Introducción: En un servicio de anatomía patológica se analiza la carga laboral en tiempo médico en función de la complejidad de las muestras recibidas, y se valora su distribución entre los patólogos, presentado un nuevo algoritmo informático que favorece una distribución equitativa. Métodos: Siguiendo las directrices para la «Estimación de la carga de trabajo en citopatología e histopatología (tiempo médico) atendiendo al catálogo de muestras y procedimientos de la SEAP-IAP (2.ª edición)» se determinan las unidades de carga laboral (UCL) por patólogo y UCL global del servicio, la carga media laboral que soporta el servicio (factor MU), el tiempo de dedicación de cada patólogo a la actividad asistencial y el número de patólogos óptimo según la carga laboral del servicio. Resultados: Determinamos 12.197 UCL totales anuales para el patólogo jefe de servicio, así como 14.702 y 13.842 para los patólogos adjuntos, con una UCL global del servicio de 40.742. El factor MU calculado es 4,97. El jefe ha dedicado el 72,25% de su jornada a la asistencia y los adjuntos el 87,09 y 82,01%. El número de patólogos óptimo para el servicio es de 3,55. Conclusiones: Todos los resultados obtenidos demuestran la sobrecarga laboral médica, y la distribución de las UCL entre los patólogos no resulta equitativa. Se propone un algoritmo informático capaz de distribuir la carga laboral de manera equitativa, asociado al sistema de información del laboratorio, y que tenga en cuenta el tipo de muestra, su complejidad y la dedicación asistencial de cada patólogo.(AU)
Introduction: In a pathological anatomy service, the workload in medical time is analyzed based on the complexity of the samples received and its distribution among pathologists is assessed, presenting a new computer algorithm that favors an equitable distribution. Methods: Following the second edition of the Spanish guidelines for the estimation of workload in cytopathology and histopathology (medical time) according to the Spanish Pathology Society-International Academy of Pathology (SEAP-IAP) catalog of samples and procedures, we determined the workload units (UCL) per pathologist and the overall UCL of the service, the average workload of the service (MU factor), the time dedicated by each pathologist to healthcare activity and the optimal number of pathologists according to the workload of the service. Results: We determined 12 197 total annual UCL for the chief pathologist, as well as 14 702 and 13 842 UCL for associate pathologists, with an overall of 40 742 UCL for the whole service. The calculated MU factor is 4.97. The chief pathologist devoted 72.25% of his working day to healthcare activity while associate pathologists dedicated 87.09% and 82.01% of their working hours. The optimal number of pathologists for the service is found to be 3.55. Conclusions: The results demonstrate medical work overload and a non-equitable distribution of UCLs among pathologists. We propose a computer algorithm capable of distributing the workload in an equitable manner. It would be associated with the laboratory information system and take into account the type of specimen, its complexity and the dedication of each pathologist to healthcare activity.(AU)
Humans , Male , Female , Pathology , Workload , Pathologists , Pathology Department, Hospital , Algorithms
No standard tool to measure pathologist workload currently exists. An accurate measure of workload is needed for determining the number of pathologists to be hired, distributing the workload fairly among pathologists, and assessing the overall cost of pathology consults. Initially, simple tools such as counting cases or slides were used to give an estimate of the workload. More recently, multiple workload models, including relative value units (RVUs), the Royal College of Pathologists (RCP) point system, Level 4 Equivalent (L4E), Work2Quality (W2Q), and the University of Washington, Seattle (UW) slide count method, have been developed. There is no "ideal" model that is universally accepted. The main differences among the models come from the weights assigned to different specimen types, differential calculations for organs, and the capture of additional tasks needed for safe and timely patient care. Academic centers tend to see more complex cases that require extensive sampling and additional testing, while community-based and private laboratories deal more with biopsies. Additionally, some systems do not account for teaching, participation in multidisciplinary rounds, quality assurance activities, and medical oversight. A successful workload model needs to be continually updated to reflect the current state of practice.Awareness about physician burnout has gained attention in recent years and has been added to the World Health Organization's International Classification of Diseases (World Health Organization, WHO) as an occupational phenomenon. However, the extent to which this affects pathologists is not well understood. According to the WHO, burnout syndrome is diagnosed by the presence of three components: emotional exhaustion, depersonalization from one's work (cynicism related to one's job), and a low sense of personal achievement or accomplishment. Three drivers of burnout are the demand for productivity, lack of recognition, and electronic health records. Prominent consequences of physician burnout are economic and personal costs to the public and to the providers.Wellness is physical and mental well-being that allows individuals to manage stress effectively and to thrive in both their professional and personal lives. To achieve wellness, it is necessary to understand the root causes of burnout, including over-work and working under stressful conditions. Wellness is more than the absence of stress or burnout, and the responsibility of wellness should be shared by pathologists themselves, their healthcare organization, and governing bodies. Each pathologist needs to take their own path to achieve wellness.
