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AIMS: Urothelial carcinoma (UC) demonstrates significant molecular and histologic heterogeneity. The WHO 2022 classification has hinted at adding molecular signatures to the morphologic diagnosis. As morphology and associated molecular repertoire may potentially translate to choices of and response to therapy and relapse rate, broader acceptability of recognizing these key features among uropathologists is needed. This prompted an international survey to ascertain the practice patterns in classical/subtype UC among uropathologists across the globe. METHODS AND RESULTS: A survey instrument was shared among 98 uropathologists using SurveyMonkey software. Anonymized respondent data were analysed. The response rate was 85%. A majority were in concordance with the profiles of luminal (93%) and basal (82%) types. Opinion on the FGFR3 testing platform was variable. While 95% concurred that TERT promoter mutation is the key driver in UC, 72% had the opinion that APOBEC mutagenesis is the main signature in muscle invasive bladder cancer (MIBC). Uropathologists have divergent opinions on MIBC and ERCC2 mutations. Among the participants, 94% would quantify aggressive micropapillary and sarcomatoid histology, while 88% would reevaluate another transurethral resection of the bladder tumour specimen in nonmuscle invasive tumour with micropapillary, small cell, or sarcomatoid histology. A leading number agreed to specific molecular signatures of micropapillary (93%), plasmacytoid (97%), and small cell (86%) subtypes. Ninety-six percent of participants agreed that a small-cell component portends a more aggressive course and should be treated with neoadjuvant chemotherapy and 63% would perform HER2/neu testing only on oncologist's request in advanced tumours. Ninety percent agreed that microsatellite instability testing, although not a standard protocol, should be considered in young patients with upper tract UC. Eighty-six percent agreed that UC with high tumour mutational burden would be a better candidate for immunotherapy. CONCLUSION: In the era of precision medicine, enhanced understanding of molecular heterogeneity of UC will contribute to better therapeutic options, novel biomarker discovery, innovative management protocols, and outcomes. Our survey provides a broad perspective of pathologists' perceptions and experience regarding incorporation of histomolecular approaches to "personalize" therapy. Due to variable clinical adoption, there is a need for additional data using uniform study criteria. This will drive generation of best practice guidelines in this area for widespread and consistent clinical utility.
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Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.
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Carcinoma de Células Renales , Neoplasias Renales , Mutación , Proteína Supresora de Tumores del Síndrome de Von Hippel-Lindau , Secuenciación Completa del Genoma , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/terapia , Carcinoma de Células Renales/mortalidad , Carcinoma de Células Renales/patología , Humanos , Neoplasias Renales/genética , Neoplasias Renales/terapia , Proteína Supresora de Tumores del Síndrome de Von Hippel-Lindau/genética , Pronóstico , Masculino , Femenino , Variaciones en el Número de Copia de ADN , Persona de Mediana Edad , Epigénesis Genética , Anciano , Regulación Neoplásica de la Expresión Génica , Inmunoterapia/métodosRESUMEN
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
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Recurrencia Local de Neoplasia , Tomografía Computarizada por Tomografía de Emisión de Positrones , Prostatectomía , Neoplasias de la Próstata , Radiocirugia , Humanos , Masculino , Prostatectomía/métodos , Neoplasias de la Próstata/cirugía , Neoplasias de la Próstata/radioterapia , Conducto Deferente/diagnóstico por imagen , Anciano , Persona de Mediana EdadRESUMEN
Prostate cancer is typically of acinar adenocarcinoma type but can occasionally present as neuroendocrine and/or ductal type carcinoma. These are associated with clinically aggressive disease, and the former often arises on a background of androgen deprivation therapy, although it can also arise de novo. Two prostate cancer cases were sequenced by exome capture from archival tissue. Case 1 was de novo small cell neuroendocrine carcinoma and ductal adenocarcinoma with three longitudinal samples over 5 years. Case 2 was a single time point after the development of treatment-related neuroendocrine prostate carcinoma. Case 1 showed whole genome doubling in all samples and focal amplification of AR in all samples except the first time point. Phylogenetic analysis revealed a common ancestry for ductal and small cell carcinoma. Case 2 showed 13q loss (involving RB1) in both adenocarcinoma and small cell carcinoma regions, and 3p gain, 4p loss, and 17p loss (involving TP53) in the latter. By using highly curated samples, we demonstrate for the first time that small-cell neuroendocrine and ductal prostatic carcinoma can have a common ancestry. We highlight whole genome doubling in a patient with prostate cancer relapse, reinforcing its poor prognostic nature.
