BACKGROUND: In 2022, our team launched the pioneering national proficiency testing (PT) scheme for the pathological diagnosis of breast cancer, rapidly establishing its credibility throughout China. Aiming to continuously monitor and improve the proficiency of Chinese pathologists in breast pathology, the second round of the PT scheme was initiated in 2023, which will expand the number of participating institutions, and will conduct a nationwide investigation into the interpretation of HER2 0, 1+, and 2+/FISH- categories in China.
METHODS: The methodology employed in the current round of PT scheme closely mirrors that of the preceding cycle in 2022, which is designed and implemented according to the \"Conformity assessment-General requirements for proficiency testing\"(GB/T27043-2012/ISO/IEC 17043:2010). More importantly, we utilized a statistics-based method to generate assigned values to enhance their robustness and credibility.
RESULTS: The final PT results, published on the website of the National Quality Control Center for Cancer ( http://117.133.40.88:3927 ), showed that all participants passed the testing. However, a few institutions demonstrated systemic biases in scoring HER2 0, 1+, and 2+/FISH- with accuracy levels below 59%, considered unsatisfactory. Especially, the concordance rate for HER2 0 cases was only 78.1%, indicating challenges in distinguishing HER2 0 from low HER2 expression. Meanwhile, areas for histologic type and grade interpretation improvement were also noted.
CONCLUSIONS: Our PT scheme demonstrated high proficiency in diagnosing breast cancer in China. But it also identified systemic biases in scoring HER2 0, 1+, and 2+/FISH- at some institutions. More importantly, our study highlighted challenges in the evaluation at the extreme lower end of the HER2 staining spectrum, a crucial area for further research. Meanwhile, it also revealed the need for improvements in interpreting histologic types and grades. These findings strengthened the importance of robust quality assurance mechanisms, like the nationwide PT scheme conducted in this study, to maintain high diagnostic standards and identify areas requiring further training and enhancement.

Endocervical adenocarcinomas (EACs) are a group of malignant neoplasms associated with diverse pathogenesis, morphology, and clinical behavior. As a component of the International Society of Gynecological Pathologists International Endocervical Adenocarcinoma Project, a large international retrospective cohort of EACs was generated in an effort to study potential clinicopathological features with prognostic significance that may guide treatment in these patients. In this study, we endeavored to develop a robust human papillomavirus (HPV)-associated EAC prognostic model for surgically treated International Federation of Gynecology and Obstetrics (FIGO) stage IA2 to IB3 adenocarcinomas incorporating patient age, lymphovascular space invasion (LVSI) status, FIGO stage, and pattern of invasion according to the Silva system (traditionally a 3-tier system). Recently, a 2-tier/binary Silva pattern of invasion system has been proposed whereby adenocarcinomas are classified into low-risk (pattern A/pattern B without LVSI) and high-risk (pattern B with LVSI/pattern C) categories. Our cohort comprised 792 patients with HPV-associated EAC. Multivariate analysis showed that a binary Silva pattern of invasion classification was associated with recurrence-free and disease-specific survival (P < 0.05) whereas FIGO 2018 stage I substages were not. Evaluation of the current 3-tiered system showed that disease-specific survival for those patients with pattern B tumors did not significantly differ from that for those patients with pattern C tumors, in contrast to that for those patients with pattern A tumors. These findings underscore the need for prospective studies to further investigate the prognostic significance of stage I HPV-associated EAC substaging and the inclusion of the binary Silva pattern of invasion classification (which includes LVSI status) as a component of treatment recommendations.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.

Postmortem examination of deceased production animals with appropriate ancillary testing is fundamental to determining causes of morbidity and mortality. Reaching a definitive diagnosis is crucial to evidence-based herd management and treatment decisions that safeguard animal health and welfare, food safety, and human health. However, for a range of reasons, carcasses sometimes cannot be examined in a veterinary diagnostic laboratory. As a result, postmortem examinations of farmed animals, including cattle, are often performed on-farm by the referring veterinarian (rVet) with tissue samples submitted to a veterinary diagnostic laboratory for ancillary testing. For various reasons, field postmortems can be associated with lower diagnostic rates. We investigated real-time pathologist-assisted field postmortem examination (rtPAP) assistance to beef cattle rVets to gauge any improvement in attaining a final diagnosis. We found that rtPAPs improved the success of reaching a final diagnosis compared to unassisted field postmortem examinations. Both the participating bovine rVets and the pathologists saw benefits to the rtPAPs, with bovine rVets indicating that they would utilize this service in the future if available. Our proof-of-concept study demonstrated the positive role of rtPAPs in diagnosing beef cattle disease and speaks to the need for telepathology services supporting food animal rVets and producers.

