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OBJECTIVES: Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement. METHODS: We constructed a web-based "smart template" (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees. RESULTS: Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence-assisted scoring of biopsies. CONCLUSIONS: Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms.

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Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.

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Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence.

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The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field of Pathology Informatics. With a focus on data analytics, data science, and data management in 2022, the bootcamp addressed the growing importance of data analysis in pathology and laboratory medicine practice. The expansion of data-related subjects in Pathology Informatics Essentials for Residents (PIER) and the Clinical Informatics fellowship examinations highlights the increasing significance of these skills in pathology practice in particular and medicine in general. The curriculum included lectures on databases, programming, analytics, machine learning basics, and specialized topics like anatomic pathology data analysis and dashboarding.

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Összefoglaló. Bevezetés: Az I. Patológiai és Kísérleti Rákkutató Intézet - a Semmelweis Egyetemen belüli diagnosztikai szolgáltatásnyújtás mellett - kiterjedt külso partneri hálózattal (vizsgálatmegrendelovel) bír. Az Intézet a napi muködése során párhuzamosan használja az egyetem központi informatikai rendszerét, valamint belso, folyamattámogató alkalmazását (workflow management). A külsos partnerek hozzáférése vizsgálatfeladásra az egyetemi központi informatikai rendszerhez nincs biztosítva. A vizsgálatok rendelése papíralapú, a minta érkeztetésekor a klinikai adatok rögzítése manuális, kifejezetten humáneroforrás-igényes. Célkituzés: Célunk volt a patológiai minták regisztrációjának egyszerusítése és felgyorsítása, az adminisztratív folyamatok hatékonyságának javítása. Módszer: A kituzött célt a minoségfejlesztésbol ismert Plan-Do-Check-Act (Tervezés-Cselekvés-Ellenorzés-Beavatkozás) ciklus módszereit alkalmazva kívántuk elérni, online, a mintavétel helyén elérheto, a meglévo belso folyamattámogató alkalmazáshoz kapcsolódó, szakterület-specifikus vizsgálatkéro felület kifejlesztésével. Eredmények: A vizsgálati minták regisztrációjának átlagos ideje 65%-kal csökkent az online vizsgálatkéro rendszerhez csatlakozott klinikai partnerek körében. Megbeszélés: Az elmúlt években tapasztalható volt, hogy kisebb, nem hatékonyan muködtetheto patológiai osztályok megszuntek, részben vagy egészben beolvadtak nagyobb diagnosztikai egységekbe. A humáneroforrás-problémák (elöregedo szakma a patológia) a fenti folyamatot minden bizonnyal tovább erosítik. Várható, hogy a nagyobb patológiai osztályokon a következo években a mintaszám tovább növekszik, a vizsgálatkérések egyre nagyobb hányada érkezik majd intézményen kívülrol. Következtetés: A patológiai informatika fejlesztésekor figyelembe kell venni, hogy szükséges már a mintavétel helyén biztosítani az informatikai támogatást a minta nyomon követéséhez, nem elégséges csak a laboron belüli folyamatok kiszolgálása. Orv Hetil. 2021; 162(49): 1962-1967. INTRODUCTION: The 1st Department of Pathology and Experimental Cancer Research, Semmelweis University (Budapest, Hungary) has a broad network of clinical partners, many of which are non-university hospitals. A separate hospital information system and a local laboratory workflow management system is used at the Department. University clinics use the hospital information system for electronic requesting of tests. Non-university partners have no access to the systems, requesting tests is paper-based, registration of the requests at the pathology lab is manual and laborious. OBJECTIVE: Our main objective was to improve the efficiency of the sample registration step of the pathology workflow. METHOD: Applying the Plan-Do-Check-Act procedure, a quality improvement project has been carried out and an online, subspecialty-based requesting application tool, interfaced with the current laboratory information system, was developed. RESULTS: The average sample registration time improved with 65% among the early user partners. DISCUSSION: The past years have shown smaller, inefficient pathology labs decreasing in number and integrated into larger regional diagnostic centers. Both issues of efficiency and quality assurance and problems rooted in human resources are drivers of further centralisation. The numbers of test requests and samples from non in-house partners are expected to be increased in the pathology labs in the future. CONCLUSION: Efficient and safe sample tracking has to start at the site of sample acquisition. State of the art laboratory information systems should support this expansion of competence. Orv Hetil. 2021; 162(49): 1962-1967.