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Carcinoma de Células Acinares , Carcinoma Ductal , Carcinoma de Células Pequeñas , Neoplasias Pulmonares , Neoplasias de la Próstata , Carcinoma Pulmonar de Células Pequeñas , Masculino , Humanos , Neoplasias de la Próstata/genética , Antagonistas de Andrógenos , Filogenia , Carcinoma Ductal/genética , Evolución MolecularRESUMEN
AIM: we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) "Best Practice Recommendations for Implementing Digital Pathology (DP)," at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off. METHODS AND RESULTS: twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting. CONCLUSION: we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides.
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AIMS: With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges. METHODS: An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020. RESULTS: 19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts. CONCLUSIONS: The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.
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Patólogos , Patología Clínica , Humanos , Patología Clínica/métodos , Interpretación de Imagen Asistida por Computador/métodos , Encuestas y Cuestionarios , Reino UnidoRESUMEN
Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2-3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.
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There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (<1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed.
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Renal cell carcinoma (RCC) occurs in a number of cancer predisposition syndromes, but the genetic architecture of susceptibility to RCC is not well defined. We investigated the frequency of pathogenic and likely pathogenic (P/LP) germline variants in cancer susceptibility genes (CSGs) within a large series of unselected RCC participants. Whole-genome sequencing data on 1336 RCC participants and 5834 controls recruited to the UK 100 000 Genomes Project, a nationwide multicentre study, was analyzed to identify rare P/LP short variants (single nucleotide variants and insertions/deletions ranging from 1 to 50 base pairs) and structural variants in 121 CSGs. Among 1336 RCC participants [mean: 61.3 years (±12 SD), range: 13-88 years; 64% male], 85 participants [6.4%; 95% CI (5.1, 7.8)] had one or more P/LP germline variant in a wider range of CSGs than previously recognized. A further 64 intragenic variants in CSGs previously associated with RCC were classified as a variant of uncertain significance (VUS) (24 'hot VUSs') and were considered to be of potential clinical relevance as further evaluation might results in their reclassification. Most patients with P variants in well-established CSGs known to predispose to renal cell carcinoma (RCC-CSGs) were aged <50 years. Burden test analysis for filtered variants in CSGs demonstrated a significant excess of CHEK2 variants in European RCC participants compared with the healthy European controls (P = 0.0019). Approximately, 6% of the patients with RCC unselected for family history have a germline variant requiring additional follow-up analysis. To improve diagnostic yield, we suggest expanding the panel of RCC-CSGs tested to include CHEK2 and all SDHx subunits and raising the eligibility criteria for age-based testing.
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Carcinoma de Células Renales , Neoplasias Renales , Carcinoma de Células Renales/genética , Femenino , Predisposición Genética a la Enfermedad , Células Germinativas , Mutación de Línea Germinal/genética , Humanos , Neoplasias Renales/genética , MasculinoRESUMEN
Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at [Formula: see text] magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall 'usability' (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86-90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.
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Inteligencia Artificial , Interpretación de Imagen Asistida por Computador , Algoritmos , Humanos , Interpretación de Imagen Asistida por Computador/métodos , Masculino , Redes Neurales de la Computación , Estudios RetrospectivosRESUMEN
Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.
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Patología Clínica , Humanos , Irlanda , Patólogos , Reino UnidoRESUMEN
BACKGROUND: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. METHODS: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. RESULTS: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. CONCLUSION: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.
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BACKGROUND: Pathological grading of non-invasive urothelial carcinoma has a direct impact upon management. This study evaluates the reproducibility of grading these tumours on glass slides and digital pathology. METHODS: Forty eight non-invasive urothelial bladder carcinomas were graded by three uropathologists on glass and on a digital platform using the 1973 WHO and 2004 ISUP/WHO systems. RESULTS: Consensus grades for glass and digital grading gave Cohen's kappa scores of 0.78 (2004) and 0.82 (1973). Of 142 decisions made on the key therapeutic borderline of low grade versus high grade urothelial carcinoma (2004) by the three pathologists, 85% were in agreement. For the 1973 grading system, agreement overall was 90%. CONCLUSIONS: Agreement on grading on glass slide and digital screen assessment is similar or in some cases improved, suggesting at least non-inferiority of DP for grading of non-invasive urothelial carcinoma.
