse cursor movements may serve as a practical method of studying digital slide interpretation, and mouse cursor data may illuminate pathologists' viewing strategies and time expenditures in their interpretive workflow. Aims: To evaluate the utility of mouse cursor movement data, in addition to eye-tracking data, in studying pathologists' attention and viewing behavior. Settings and Design: Pathologists (N = 7) viewed 10 digital whole slide images of breast tissue that were selected using a random stratified sampling technique to include a range of breast pathology diagnoses (benign/atypia, carcinoma in situ, and invasive breast cancer). A panel of three expert breast pathologists established a consensus diagnosis for each case using a modified Delphi approach. Materials and Methods: Participants' foveal vision was tracked using SensoMotoric Instruments RED 60 Hz eye-tracking system. Mouse cursor movement was tracked using a custom MATLAB script. Statistical Analysis Used: Data on eye-gaze and mouse cursor position were gathered at fixed intervals and analyzed using distance comparisons and regression analyses by slide diagnosis and pathologist expertise. Pathologists' accuracy (defined as percent agreement with the expert consensus diagnoses) and efficiency (accuracy and speed) were also analyzed. Results: Mean viewing time per slide was 75.2 seconds (SD = 38.42). Accuracy (percent agreement with expert consensus) by diagnosis type was: 83% (benign/atypia); 48% (carcinoma in situ); and 93% (invasive). Spatial coupling was close between eye-gaze and mouse cursor positions (highest frequency ∆x was 4.00px (SD = 16.10), and ∆y was 37.50px (SD = 28.08)). Mouse cursor position moderately predicted eye gaze patterns (Rx = 0.33 and Ry = 0.21). Conclusions: Data detailing mouse cursor movements may be a useful addition to future studies of pathologists' accuracy and efficiency when using digital pathology.

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.

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.

Background: Graphical reports that contain charts, images, and tables have potential to convey information more effectively than text-based reports; however, studies have not measured how much clinicians value such features. We sought to identify factors that might influence the utilization of reports with graphical elements postulating that this is a surrogate for relative clinical utility of these graphical elements. Materials and Methods: We implemented a pilot project at ARUP laboratories to develop online enhanced laboratory test reports that contained graphical elements. We monitored on-demand clinician access to reports generated for 48 reportable tests over 22 months. We evaluated utilization of reports with graphical elements by clinicians at all institutions that use ARUP as a reference laboratory using descriptive statistics, regression, and meta-analysis tools to evaluate groups of similar test reports. Results: Median download rate by test was 8.6% with high heterogeneity in download rates between tests. Test reports with additional graphical elements were not necessarily downloaded more often than reports without these elements. Recently implemented tests and tests reporting abnormal results were associated with higher download rates (P < 0.01). Higher volume tests were associated with lower download rates (P = 0.03). Conclusions: In select cases graphical information may be clinically useful, particularly for less frequently ordered tests and in on reports of abnormal results. The utilization data presented could be used as a reference point for other laboratories planning on implementing graphical reporting. However, between-test heterogeneity was high and in many cases graphical elements may add little clinical utility, particularly if these merely reinforce information already contained in text based reports.

Background: Image bioinformatics infrastructure typically relies on a combination of server-side high-performance computing and client desktop applications tailored for graphic rendering. On the server side, matrix manipulation environments are often used as the back-end where deployment of specialized analytical workflows takes place. However, neither the server-side nor the client-side desktop solution, by themselves or combined, is conducive to the emergence of open, collaborative, computational ecosystems for image analysis that are both self-sustained and user driven. Materials and Methods: ImageJS was developed as a browser-based webApp, untethered from a server-side backend, by making use of recent advances in the modern web browser such as a very efficient compiler, high-end graphical rendering capabilities, and I/O tailored for code migration. Results : Multiple versioned code hosting services were used to develop distinct ImageJS modules to illustrate its amenability to collaborative deployment without compromise of reproducibility or provenance. The illustrative examples include modules for image segmentation, feature extraction, and filtering. The deployment of image analysis by code migration is in sharp contrast with the more conventional, heavier, and less safe reliance on data transfer. Accordingly, code and data are loaded into the browser by exactly the same script tag loading mechanism, which offers a number of interesting applications that would be hard to attain with more conventional platforms, such as NIH's popular ImageJ application. Conclusions : The modern web browser was found to be advantageous for image bioinformatics in both the research and clinical environments. This conclusion reflects advantages in deployment scalability and analysis reproducibility, as well as the critical ability to deliver advanced computational statistical procedures machines where access to sensitive data is controlled, that is, without local "download and installation."