Burnout, Professional , Pathologists , Workload , Humans
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. CONCLUSION: 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.
Pathology , Students, Medical , Humans , Pilot Projects , Students, Medical/psychology , Pathology/education , Germany , Clinical Competence , Neoplasms/pathology , Education, Medical, Undergraduate , Teaching , Curriculum , Pathologists/education , Male , Female
INTRODUCTION: In a pathological anatomy service, the workload in medical time is analyzed based on the complexity of the samples received and its distribution among pathologists is assessed, presenting a new computer algorithm that favors an equitable distribution. METHODS: Following the second edition of the Spanish guidelines for the estimation of workload in cytopathology and histopathology (medical time) according to the Spanish Pathology Society-International Academy of Pathology (SEAP-IAP) catalog of samples and procedures, we determined the workload units (UCL) per pathologist and the overall UCL of the service, the average workload of the service (MU factor), the time dedicated by each pathologist to healthcare activity and the optimal number of pathologists according to the workload of the service. RESULTS: We determined 12 197 total annual UCL for the chief pathologist, as well as 14 702 and 13 842 UCL for associate pathologists, with an overall of 40 742 UCL for the whole service. The calculated MU factor is 4.97. The chief pathologist devoted 72.25% of his working day to healthcare activity while associate pathologists dedicated 87.09% and 82.01% of their working hours. The optimal number of pathologists for the service is found to be 3.55. CONCLUSIONS: The results demonstrate medical work overload and a non-equitable distribution of UCLs among pathologists. We propose a computer algorithm capable of distributing the workload in an equitable manner. It would be associated with the laboratory information system and take into account the type of specimen, its complexity and the dedication of each pathologist to healthcare activity.
Pathology Department, Hospital , Workload , Humans , Pathologists , Algorithms
Idiopathic Interstitial Pneumonias (IIPs) are a heterogeneous group of the broader category of Interstitial Lung Diseases (ILDs), pathologically characterized by the distortion of lung parenchyma by interstitial inflammation and/or fibrosis. The American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of the IIPs was published in 2002 and then updated in 2013, with the authors emphasizing the need for a multidisciplinary approach to the diagnosis of IIPs. The histological evaluation of IIPs is challenging, and different types of IIPs are classically associated with specific histopathological patterns. However, morphological overlaps can be observed, and the same histopathological features can be seen in totally different clinical settings. Therefore, the pathologist's aim is to recognize the pathologic-morphologic pattern of disease in this clinical setting, and only after multi-disciplinary evaluation, if there is concordance between clinical and radiological findings, a definitive diagnosis of specific IIP can be established, allowing the optimal clinical-therapeutic management of the patient.
Idiopathic Interstitial Pneumonias , Pathologists , Humans , Consensus , Interdisciplinary Studies , Respiratory Rate , Idiopathic Interstitial Pneumonias/diagnosis
OBJECTIVE: To analyse quantitatively the adequacy of demographics of clinical information and highlight specific areas of neglect, by assessing the adequacy of filling out histopathology request forms. STUDY DESIGN: Clinical Audit. Place and Duration of the Study: Department of Pathology, Dow University of Health Sciences (DUHS), Karachi, Pakistan, from January to September 2021. METHODOLOGY: A retrospective audit was carried out on the request forms of surgically resected tumours and biopsies. The recorded details of the patients' demographics and biopsy, clinical history and examination, and intraoperative findings were analysed. RESULTS: Out of 175 forms, patients' names were written in 174 (99.4%) while medical record numbers were written in 113 (64.6%). The doctors' names were given in 172 (98.3%) forms and phone numbers in 34 (19.4%). A clinical diagnosis was provided in 164 (93.7%) forms, while 152 (86.9%) forms correctly entered the biopsy site. Sixty-seven (38.3%) forms included the correct nature of the biopsy. Relevant operative details were provided in half of the forms. Symptoms and their duration were mentioned in 144 (82.3%) and 100 (57.1%), respectively. The form-filling rate was the same for both benign and malignant tumours. CONCLUSION: This study shows that in a significant proportion of cases, complete relevant information is not provided to the histopathologists on request forms for logistics. KEY WORDS: Histopathology, Request forms, Tumours, Audit.
