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.

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.

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.

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.

Occupational lung/thoracic diseases are a major global public health issue. They comprise a diverse spectrum of health conditions with complex pathology, most of which arise following chronic heavy workplace exposures to various mineral dusts, metal fumes, or following inhaled organic particulate reactions. Many occupational lung diseases could become irreversible; thus accurate diagnosis is mandatory to minimize dust exposure and consequently reduce damage to the respiratory system. Lung biopsy is usually required when exposure history is inconsistent with imaging, in case of unusual or new exposures, in case of unexpected malignancy, and in cases in which there are claims for personal injury and legal compensation. In this paper, we provide an overview of the most frequent occupational lung diseases with a focus on pathological diagnosis. This is a paper that summarizes the expert opinion from a group of European pathologists, together with contributions from other specialists who are crucial for the diagnosis and management of these diseases. Indeed, tight collaboration of all specialists involved in the workup is mandatory as many occupational lung diseases are misdiagnosed or go unrecognized. This document provides a guide for pathologists in practice to facilitate the accurate diagnosis of occupational lung disease. The review article reports relevant topics discussed during an educational course held by expert pathologists, active members of the Pulmonary Pathology Working Group of the European Society of Pathology. The course was endorsed by the University of Padova as a \"winter school\" (selected project in the call for \"Shaping a World-class University\" 2022).

BACKGROUND: The shortage of pathologists in Germany, coupled with an aging workforce, requires innovative approaches to attract medical students to the field. Medical education must address different learning styles to ensure that all students are successful.
METHODS: The pilot project \"Practical Pathology\" aims to enhance students\' understanding of pathology by providing hands-on experience in macroscopic gross analysis through the use of tumor dummies built from scratch.
RESULTS: An evaluation survey, completed by 63 participating students provided positive feedback on the course methodology, its relevance to understanding the pathology workflow, and its improvement over traditional teaching methods. The majority of students recognized the importance of hands-on training in medical education. Students with previous work experience rated the impact of the course on knowledge acquisition even more positively.
CONCLUSIONS: The course improved students\' understanding of pathological processes and potential sources of clinical-pathological misunderstanding. An increase in motivation for a potential career in the field of pathology was observed in a minority of students, although this exceeded the percentage of pathologists in the total medical workforce.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists\' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Telepathology, as a subset of teleconsulting, is pathology interpretation performed at a distance. Telepathology is not a new phenomenon, but since ~2015, significant advances in information technology and telecommunications coupled with the pandemic have led to unprecedented sophistication, accessibility, and use of telepathology in human and veterinary medicine. Furthermore, telepathology can connect veterinary practices to distant laboratories and provide support for underserved animals and communities. Through our scoping review, we provide an overview of how telepathology is being used in veterinary medicine, identify gaps in the literature, and highlight future areas of research and service development. We searched MEDLINE, CAB Abstracts, and the gray literature, and included all relevant literature. Despite the widespread use of digital microscopy in large veterinary diagnostic laboratories, we identified a paucity of literature describing the use of telepathology in veterinary medicine, with a significant gap in studies addressing the validation of whole-slide imaging for primary diagnosis. Underutilization of telepathology to support postmortem examinations conducted in the field was also identified, which indicates a potential area for service development. The use of telepathology is increasing in veterinary medicine, and pathologists must keep pace with the changing technology, ensure the validation of innovative technologies, and identify novel uses to advance the profession.

OBJECTIVE: Reproducible diagnoses of endometrial hyperplasia (EH) remains challenging and has potential implications for patient management. This systematic review aimed to identify pathologist-specific factors associated with interobserver variation in the diagnosis and reporting of EH.
METHODS: Three electronic databases, namely MEDLINE, Embase and Web of Science, were searched from 1st January 2000 to 25th March 2023, using relevant key words and subject headings. Eligible studies reported on pathologist-specific factors or working practices influencing interobserver variation in the diagnosis of EH, using either the World Health Organisation (WHO) 2014 or 2020 classification or the endometrioid intraepithelial neoplasia (EIN) classification system. Quality assessment was undertaken using the QUADAS-2 tool, and findings were narratively synthesised.
RESULTS: Eight studies were identified. Interobserver variation was shown to be significant even amongst specialist gynaecological pathologists in most studies. Few studies investigated pathologist-specific characteristics, but pathologists were shown to have different diagnostic styles, with some more likely to under-diagnose and others likely to over-diagnose EH. Some novel working practices were identified, such as grading the \"degree\" of nuclear atypia and the incorporation of objective methods of diagnosis such as semi-automated quantitative image analysis/deep learning models.
CONCLUSIONS: This review highlighted the impact of pathologist-specific factors and working practices in the accurate diagnosis of EH, although few studies have been conducted. Further research is warranted in the development of more objective criteria that could improve reproducibility in EH diagnostic reporting, as well as determining the applicability of novel methods such as grading the degree of nuclear atypia in clinical settings.

This paper explores the role of teledermatology (TD) in Mohs micrographic surgery (MMS) at various stages of patient care. The study aims to assess the benefits, limitations, and patient experiences surrounding TD integration into MMS practices. We conducted a PubMed search using keywords related to TD and MMS, categorizing selected articles into pre-operative, intra-operative, and post-operative stages of MMS. TD reduced waiting times (26.10 days for TD compared to 60.57 days for face-to-face [FTF]) and consultation failure rates (6% for TD vs. 17% for FTF) for MMS preoperative consultations. It also shortened time to treatment by two weeks and led to notable travel savings (162.7 min, 144.5 miles, and $60.00 per person). Telepathology facilitated communication and decision-making during MMS, improving accuracy and efficiency, especially in challenging cases requiring collaboration where physical presence of another surgeon or pathologist is not feasible. Telepathology definitively diagnosed benign lesions and malignant tumors in 81.8% of cases (18/22). Additionally, there was a 95% agreement between conventional light microscopy diagnosis and telepathology in tumors (19/20), and 100% agreement for all 20 Mohs frozen section consultations. For post-operative follow-up, telephone follow-up (TFU) and text messaging proved effective, cost-efficient alternatives with high patient satisfaction (94% in New Zealand and 96% in the U.K.) and early complication identification. This study underscores TD\'s multifaceted benefits in MMS: enhanced patient experience preoperatively, improved communication during surgery, and cost-effective postoperative follow-up. Limitations include the financial expense and technical issues that can arise with TD (connectivity problems, delays in video/audio transmission, etc.). Further studies are needed to explore emerging TD modalities in post-operative patient management. The integration of TD into MMS signifies a progressive step in dermatological care, offering convenient, cost-effective, and better solutions with the potential to enhance patient experiences and outcomes.