Background: The use of adjuvant tamoxifen therapy in the treatment of estrogen receptor (ER) expressing breast carcinomas represents a major advance in personalized cancer treatment. Because there is no benefit (and indeed there is increased morbidity and mortality) associated with the use of tamoxifen therapy in ER-negative breast cancer, its use is restricted to women with ER expressing cancers. However, correctly classifying cancers as ER positive or negative has been challenging given the high reported false negative test rates for ER expression in surgical specimens. In this paper I model practice recommendations using published information from clinical trials to address the question of whether there is a false negative test rate above which it is more efficacious to forgo ER testing and instead treat all patients with tamoxifen regardless of ER test results. Methods: I used data from randomized clinical trials to model two different hypothetical treatment strategies: (1) the current strategy of treating only ER positive women with tamoxifen and (2) an alternative strategy where all women are treated with tamoxifen regardless of ER test results. The variables used in the model are literature-derived survival rates of the different combinations of ER positivity and treatment with tamoxifen, varying true ER positivity rates and varying false negative ER testing rates. The outcome variable was hypothetical 10-year survival. Results: The model predicted that there will be a range of true ER rates and false negative test rates above which it would be more efficacious to treat all women with breast cancer with tamoxifen and forgo ER testing. This situation occurred with high true positive ER rates and false negative ER test rates in the range of 20-30%. Conclusions: It is hoped that this model will provide an example of the potential importance of diagnostic error on clinical outcomes and furthermore will give an example of how the effect of that error could be modeled using real-world data from clinical trials.

Introduction: We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance. Materials and Methods: A total of 100 regions of interest (ROIs) from a breast biopsy whole-slide images at five levels of JPEG 2000 compression (8:1, 16:1, 32:1, 64:1, and 128:1) plus the uncompressed version were shown to six pathologists to determine benign versus malignant status. Results: There was a significant decrease in performance as a function of compression ratio (F = 14.58, P < 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. Just-noticeable difference (JND) metrics were computed for each image, including the mean, median, ≥90th percentiles, and maximum values. For comparison, PSNR (peak signal-to-noise ratio) and Structural Similarity (SSIM) were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. Observer performance as measured by the Receiver Operating Characteristic area under the curve (ROC Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression levels. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5. Conclusion: Whole-slide images may be compressible to relatively high levels before impacting WSI interpretation performance. The VDM metrics correlated well with artifact conspicuity and human performance.

Background: It is expected that health information technology (HIT) will deliver a safer, more efficient and effective health care system. The aim of this study was to undertake a qualitative and video-ethnographic examination of the impact of information technologies on work processes in the reception area of a Microbiology Department, to ascertain what changed, how it changed and the impact of the change. Materials and Methods: The setting for this study was the microbiology laboratory of a large tertiary hospital in Sydney. The study consisted of qualitative (interview and focus group) data and observation sessions for the period August 2005 to October 2006 along with video footage shot in three sessions covering the original system and the two stages of the Cerner implementation. Data analysis was assisted by NVivo software and process maps were produced from the video footage. Results: There were two laboratory information systems observed in the video footage with computerized provider order entry introduced four months later. Process maps highlighted the large number of pre data entry steps with the original system whilst the newer system incorporated many of these steps in to the data entry stage. However, any time saved with the new system was offset by the requirement to complete some data entry of patient information not previously required. Other changes noted included the change of responsibilities for the reception staff and the physical changes required to accommodate the increased activity around the data entry area. Conclusions: Implementing a new HIT is always an exciting time for any environment but ensuring that the implementation goes smoothly and with minimal trouble requires the administrator and their team to plan well in advance for staff training, physical layout and possible staff resource reallocation.

Introduction: An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI). Methods: A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image. Results: System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9% overall accuracy in the five subtype classification. Conclusion: Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20× and 40× data, and expanding the applications for bone marrow aspirates.

Background: Immunohistochemistry (IHC) is an important tool to identify and quantify expression of certain proteins (antigens) to gain insights into the molecular processes in a diseased tissue. However, it is a challenge for pathologists to remember the discriminative characteristics of the growing number of such antigens across multiple diseases. The complexity of their expression patterns, fueled by continuous discoveries in molecular pathology, gives rise to a combinatorial explosion that places an unprecedented burden on a practicing pathologist and therefore increases cost and variability of IHC studies. Materials and Methods: To tackle these issues, we have developed antibody test optimized selection method, a novel informatics tool to help pathologists in improving the IHC antibody selection process. The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree. Results: A comparative analysis of our method with the expert and World Health Organization classification guidelines showed that the proposed method brings threefold reduction in number of antibody tests required to reach a diagnostic conclusion. Conclusion: The developed method can significantly streamline the antibody test selection process, decrease associated costs and reduce inter- and intrapathologist variability in IHC decision-making.