Multidisciplinary team (MDT) meetings have emerged as a promising approach for the treatment of cancer patients. These meetings involve a team of healthcare professionals from different disciplines working together to develop a holistic, patient-centered treatment. Although MDT meetings are well established in oncology, they play a minor role in other diseases. Recent evidence suggests that the implementation of MDT meetings can improve patient outcomes in musculoskeletal infections. The aim of this retrospective, observational study was to present the agenda of our multidisciplinary limb board including live microscopy with a special focus on the pathologist\'s role. The descriptive analysis of the limb board included 66 cases receiving live microscopy at the meeting and a total of 124 histopathological findings and 181 stainings. We could elucidate that pathologists seem to play an important role especially in clarifying the correct diagnosis. In 80.3 % of the findings, the pathologist specified the clinical diagnosis of the requesting physician leading to a consensus-based treatment plan for each patient. The implementation of MDT meetings including live microscopy in patients with musculoskeletal infections holds potential benefits, such as improved communication, scientific collaboration, and raising clinicians\' awareness and understanding of histopathology findings. However, potential challenges, such as organizational effort and technical prerequisites should be considered.

Australia has the highest incidence of skin cancer globally. Early detection and treatment of skin cancer is critical for positive patient outcomes. General practitioners (GPs) play a central role in skin cancer management in Australia.
Collaboration between GPs and pathologists can improve the accuracy of skin cancer diagnosis. However, for improvement to occur, clear communication and high-quality specimens are essential.
Inadequate clinical information and suboptimal biopsy specimens can hinder diagnosis. Improved communication, targeted training and selecting appropriate biopsy techniques are essential. A collaborative approach, guided by recommended techniques and clear guidelines, can minimise errors and improve patient outcomes in Australia\'s GP-led skin cancer management system.

Asbestos is a carcinogen that can cause lung cancer. The suspicion that a lung cancer diagnosis may be associated with exposure to asbestos has no bearing on treatment. However, attributing an individual\'s lung cancer to asbestos exposure has important medicolegal implications and may impact public health measures and policy. Simultaneous exposure(s) to other carcinogens (such as tobacco smoke, silica and many others) adds complexity while trying to answer the causation question. The Helsinki criteria were formulated to assist attributing lung cancer to previous asbestos exposure. Surrogate markers can be used and include signs of asbestosis and pleural plaques. The most widely used criterion for the presence of asbestosis is interstitial fibrosis in conjunction with 2 or more asbestos bodies/1 cm2 tissue section by light microscopy. Identification of asbestos bodies ty light pr electron microscopy provides an important element for asbestos diagnosis. However, fibrosis may be subtle, and the distribution of asbestos bodies is not uniform throughout the lungs, some types of asbestos fibres have low biopersistence, and not all types of asbestos readily form asbestos bodies. Additional criteria require knowledge of exposure history, which is often unknown to pathologists, but reliance on morphology in isolation may lead to mis-classification of interstitial lung disease as idiopathic. While a smoking-related lung cancer signature has emerged, an asbestos-related lung cancer signature has not yet been identified. In this review we will discuss practice points for the surgical pathologist.

The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists\' annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.

OBJECTIVE: We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists.
METHODS: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative and qualitative questions. Patterns in the respondents\' adoption and perspectives on these artificial intelligence tools were analyzed.
RESULTS: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly for information retrieval, proofreading, academic writing, and drafting pathology reports, highlighting a significant time-saving benefit. Academic pathologists demonstrated a better level of understanding of LLMs than their peers. Although chatbots sometimes provided incorrect general domain information, they were considered moderately proficient concerning pathology-specific knowledge. The technology was mainly used for drafting educational materials and programming tasks. The most sought-after feature in LLMs was their image analysis capabilities. Participants expressed concerns about information accuracy, privacy, and the need for regulatory approval.
CONCLUSIONS: Large language model applications are gaining notable acceptance among pathologists, with nearly half of respondents indicating adoption less than a year after the tools\' introduction to the market. They see the benefits but are also worried about these tools\' reliability, ethical implications, and security.

BACKGROUND: The ultimate aim of medical education is to produce successful practitioners, which is a goal that educators, students and stakeholders support. These groups consider success to comprise optimum patient care with consequently positive career progression. Accordingly, identification of the common educational features of such high-achieving doctors will facilitate the generation of clinical excellence amongst future medical trainees. In our study we source data from British clinical merit award schemes and subsequently identify the medical school origins of pathologists who have achieved at least national distinction.
METHODS: Britain operates Distinction Award/Clinical Excellence Award schemes which honour National Health Service doctors in Scotland, Wales and England who are identified as high achievers. This quantitative observational study used these awards as an outcome measure in an analysis of the 2019-20 dataset of all 901 national award-winning doctors. Where appropriate, Pearson\'s Chi-Square test was applied.
RESULTS: The top five medical schools (London university medical schools, Aberdeen, Edinburgh, Oxford and Cambridge) were responsible for 60.4% of the pathologist award-winners, despite the dataset representing 85 medical schools. 96.4% of the pathologist merit award-winners were from European medical schools. 9.0% of the pathologist award-winners were international medical graduates in comparison with 11.4% of all 901 award-winners being international medical graduates.
CONCLUSIONS: The majority of pathologists who were national merit award-winners originated from only five, apparently overrepresented, UK university medical schools. In contrast, there was a greater diversity in medical school origin among the lower grade national award-winners; the largest number of international medical graduates were in these tier 3 awards (13.9%). As well as ranking educationally successful university medical schools, this study assists UK and international students, by providing a roadmap for rational decision making when selecting pathologist and non-pathologist medical education pathways that are more likely to fulfil their career ambitions.