Individualized medicine and demographic change as determining workload factors in pathology: quo vadis?

Publication productivity of pathology residents: a nationwide cohort study in France

The publication productivity of residents has been reported in various specialties, mainly in North America, but never in pathology. In France, pathology residents must defend a medical thesis to obtain the title of medical doctor and to practice medicine. The aim of this study was to assess the thesis performance and publication output of a nationwide cohort of pathology residents from six graduating classes in France. Among 231 theses, 110 (48%) resulted in publications, of which 95% were original articles (OA) and 74% were resident first-author publications. The median impact factor (IF) was 3.6 (2.8-5.9). During residency and in the 4 years following defense, residents published a median of 5 (2-10) total publications, 2 (1-6) OA, and 1 (0-3) first-author manuscripts. Among 1849 publications, 822 (44%) were first, second, or last-authored by residents. The median IF of the 362 (20%) OA published as first, second, and last author was 3.1 (2.4-5), 3.3 (2.2-5.2), and 3.2 (0.9-3.3), respectively. Only 44% of these OA were indexed in the pathology category according to Web of Science, with Virchows Arch being the most common journal. Residents who published their medical thesis had a higher median number of total publications, as well as first- and last-author OA (p = 0.0005, p = 0.001 and p = 0.007, respectively). The publication record of pathology residents goes beyond the field of pathology, with most contributions to non-pathology journals. The mandatory medical thesis provides a valuable opportunity for pathology residents to engage in research and may be the first step towards publication productivity.

The current troubled state of the global pathology workforce: a concise review

The histopathology workforce is a cornerstone of cancer diagnostics and is essential to the delivery of cancer services and patient care. The workforce has been subject to significant pressures over recent years, and this review considers them in the UK and internationally. These pressures include declining pathologist numbers, the increasing age of the workforce, and greater workload volume and complexity. Forecasts of the workforce's future in numerous countries are also not favourable - although this is not universal. Some in the field suggest that the effects of these pressures are already coming to bear, such as the financial costs of the additional measures needed to maintain clinical services. There is also some evidence of a detrimental impact on service delivery, patient care and pathologists themselves. Various solutions have been considered, including increasing the number of training places, enhancing recruitment, shortening pathology training and establishing additional support roles within pathology departments. A few studies have examined the effect of some of these solutions. However, the broader extent of their implementation and impact, if any, remains to be determined. In this regard, it is critical that future endeavours should focus on gaining a better understanding of the benefits of implemented workforce solutions, as well as obtaining more detailed and updated pathology workforce numbers. With a concentrated effort in these areas, the future of the pathology workforce could become brighter in the face of the increased demands on its services.

Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA Platform and successfully integrated 14 AI-based image analysis apps from eight different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Increasing demand for ophthalmic pathology: time trends in a laboratory with nationwide coverage

PURPOSE

To report the time trends in basic patient characteristics and the number of specimens received at a national referral center for ophthalmic pathology.

METHODS

Data on patient sex, age at surgical resection and geographical location of the referring unit were obtained for all specimens received at the St. Erik Ophthalmic Pathology laboratory, Stockholm, Sweden, between January 1^st, 1959, and December 31^st, 2021.

RESULTS

A total of 33 057 specimens had been received, of which 14 560 (44%) came from men and 18 477 (56%) from women (for 20 patients, the sex was not specified). The average annual percent change (AAPC) in the number specimens received was + 10.5%, whereas the Swedish population increased with 0.5% per year. Patients became older throughout the period, with an average yearly increase of patient age at surgery of 0.3 years (AAPC 0.2%). Overall, women were three years older than men at surgery (59.4 versus 56.4 years, P < 0.0001) The number of specimens increased with patient age from the first to the 8^th decade, after which it decreased to zero in the 11^th decade. The largest portion of patients had undergone their surgery in one of the hospitals or clinics in the capital region, with four of the five largest sources corresponding to the most populous counties in the country.

CONCLUSIONS

During six decades, the growth in number of specimens sent to our national referral center for ophthalmic pathology has greatly outpaced the growth of the population, indicating an increasing demand for subspecialized services. Throughout the period, patients have become older, and a higher number of specimens have been submitted from female patients.

Characterising the use of surgical pathology rush requests: a descriptive analysis and survey

This study aimed to characterise priority or 'rush' surgical pathology requests and identify potentially targetable factors. We performed a retrospective descriptive analysis of rush requests at our institution from 2016 to 2019 and conducted a survey asking pathologists about their perspectives on rush cases. There were 3677 rush cases, with case characteristics generally stable over the study period. Two categories of requests were identified based on hospital status; outpatient requests more frequently provided a specific date for diagnosis, while inpatient rush requests generally required a diagnosis as soon as possible. Most pathologists found rush cases to be somewhat more stressful compared with routine cases (65.2%) and found it very or extremely useful to know when a result is needed (86.9%). The use of hospitalisation status, and identifying if results are required by a certain date, may help in more effective triaging of rush surgical pathology cases.

Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?

Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression

OBJECTIVE

Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems.

METHODS

We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas.

RESULTS

Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting.

CONCLUSION

mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

Changing Trends in Dermatopathology Case Complexity: A 9-Year Academic Center Experience

CONTEXT.—

Pathology case volume and complexity impact clinical service burden, staffing, and reimbursement, particularly in an academic setting.

OBJECTIVE.—

To investigate dermatopathology case complexity by using indicators of challenging cases, which require increased clinical service effort.

DESIGN.—

A retrospective review was performed of dermatopathology cases during a 9-year period at a tertiary care academic center. A subset of cases was analyzed for which extractable data were available. Cases requiring the following metrics of complexity were identified: rush processing, consensus agreement, performance of immunohistochemistry, use of special histochemical stains, use of immunofluorescence, examination of additional tissue levels, review of a prior case, addition of an explanatory note, presence of multiple specimen parts, and use of intradepartmental consultation.

RESULTS.—

A total of 8173 cases were reviewed. During the same 3-month period of the year, there was a statistically significant increase in use of rush processing/interpretation, consensus review, number of cases requiring immunostains, special stains, levels, and an explanatory note, and cases reviewed by other subspecialists in the department from 2010 to 2019.

CONCLUSIONS.—

This study shows an increasing trend in dermatopathology case complexity, suggesting that overall clinical service efforts have increased. These findings may inform clinical service staffing and reimbursement.

Number of pathologists in Germany: comparison with European countries, USA, and Canada

The rapid development of pathology is in contrast to a shortage of qualified staff. The aims of the present study are to compile basic information on the numbers of German physicians in pathology and to compare it with the situation in Europe and overseas. In addition, model calculations will shed light on the effects of part-time working models. Various publicly accessible databases (EuroStat) as well as publications of medical associations and professional associations of European countries and the USA/Canada were examined. In addition, a survey was carried out among the institutes of German universities. Figures from 24 European countries and the USA/Canada were evaluated. With one pathologist per 47,989 inhabitants, the density of pathologists in Germany in relation to the population is the second-lowest in Europe (average: 32,018). Moreover, the proportion of pathologists among the physicians working in Germany is the lowest in Europe and at the same time lower than in the USA and Canada (Germany: 1:200, USA: 1:70, Canada: 1:49). The ratio of pathologists to medical specialists is shifted in the same direction. The survey among university pathologists revealed a relevant increase in the workload over the last 10 years. The majority of institutes can manage this workload only with considerable difficulties. With a ratio between specialists and residents of 1:1, the university institutes show a high commitment in the area of training. The results of this study indicate a shortage of pathologists in Germany that could lead to a bottleneck in large parts of the health system.

Hospital Medical and Nursing Managers' Perspectives on Health-Related Work Design Interventions. A Qualitative Study

BACKGROUND

Research indicates that the active support of managers is essential for the sustainable implementation of health-related work design interventions in organizations. However, little is known about managers' perceptions of such health promotion measures.

OBJECTIVE

Our study aims to provide information that help to foster managers active support of health-related work design interventions in hospitals. Based on Ajzen's Theory of Planned Behavior (TPB) we explore the attitudes, perceived organizational norms, and perceived behavioral control of managers in the hospital regarding such interventions.

METHODS

Semi-structured interviews with 37 managers (chief physicians, senior physicians, and senior nurses) were carried out in one German hospital. A software aided qualitative content analysis was applied.

RESULTS

We observed that the majority of managers are aware of the importance of health-related work design. We found a high variation in the perception of organizational norms related to mental health promotion of employees. Behavioral control for supporting interventions is perceived more on an individual (e.g., appraisal interviews, professional development or support) and team level (e.g., fair work schedule, regular team meetings), less on an organizational level.

CONCLUSION

To enable and to motivate hospital medical and nursing managers to support health-related work design, hospitals need to establish clear organizational norms that the health promotion of their employees is an important organizational goal. Moreover, managers need to get more work-design competencies and decision latitude to get more control. Important arguments for the top hospital management could be that health-related work design is highly effective for economic success, for treatment quality, and that the middle management already has a positive attitude toward the implementation of measures that help promote the mental health of their staff.

[Histopathological research laboratories in translational research : Conception and integration into the infrastructure of pathological institutes]

A systematic review of histopathology from experimental animal systems is an essential part of up-to-date biomedical research. Pathologists at university hospitals are especially and increasingly challenged by these specialized and time-consuming duties. This article presents and analyzes a new laboratory structure of comparative experimental pathology-jointly lead by veterinary and human pathologists-which might solve this problem. The focus is on the establishment and full integration of this laboratory structure into a local, regional, and nationwide biomedical research cluster. A detailed comparison with an established structure of routine histopathology laboratories discusses merits and benefits as well as disadvantages.