Neoplasms , Physicians , Humans , Pathologists , Retrospective Studies , Biopsy
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.
Endometrial Hyperplasia , Observer Variation , Pathologists , Humans , Female , Endometrial Hyperplasia/diagnosis , Endometrial Hyperplasia/pathology , Endometrial Neoplasms/diagnosis , Endometrial Neoplasms/pathology
Next-generation sequencing (NGS) is increasingly being utilized as an ancillary tool for diagnostically challenging melanocytic neoplasms. It is incumbent upon the pathology community to perform studies assessing the benefits and limitations of these tools in specific diagnostic scenarios. One of the most challenging diagnostic scenarios faced by skin pathologists involves accurate diagnosis of desmoplastic melanocytic neoplasms (DMNs). In this study, 20 expert melanoma pathologists rendered a diagnosis on 47 DMNs based on hematoxylin and eosin sections with demographic information. After submitting their diagnosis, the experts were given the same cases, but this time with comprehensive genomic sequencing results, and asked to render a diagnosis again. Identification of desmoplastic melanoma (DM) improved by 7%, and this difference was statistically significant ( P <0.05). In addition, among the 15 melanoma cases, in the pregenomic assessment, only 12 were favored to be DM by the experts, while after genomics, this improved to 14 of the cases being favored to be DM. In fact, some cases resulting in metastatic disease had a substantial increase in the number of experts recognizing them as DM after genomics. The impact of the genomic findings was less dramatic among benign and intermediate-grade desmoplastic tumors (BIDTs). Interobserver agreement also improved, with the Fleiss multirater Kappa being 0.36 before genomics to 0.4 after genomics. NGS has the potential to improve diagnostic accuracy in the assessment of desmoplastic melanocytic tumors. The degree of improvement will be most substantial among pathologists with some background and experience in bioinformatics and melanoma genetics.
High-Throughput Nucleotide Sequencing , Melanoma , Observer Variation , Skin Neoplasms , Humans , Melanoma/genetics , Melanoma/diagnosis , Melanoma/pathology , Skin Neoplasms/genetics , Skin Neoplasms/pathology , Skin Neoplasms/diagnosis , Female , Male , Reproducibility of Results , Predictive Value of Tests , Middle Aged , Adult , Aged , Pathologists , Biomarkers, Tumor/genetics
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.
Communication , Mohs Surgery , Humans , New Zealand , Pathologists , Patient Satisfaction
The Silva pattern-based classification of HPV-associated endocervical adenocarcinoma has become an integral part of the histologic assessment of these tumors. Unfortunately, the Silva system reproducibility has had mixed results in past studies, and clinical practice still favors the FIGO stage assessment in directing therapeutic interventions for patients. In our study, we aimed to assess our institution's concordance including not only gynecologic pathologists, but also pathology trainees through a series of 69 cases. The grouped total kappa concordance from all participants was 0.439 (Moderate), with an overall trainee kappa of 0.417 (moderate) and an overall pathologist kappa of 0.460 (moderate). Perfect concordance among all 10 study participants was seen in 8/69 cases (11.6 %), corresponding to 5/22 Pattern A cases (22.7 %), 0/16 Pattern B cases (0 %), and 3/31 Pattern C cases (9.7 %), with similar findings between trainees and pathologists when compared within their own cohorts. Recurrence was identified in 2 Pattern A cases, indicating a potential issue with limited excisional specimens which may not fully appreciate the true biologic aggressiveness of the lesions.