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Carcinoma Papilar/diagnóstico , Pruebas Diagnósticas de Rutina/normas , Variaciones Dependientes del Observador , Patólogos/normas , Neoplasias de la Vejiga Urinaria/diagnóstico , Humanos , Clasificación del Tumor , Reproducibilidad de los Resultados , Estudios RetrospectivosRESUMEN
BACKGROUND: There are recognised potential pitfalls in digital diagnosis in urological pathology, including the grading of dysplasia. The World Health Organisation/International Society of Urological Pathology (WHO/ISUP) grading system for renal cell carcinoma (RCC) is prognostically important in clear cell RCC (CCRCC) and papillary RCC (PRCC), and is included in risk stratification scores for CCRCC, thus impacting on patient management. To date there are no systematic studies examining the concordance of WHO/ISUP grading between digital pathology (DP) and glass slide (GS) images. We present a validation study examining intraobserver agreement in WHO/ISUP grade of CCRCC and PRCC. METHODS: Fifty CCRCCs and 10 PRCCs were graded (WHO/ISUP system) by three specialist uropathologists on three separate occasions (DP once then two GS assessments; GS1 and GS2) separated by wash-out periods of at least two-weeks. The grade was recorded for each assessment, and compared using Cohen's and Fleiss's kappa. RESULTS: There was 65 to 78% concordance of WHO/ISUP grading on DP and GS1. Furthermore, for the individual pathologists, the comparative kappa scores for DP versus GS1, and GS1 versus GS2, were 0.70 and 0.70, 0.57 and 0.73, and 0.71 and 0.74, and with no apparent tendency to upgrade or downgrade on DP versus GS. The interobserver kappa agreement was less, at 0.58 on DP and 0.45 on GS. CONCLUSION: Our results demonstrate that the assessment of WHO/ISUP grade on DP is noninferior to that on GS. There is an apparent slight improvement in agreement between pathologists on RCC grade when assessed on DP, which may warrant further study.
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Carcinoma Papilar/patología , Carcinoma de Células Renales/patología , Neoplasias Renales/patología , Microscopía , Patólogos , Biopsia , Bases de Datos Factuales , Humanos , Clasificación del Tumor , Variaciones Dependientes del Observador , Valor Predictivo de las Pruebas , Reproducibilidad de los ResultadosRESUMEN
The ATPase p97 is a central component of the ubiquitin-proteasome degradation system. p97 uses its ATPase activity and co-factors to extract ubiquitinated substrates from different cellular locations, including DNA lesions, thereby regulating DNA repair pathway choice. Here, we find that p97 physically and functionally interacts with the MRE11-RAD50-NBS1 (MRN) complex on chromatin and that inactivation of p97 blocks the disassembly of the MRN complex from the sites of DNA damage upon ionizing radiation (IR). The inhibition of p97 function results in excessive 5'-DNA end resection mediated by MRE11 that leads to defective DNA repair and radiosensitivity. In addition, p97 inhibition by the specific small-molecule inhibitor CB-5083 increases tumor cell killing following IR both in vitro and in vivo. Mechanistically, this is mediated via increased MRE11 nuclease accumulation. This suggests that p97 inhibitors might be exploited to improve outcomes for radiotherapy patients.
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Adenosina Trifosfatasas/metabolismo , Muerte Celular/genética , ADN/genética , Proteína Homóloga de MRE11/metabolismo , Proteínas Nucleares/metabolismo , Radiación Ionizante , HumanosRESUMEN
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.
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Aprendizaje Profundo , Interpretación de Imagen Asistida por Computador/métodos , Inmunohistoquímica , Patología Clínica/métodos , Neoplasias de la Próstata/diagnóstico , Automatización de Laboratorios/métodos , Biopsia , Humanos , Masculino , Flujo de TrabajoRESUMEN
Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.
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The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered.
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COVID-19 , Patología Clínica/métodos , Telepatología/métodos , Humanos , Patólogos , SARS-CoV-2 , Encuestas y Cuestionarios , Centros de Atención Terciaria , Reino UnidoRESUMEN
The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.