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OBJECTIVES: Bone marrow evaluation plays a critical role in the diagnosis, staging, and monitoring of many diseases. Although there are standardized guidelines for assessing bone marrow specimen quality, there is a lack of evidence-based tools to perform such assessments. The objective was to monitor bone marrow sample quality in real time by standardizing the basic components of a synoptic report and incorporating it into a bone marrow report template. MATERIALS AND METHODS: A relational database of bone marrow quality parameters was developed and incorporated into our laboratory information system bone marrow report template, with data entry completed during specimen sign out. Data from multiple reports created within a date range were extracted by Structured Query Language query, and summarized in tabular form. Reports generated from these data were utilized in quality improvement efforts. RESULTS: The synoptic reporting system was routinely used to record the quality of bone marrow specimens from adult patients. Data from 3189 bone marrow aspirates, 3302 biopsies, and 3183 biopsy touch imprints identified hemodilution as the principal issue affecting bone marrow aspirate quality, whereas aspiration artifact and fragmentation affected bone marrow biopsy quality. CONCLUSIONS: The bone marrow synoptic reporting process was easy to use, readily adaptable, and has proved a useful component of the overall quality assurance process to optimize bone marrow quality.

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In March 2020, NorthShore University Health System laboratories mobilized to develop and validate polymerase chain reaction based testing for detection of SARS-CoV-2. Using laboratory data, NorthShore University Health System created the Data Coronavirus Analytics Research Team to track activities affected by SARS-CoV-2 across the organization. Operational leaders used data insights and predictions from Data Coronavirus Analytics Research Team to redeploy critical care resources across the hospital system, and real-time data were used daily to make adjustments to staffing and supply decisions. Geographical data were used to triage patients to other hospitals in our system when COVID-19 detected pavilions were at capacity. Additionally, one of the consequences of COVID-19 was the inability for patients to receive elective care leading to extended periods of pain and uncertainty about a disease or treatment. After shutting down elective surgeries beginning in March of 2020, NorthShore University Health System set a recovery goal to achieve 80% of our historical volumes by October 1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and clinical teams were able to achieve 89% of our historical volumes a month ahead of schedule, allowing rapid recovery of surgical volume and financial stability. The Data Coronavirus Analytics Research Team also was used to demonstrate that the accelerated recovery period had no negative impact with regard to iatrogenic COVID-19 infection and did not result in increased deep vein thrombosis, pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate how a coordinated and transparent data-driven effort that was built upon a robust laboratory testing capability was essential to the operational response and recovery from the COVID-19 crisis.

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Clinical practice is most efficient when physicians have the right information, including pathology and laboratory results, at the point of contact with the patient. In downstream workflows, subsequent groups using lab data want to have it available in a format that is easy to manipulate. With the complexity of electronic medical records, hospital information systems, and the need to accommodate data from outside systems, this is not easy to accomplish. By utilizing a group of concepts from clinical and pathology informatics, system implementations may be improved to achieve relevant laboratory data in a format that is usable by healthcare entities to improve patient care and forward endeavors in precision medicine.

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With over 20 years of the opioid crisis, our collective response has evolved to address the ongoing needs related to the management of opioid use and opioid use disorder. There has been an increasing recognition of the need for standardized metrics to evaluate organizational management and stewardship. The clinical laboratory, with a wealth of objective and quantitative health information, is uniquely poised to support opioid stewardship and drive valuable metrics for opioid prescribing practices and opioid use disorder (OUD) management. To identify laboratory-related insights that support these patient populations, a collection of 5 independent institutions, under the umbrella of the Clinical Laboratory 2.0 movement, developed and prioritized metrics. Using a structured expert panel review, laboratory experts from 5 institutions assessed possible metrics as to their relative importance, usability, feasibility, and scientific acceptability based on the National Quality Forum criteria. A total of 37 metrics spanning the topics of pain and substance use disorder (SUD) management were developed with consideration of how laboratory insights can impact clinical care. Monitoring these metrics, in the form of summative reports, dashboards, or embedded in laboratory reports themselves may support the clinical care teams and health systems in addressing the opioid crisis. The clinical insights and standardized metrics derived from the clinical laboratory during the opioid crisis exemplifies the value proposition of clinical laboratories shifting into a more active role in the healthcare system. This increased participation by the clinical laboratories may improve patient safety and reduce healthcare costs related to OUD and pain management.