Adenocarcinoma , Papillomavirus Infections , Pathologists , Uterine Cervical Neoplasms , Humans , Female , Uterine Cervical Neoplasms/virology , Uterine Cervical Neoplasms/pathology , Adenocarcinoma/virology , Adenocarcinoma/pathology , Papillomavirus Infections/pathology , Papillomavirus Infections/virology , Papillomavirus Infections/complications , Adult , Middle Aged , Gynecology/education , Reproducibility of Results , Observer Variation , Aged
PURPOSE: This prologue introduces the forum "Pediatric Feeding Disorder and the School-Based SLP: An Evidence-Based Update for Clinical Practice" and informs the reader of the scope of articles presented. METHOD: The guest prologue author provides a brief history of pediatric feeding and swallowing services in the public-school setting, including previous forums on swallowing and feeding services in the schools (Logemann & O'Toole, 2000; McNeilly & Sheppard, 2008). The concepts that have been learned since the 2008 forum are shared. The contributing authors in the forum are introduced, and a summary is provided for each of the articles. CONCLUSIONS: The articles provide evidence-based information on topics that are uniquely of interest to school-based speech-language pathologists managing pediatric feeding and swallowing in their districts. The topics shared in this forum range from relevant information on anatomy, physiology, developmental milestones, and differential diagnosis to therapeutic practice when identifying and treating pediatric feeding and swallowing in the school setting. The forum also includes focused articles on the necessity of collaboration with families during the treatment process, current information on legal parameters dealing with school-based pediatric feeding disorder services, and a framework for assessment and treating pediatric feeding disorder in the school setting.
Feeding and Eating Disorders , Speech-Language Pathology , Humans , Child , Pathologists , Speech , Language , Learning , Feeding and Eating Disorders/diagnosis , Feeding and Eating Disorders/therapy
Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.
Artificial Intelligence , B7-H1 Antigen , Biomarkers, Tumor , Observer Variation , Humans , B7-H1 Antigen/analysis , B7-H1 Antigen/metabolism , Biomarkers, Tumor/analysis , Biomarkers, Tumor/metabolism , Reproducibility of Results , Carcinoma, Transitional Cell/pathology , Carcinoma, Transitional Cell/metabolism , Carcinoma, Transitional Cell/diagnosis , Urinary Bladder Neoplasms/pathology , Urinary Bladder Neoplasms/metabolism , Urologic Neoplasms/pathology , Urologic Neoplasms/metabolism , Immunohistochemistry/methods , Pathologists , Urothelium/pathology , Urothelium/metabolism
CONTEXT: Recent clinical trials indicate that HER2-targeted therapy may benefit HER2-low breast cancer patients including HER2 score 1+ or 2+ and no gene amplification. Concordance between pathologists and between core biopsy and surgical excision in establishing HER2-low status was evaluated. DESIGN: 57 patients with HER2 negative breast cancer (IHC 0, 1+, or 2+, no gene amplification) by core biopsy were included. Core biopsy and representative tumor from corresponding surgical excision was immunostained for HER2. Original HER2 IHC scores were interpreted using 2018 guidelines. Three pathologists independently interpreted again under 2023 guidelines. Kappa statistic evaluated agreement of HER2 IHC scores. RESULTS: Applying 2023 guidelines, HER2 IHC scores were concordant among study pathologists in 46 of 57 (81 %) core biopsy and 50 of 57 (88 %) surgical resections. Kappa statistics were 0.78 and 0.85 (substantial agreement), for inter-pathologist agreement of core biopsy and surgical resections under 2023 guidelines; 0.55 (moderate agreement) for agreement between first interpretation by 2018 guidelines and second interpretation by 2023 guidelines; and 0.13 (slight agreement) for agreement in HER2 consensus scores between outside core and surgical resection and 0.49 (moderate agreement) for inside core and surgical resection. Low HER2 expression was found in 28 of 57 (49 %) core biopsy and in 25 of 57 (44 %) surgical excisions. CONCLUSIONS: Interobserver agreement among study pathologists was good in core biopsy and surgical excisions, applying updated 2023 guidelines. Intratumoral heterogeneity in protein expression and preanalytical factors may result in variable identification of HER2-low status in core biopsy and surgical excision specimens.
Breast Neoplasms , Immunohistochemistry , Pathologists , Receptor, ErbB-2 , Humans , Breast Neoplasms/pathology , Breast Neoplasms/metabolism , Breast Neoplasms/diagnosis , Receptor, ErbB-2/metabolism , Female , Immunohistochemistry/methods , Biomarkers, Tumor/metabolism , Middle Aged , Biopsy, Large-Core Needle/methods , Observer Variation , Adult , Aged
Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.
Carcinoma, Non-Small-Cell Lung , Deep Learning , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/pathology , Lung Neoplasms/pathology , Pathologists , B7-H1 Antigen/metabolism , Immunohistochemistry , Biomarkers, Tumor/metabolism
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Brain Neoplasms , Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Lung Neoplasms/pathology , Carcinoma, Non-Small-Cell Lung/pathology , Algorithms , Pathologists
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.
Artificial Intelligence , Prostatic Neoplasms , Humans , Male , Pathologists , Prostate , Biopsy
