Background: DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence. However, the reliability of unsophisticated match statistics for DNA mixtures has been questioned. Materials and Methods: The most prevalent match statistic for DNA mixtures is the combined probability of inclusion (CPI), used by crime labs for over 15 years. When testing 13 short tandem repeat (STR) genetic loci, the CPI ^-1 value is typically around a million, regardless of DNA mixture composition. However, actual identification information, as measured by a likelihood ratio (LR), spans a much broader range. This study examined probability of inclusion (PI) mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data. The log(PI ^-1 ) values were examined and compared with corresponding log(LR) values. Results: The LR and CPI methods were compared in case examples of false inclusion, false exclusion, a homicide, and criminal justice outcomes. Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center, with little correlation to actual identification information. By the law of large numbers (LLN), CPI ^-1 increases with the number of tested genetic loci, regardless of DNA mixture composition or match information. These statistical findings explain why CPI is relatively constant, with implications for DNA policy, criminal justice, cost of crime, and crime prevention. Conclusions: Forensic crime laboratories have generated CPI statistics on hundreds of thousands of DNA mixture evidence items. However, this commonly used match statistic behaves like a random generator of inclusionary values, following the LLN rather than measuring identification information. A quantitative CPI number adds little meaningful information beyond the analyst's initial qualitative assessment that a person's DNA is included in a mixture. Statistical methods for reporting on DNA mixture evidence should be scientifically validated before they are relied upon by criminal justice.

Background: Identifying new and more robust assessments of proficiency/expertise (finding new "biomarkers of expertise") in histopathology is desirable for many reasons. Advances in digital pathology permit new and innovative tests such as flash viewing tests and eye tracking and slide navigation analyses that would not be possible with a traditional microscope. The main purpose of this study was to examine the usefulness of time-restricted testing of expertise in histopathology using digital images. Methods: 19 novices (undergraduate medical students), 18 intermediates (trainees), and 19 experts (consultants) were invited to give their opinion on 20 general histopathology cases after 1 s and 10 s viewing times. Differences in performance between groups were measured and the internal reliability of the test was calculated. Results: There were highly significant differences in performance between the groups using the Fisher's least significant difference method for multiple comparisons. Differences between groups were consistently greater in the 10-s than the 1-s test. The Kuder-Richardson 20 internal reliability coefficients were very high for both tests: 0.905 for the 1-s test and 0.926 for the 10-s test. Consultants had levels of diagnostic accuracy of 72% at 1 s and 83% at 10 s. Conclusions: Time-restricted tests using digital images have the potential to be extremely reliable tests of diagnostic proficiency in histopathology. A 10-s viewing test may be more reliable than a 1-s test. Over-reliance on "at a glance" diagnoses in histopathology is a potential source of medical error due to over-confidence bias and premature closure.

Background: Increasingly, as in our institution, operating rooms are located in hospitals and the pathology suite is located at a distant location because of off-site consolidation of pathology services. Telepathology is a technology which bridges the gap between pathologists and offers a means to obtain a consultation remotely. We aimed to evaluate the utility of telepathology as a means to assist the pathologist at the time of intraoperative consultation of lung nodules when a subspecialty pathologist is not available to directly review the slide. Methods: Cases of lung nodules suspicious for a neoplasm were included. Frozen sections were prepared in the usual manner. The pathologists on the intraoperative consultation service at two of our system hospitals notified the thoracic pathologist of each case after rendering a preliminary diagnosis. The consultation was performed utilizing a Nikon™ Digital Sight camera and web-based Remote Medical Technologies™ software with live video streaming directed by the host pathologist. The thoracic pathologist rendered a diagnosis without knowledge of the preliminary interpretation then discussed the interpretation with the frozen section pathologist. The interpretations were compared with the final diagnosis rendered after sign-out. Results: One hundred and three consecutive cases were included. The frozen section pathologist and a thoracic pathologist had concordant diagnoses in 93 cases (90.2%), discordant diagnoses in nine cases (8.7%), and one case in which both deferred. There was an agreement between the thoracic pathologist's diagnosis and the final diagnosis in 98% of total cases including 8/9 (88.9%) of the total discordant cases. In two cases, if the thoracic pathologist had not been consulted, the patient would have been undertreated. Conclusions: We have shown that telepathology is an excellent consultation tool in the frozen section diagnosis of lung nodules.

Introduction: Cervical cancer is one of the most common causes of death in women worldwide. ^[1] The introduction of cervical cytology in screening programs is an effective way for early detection and treatment of cervical precancerous lesions. Conventional screening of cervical cytology slides is still considered the current "gold standard" for the assessment of proficiency in becoming a cytotechnician, but diagnosis using digital whole slide images (WSI) may offer many advantages. Materials and Methods: In this study, we have used a selection of WSI from thin-layer specimens of the most common cervical infections and (pre) neoplastic lesions, and hypothesized that weekly WSI based case-meetings would help to obtain optimal acceptance of the new digital workflow in daily pathology practice. A questionnaire, before and after the test period was used to study the effect of our approach. Results: The participants clearly had to go through a learning curve to get accustomed to viewing WSI. In the beginning, there was a little self-confidence in recognizing classical cervical cytomorphological features in the WSI, and there were complaints about the speed of viewing and insufficient Z-resolution for cell groups. Adjusting the Z-stack settings resulted in better three-dimensional information due to better focusing options. Weekly meetings appeared to be instrumental in the implementation process, as participants had to select and present WSI from thematic cases themselves, and thereby, got used to viewing WSI. Some WSI were replaced by better ones until a final set of 45 representatives WSI remained. Eventually, the consensus was reached among all participants that cytomorphological features in WSI from thin-layers cervical specimens could comparably be appreciated in WSI as by conventional microscopy. The selection of 45 WSI was now used to create a digital WSI based reference atlas to support further studies. Conclusion: We have obtained consensus between professionals that WSI from cervical cytology can be used to identify cytomorphological features, necessary for diagnosis. In addition, we observed that active participation of professionals had a positive effect on the overall acceptance of WSI and was important in the change management.