External Quality Assessment Identifies Training Needs to Determine the Neoplastic Cell Content for Biomarker Testing

Neoplastic cell content determination is crucial for biomarker testing. It is known that interobserver variation exists, but largescale data are missing about variation in tumor delineation and cell content determination. Results were obtained from the external quality assessment program for metastatic colorectal cancer from the European Society of Pathology (N = 5776 observations). The study included three parts: current practices were surveyed, neoplastic cell content estimations and delineations were retrieved from stained slides, and clinical reports were analyzed. Seventeen of 43 pathologists determined the neoplastic cell content in a tumor-rich area for DNA extraction and took immune cells (n = 37), tumor cell distribution (n = 33), desmoplastic stroma (n = 30), necrosis (n = 29), and mucus (n = 23) into account. The selected area was highly variable, and the average difference between the highest and lowest estimation ranged between 51% and 78% (2011 to 2017). The number of overestimations was alarmingly high in samples containing <30% tumor cells. Of concern is that 33 of 105 laboratories reported a wild-type result in a sample without tumor in 2017. Standardization of neoplastic cell content determination is needed for test outcome interpretation. The authors' data show variation in estimation practices, tumor delineations and estimations, and interpretation problems (n = 226 reports). Further training for selecting the most suitable block and creating clear reports is urgently needed.

Risk for molecular contamination of tissue samples evaluated for targeted anti-cancer therapy

With the increasing usage of sensitive PCR technology for pharmacogenetics, cross contamination becomes a significant concern. Researchers employed techniques which basically include replacing laboratory equipment after each sample preparation; however, there are no recommended guidelines. In the present work we wanted to evaluate the risk of cross contamination during tissue processing using the routine precaution measures. Twenty-one surgical samples of lung adenocarcinoma were used, of which 7 contained EGFR exon 19 mutation, 7 contained EGFR exon 21 mutation (p.L858R) and 7 were EGFR wild-type. The samples were ordered by alternating the mutation group to maximize the potential for cross contamination and underwent tissue sectioning and de-paraffinization. The entire process was performed using the same tools. Following DNA extraction all samples underwent PCR amplification and were scrutinized for small fractions of EGFR mutation using deep sequencing with the Ion torrent PGM technology. Twenty samples yielded results. The fraction of mutated copies was 41 ± 23% (range 11–66) for the cases with known exon 19 mutation and 48±24% (range 0–65) for the cases with known exon 21 mutations. No in-frame exon 19 deletion mutations were identified in the wild-type (WT) and exon 21 groups. The fraction of EGFR exon 21 (codon 858) mutations was 0.018±0.014% (range 0–0.05%) in the WT and exon 19 groups, which was not statistically different than the background sequencing artifact noise for the same base-pair alteration (p = 0.21). Our results suggest that standard precautions are sufficient for molecular pathology diagnosis of surgical samples and are not associated with increased risk of cross contamination.

The Importance of Histology and Pathology in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has become a valuable tool in cancer research. Even more, due to its capability to directly link molecular changes with histology, it holds the prospect to revolutionize tissue-based diagnostics. In order to learn to walk before running, however, information obtained through classical histology should not be neglected but rather used to its full capacity and integrated with mass spectrometry data to lead to a superior molecular histology synthesis. In order to achieve this, pathomorphological analyses have to be integrated into MSI analyses right from the beginning to avoid errors and pitfalls of MSI application possibly leading to incorrect or imprecise study outcomes. Such errors can be caused by different sample or tissue inherent factors or through factors in sample preparation. Future studies should, therefore, aim for a comprehensive incorporation of histology and pathology characteristics to ensure the generation of high-quality data in MSI to exploit its full capacity in tissue-based basic and translational research.

The effects of the DNA methyltranfserases inhibitor 5-Azacitidine on ageing, oxidative stress and DNA methylation of adipose derived stem cells

Human adipose tissue is a great source of adult mesenchymal stem cells (MSCs) which are recognized from their ability to self‐renew and differentiation into multiple lineages. MSCs have promised a vast therapeutic potential in treatment many diseases including tissue injury and immune disorders. However, their regenerative potential profoundly depends on patients’ age. Age‐related deterioration of MSC is associated with cellular senescence mainly caused by increased DNA methylation status, accumulation of oxidative stress factors and mitochondria dysfunction. We found that DNA methyltransferase (DNMT) inhibitor i.e. 5‐Azacytidine (5‐AZA) reversed the aged phenotype of MSCs. Proliferation rate of cells cultured with 5‐AZA was increased while the accumulation of oxidative stress factors and DNA methylation status were decreased. Simultaneously the mRNA levels of TET proteins involved in demethylation process were elevated in those cells. Moreover, cells treated with 5‐AZA displayed reduced reactive oxygen species (ROS) accumulation, ameliorated superoxide dismutase activity and increased BCL‐2/BAX ratio in comparison to control group. Our results indicates that, treating MSCs with 5‐AZA can be justified therapeutic intervention, that can slow‐down and even reverse aged‐ related degenerative changes in those cells.