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In situ hybridization (ISH) and immunohistochemistry (IHC) are valuable tools for molecular pathology and cancer research. Recent advances in multiplex technology, assay automation, and digital image analysis have enabled the development of co-ISH IHC or immunofluorescence (IF) methods, which allow researchers to simultaneously view and quantify expression of mRNA and protein within the preserved tissue spatial context. These data are vital to the study of the control of gene expression in the complex tumor microenvironment.

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The data presented in this article is the provincial vitamin B12 test volume data for Alberta, Canada per month between April 1, 2015 and April 30, 2018. This data set was collected from the three different Alberta Public Laboratories Laboratory Information Systems: Cerner Millennium for Calgary, Sunquest for Edmonton, and MediTech for the remaining rural zones of Alberta (Bonnyville, Grand Prairie, Camrose, Red Deer, and Medicine Hat). An educational province-wide intervention aimed at reducing redundant testing was implemented on April 11, 2017 in Calgary, Alberta and Edmonton, Alberta and on May 2, 2017 in rural Alberta sites. All vitamin B12 test results in Alberta were appended with the educational comment "A normal test result indicates adequate stores and should not be repeated. However, if specific clinical situations require re-testing, the interval should not be sooner than 1 year." Provincial monthly test volumes prior to this intervention ranged from 54,182 to 73,522 tests per month and after this intervention ranged from 59,116 to 74,006 tests per month. The total number of vitamin B12 tests ordered over the 37 months in Alberta was 2,444,724; 690,448 tests were ordered in Calgary, 1,029,315 tests were ordered in Edmonton, and 724,961 tests were ordered in rural sites. This data article was submitted as a companion paper to the related research article, "Implementation of an educational province-wide intervention to reduce redundant vitamin B12 testing: a cross-sectional study"[1].

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Data presented in this article include the top 51 ordered laboratory tests in Calgary and surrounding area, Alberta, from January to December 2017. This data set was collected from Calgary Laboratory Service's Laboratory Information System, and included top 51 tests ordered from community (n = 11, 224, 330), inpatient (n = 2,340,594) and emergency (n = 1,670,062) settings. Test order mnemonic that were not true laboratory tests (eg: "extra PST tube", "extra tube", etc.) were excluded in the analysis. The top test ordered in all 3 test encounters was the complete blood count test (community encounter, n = 921, 873; inpatient setting, n = 357, 375; and emergency setting, n = 276, 954). This data article was submitted as a companion paper to the related research article, "Estimated costs of 51 commonly ordered laboratory tests in Canada" [1].

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Pathology has a large role to play in the proper development, implementation, and optimization of clinical decision support (CDS). CDS training must be supported by an educational foundation in clinical and pathology informatics. Educational opportunities are currently limited, but expanding, in the pathology residency space with Pathology Informatics Essentials for Residents. The use of an educational version of electronic clinical systems is an important educational tool to support the needed outcomes-driven and exercise-based informatics and CDS training. With the multidisciplinary nature of informatics, it is advantageous to include laboratory professionals in the training exercises as appropriate.

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The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

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The international symposium entitled "Innovation in Transplantation: The Digital Era" took place on June 7 and 8, 2018 in Verona, Italy. This meeting was borne out of the productive collaboration between the Universities and Hospital Trusts of Verona and Padua in Italy, in the context of a vast regional project called Research and innovation project within the Health Technology Assessment. The project aimed to create an innovative digital platform for teleconsultation and delivering diagnostic second opinions in the field of organ transplantation within the Veneto region. This conference brought together pathologists, health informatics leaders, clinicians, researchers, vendors, and health-care planners from all around the globe. The symposium was conceived to promote the exchange of knowledge and kindle fertile discussion among the 130 attendees from 15 different countries. This article conveys the highlights of this symposium.

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Advances in medical imaging technology have created opportunities for computer-aided diagnostic tools to assist human practitioners in identifying relevant patterns in massive, multiscale digital pathology slides. This work presents Hierarchical Linear Time Subset Scanning, a novel statistical method for pattern detection. Hierarchical Linear Time Subset Scanning exploits the hierarchical structure inherent in data produced through virtual microscopy in order to accurately and quickly identify regions of interest for pathologists to review. We take a digital image at various resolution levels, identify the most anomalous regions at a coarse level, and continue to analyze the data at increasingly granular resolutions until we accurately identify its most anomalous subregions. We demonstrate the performance of our novel method in identifying cancerous locations on digital slides of prostate biopsy samples and show that our methods detect regions of cancer in minutes with high accuracy, both as measured by the ROC curve (measuring ability to distinguish between benign and cancerous slides) and by the spatial precision-recall curve (measuring ability to pick out the malignant areas on a slide which contains cancer). Existing methods need small scale images (small areas of a slide preselected by the pathologist for analysis, eg, 32 × 32 pixels) and may not work effectively on large, raw digitized images of size 100K × 100K pixels. In this work, we provide a methodology to fill this significant gap by analyzing large digitized images and identifying regions of interest that may be indicative of cancer.