Background: Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. Methods : We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. Results: We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Conclusions: Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

Background: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. Methods: In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. Results: Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. Conclusions: These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.

Background: Mobile networks and smartphones are growing in developing countries. Expert telemedicine consultation will become more convenient and feasible. We wanted to report on our experience in using a smartphone and a 3-D printed adapter for capturing microscopic images. Methods: Images and videos from a gastrointestinal biopsy teaching set of referred cases from the AFIP were captured with an iPhone 5 smartphone fitted with a 3-D printed adapter. Nine pathologists worldwide evaluated the images for quality, adequacy for telepathology consultation, and confidence rendering a diagnosis based on the images viewed on the web. Results: Average Likert scales (ordinal data) for image quality (1=poor, 5=diagnostic) and adequacy for diagnosis (1=No, 5=Yes) had modes of 3 and 4, respectively. Adding a video overview of the specimen improved diagnostic confidence. The mode of confidence in diagnosis based on the images reviewed was four. In 31 instances, reviewers' diagnoses completely agreed with AFIP diagnosis, with partial agreement in 9 and major disagreement in 5. There was strong correlation between image quality and confidence (r = 0.78), image quality and adequacy of image (r = 0.73) and whether images were found adequate when reviewers were confident (r = 0.72). Intraclass Correlation for measuring reliability among the four reviewers who finished a majority of cases was high (quality=0.83, adequacy= 0.76 and confidence=0.92). Conclusions: Smartphones allow pathologists and other image dependent disciplines in low resource areas to transmit consultations to experts anywhere in the world. Improvements in camera resolution and training may mitigate some limitations found in this study.

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.

Background: Computerized physician order entry (CPOE) systems are quickly becoming ubiquitous, and groups of orders ("order sets") to allow for easy order input are a common feature. This provides a streamlined mechanism to view, modify, and place groups of related orders. This often serves as an electronic equivalent of a specialty requisition. A characteristic, of these order sets is that specific orders can be predetermined to be "preselected" or "defaulted-on" whenever the order set is used while others are "optional" or "defaulted-off" (though there is typically the option is to "deselect" defaulted-on tests in a given situation). While it seems intuitive that the defaults in an order set are often accepted, additional study is required to understand the impact of these "default" settings in an order set on ordering habits. This study set out to quantify the effect of changing the default settings of an order set. Methods: For quality improvement purposes, order sets dealing with transfusions were recently reviewed and modified to improve monitoring of outcome. Initially, the order for posttransfusion hematocrits and platelet count had the default setting changed from "optional" to "preselected." The default settings for platelet count was later changed back to "optional," allowing for a natural experiment to study the effect of the default selections of an order set on clinician ordering habits. Results: Posttransfusion hematocrit values were ordered for 8.3% of red cell transfusions when the default order set selection was "off" and for 57.4% of transfusions when the default selection was "preselected" (P < 0.0001). Posttransfusion platelet counts were ordered for 7.0% of platelet transfusions when the initial default order set selection was "optional," increased to 59.4% when the default was changed to "preselected" (P < 0.0001), and then decreased to 7.5% when the default selection was returned to "optional." The posttransfusion platelet count rates during the two "optional" periods: 7.0% versus 7.5% - were not statistically different (P = 0.620). Discussion: Default settings in CPOE order sets can significantly influence physician selection of laboratory tests. Careful consideration by all stakeholders, including clinicians and pathologists, should be obtained when establishing default settings in order sets.

Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. Conclusion: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

Background: Little evidence is available concerning the impact of virtual microscopy (VM) in the undergraduate teaching of pathology. We aimed: (1) to determine the impact in student scores when moving from conventional microscopy (CM) to VM; (2) to assess the students' impressions and changes in study habits regarding the impact of this tool. Methods: We evaluated two groups taking the discipline of pathology in the same course, one using CM and the other VM. The same set of slides used in the CM classes was digitized in a VENTANA iScan HT (Roche Diagnostics, Sant Cugat, Spain) at ×20 and observed by the students using the Virtuoso viewer (Roche Diagnostics). We evaluated the skill level reached by the students with an online test. A voluntary survey was undertaken by the VM group to assess the students' impressions regarding the resource. The day and time of any accession to the viewer were registered. Results: There were no differences between the two groups in their marks in the online test (mean marks for the CM and the VM groups: 9.87 ± 0.34 and 9.86 ± 0.53, respectively; P = 0.880). 86.6% of the students found the software friendly, easy-to-use and effective. 71.6% of the students considered navigation easier with VM than with CM. The most appreciated feature of VM was the possibility to access the images anywhere and at any time (93.3%). 57.5% of the accesses were made on holidays and 41.9% later than 6:00 pm. Conclusions: Virtual microscopy can effectively replace the traditional methods of learning pathology, providing mobility and convenience to medical students.