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BACKGROUND: The alumni of today's Pathology Informatics and Clinical Informatics fellowships fill diverse roles in academia, large health systems, and industry. The evolving training tracks and curriculum of Pathology Informatics fellowships have been well documented. However, less attention has been given to the posttraining experiences of graduates from informatics training programs. Here, we examine the career paths of subspecialty fellowship-trained pathology informaticians. METHODS: Alumni from four Pathology Informatics fellowship training programs were contacted for their voluntary participation in the study. We analyzed various components of training, and the subsequent career paths of Pathology Informatics fellowship alumni using data extracted from alumni provided curriculum vitae. RESULTS: Twenty-three out of twenty-seven alumni contacted contributed to the study. A majority had completed undergraduate study in science, technology, engineering, and math fields and combined track training in anatomic and clinical pathology. Approximately 30% (7/23) completed residency in a program with an in-house Pathology Informatics fellowship. Most completed additional fellowships (15/23) and many also completed advanced degrees (10/23). Common primary posttraining appointments included chief medical informatics officer (3/23), director of Pathology Informatics (10/23), informatics program director (2/23), and various roles in industry (3/23). Many alumni also provide clinical care in addition to their informatics roles (14/23). Pathology Informatics alumni serve on a variety of institutional committees, participate in national informatics organizations, contribute widely to scientific literature, and more than half (13/23) have obtained subspecialty certification in Clinical Informatics to date. CONCLUSIONS: Our analysis highlights several interesting phenomena related to the training and career trajectory of Pathology Informatics fellowship alumni. We note the long training track alumni complete in preparation for their careers. We believe flexible training pathways combining informatics and clinical training may help to alleviate the burden. We highlight the importance of in-house Pathology Informatics fellowships in promoting interest in informatics among residents. We also observe the many important leadership roles in academia, large community health systems, and industry available to early career alumni and believe this reflects a strong market for formally trained informaticians. We hope this analysis will be useful as we continue to develop the informatics fellowships to meet the future needs of our trainees and discipline.

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INTRODUCTION: Next-generation-sequencing (NGS) is increasingly used in clinical and research protocols for patients with cancer. NGS assays are routinely used in clinical laboratories to detect mutations bearing on cancer diagnosis, prognosis and personalized therapy. A typical assay may interrogate 50 or more gene targets that encompass many thousands of possible gene variants. Analysis of NGS data in cancer is a labor-intensive process that can become overwhelming to the molecular pathologist or research scientist. Although commercial tools for NGS data analysis and interpretation are available, they are often costly, lack key functionality or cannot be customized by the end user. METHODS: To facilitate NGS data analysis in our clinical molecular diagnostics laboratory, we created a custom bioinformatics tool termed Houston Methodist Variant Viewer (HMVV). HMVV is a Java-based solution that integrates sequencing instrument output, bioinformatics analysis, storage resources and end user interface. RESULTS: Compared to the predicate method used in our clinical laboratory, HMVV markedly simplifies the bioinformatics workflow for the molecular technologist and facilitates the variant review by the molecular pathologist. Importantly, HMVV reduces time spent researching the biological significance of the variants detected, standardizes the online resources used to perform the variant investigation and assists generation of the annotated report for the electronic medical record. HMVV also maintains a searchable variant database, including the variant annotations generated by the pathologist, which is useful for downstream quality improvement and research projects. CONCLUSIONS: HMVV is a clinical grade, low-cost, feature-rich, highly customizable platform that we have made available for continued development by the pathology informatics community.

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The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist.

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The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange across stakeholders in health care, industry, and academia. In 2016, the 4th NDP installment took place in Linköping, Sweden, promoting development and collaboration in digital pathology for the benefit of routine care advances. This article summarizes the symposium, gathering 170 attendees from 13 countries. This summary also contains results from a survey on integrated diagnostics aspects, in particular radiology-pathology collaboration.