Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

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.

Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.

OBJECTIVES: The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. METHODS: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. RESULTS: Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. CONCLUSIONS: The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.

Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions.

INTRODUCTION: Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. METHODS: Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. RESULTS: Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. CONCLUSION: Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.

Digital pathology world tour.

Objective: Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited. Methods: A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP. Results: Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as "diagnostic" (147/4041, 4%) where DP was utilized for routine diagnostic workflow and "non-diagnostic" (3894/4041, 96%) for all other applications. The "non-diagnostic" articles were further categorized according to DP application including "artificial intelligence" (33%), "education" (5%), "narrative" (17%) for reviews and editorials, and "technical" (45%) for pure research publications. Conclusion: This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature.

Digital Examination of LYmph node CYtopathology Using the Sydney system (DELYCYUS): An international, multi-institutional study.

BACKGROUND: After a series of standardized reporting systems in cytopathology, the Sydney system was recently introduced to address the need for reproducibility and standardization in lymph node cytopathology. Since then, the risk of malignancy for the categories of the Sydney system has been explored by several studies, but no studies have yet examined the interobserver reproducibility of the Sydney system. METHODS: The authors assessed interobserver reproducibility of the Sydney system on 85 lymph node fine-needle aspiration cytology cases reviewed by 15 cytopathologists from 12 institutions in eight different countries, resulting in 1275 diagnoses. In total, 186 slides stained with Diff-Quik, Papanicolaou, and immunocytochemistry were scanned. A subset of the cases included clinical data and results from ultrasound examinations, flow cytometry immunophenotyping, and fluorescence in situ hybridization analysis. The study participants assessed the cases digitally using whole-slide images. RESULTS: Overall, the authors observed an almost perfect agreement of cytopathologists with the ground truth (median weighted Cohen κ = 0.887; interquartile range, κ = 0.210) and moderate overall interobserver concordance (Fleiss κ = 0.476). There was substantial agreement for the inadequate and malignant categories (κ = 0.794 and κ = 0.729, respectively), moderate agreement for the benign category (κ = 0.490), and very slight agreement for the suspicious (κ = 0.104) and atypical (κ = 0.075) categories. CONCLUSIONS: The Sydney system for reporting lymph node cytopathology shows adequate interobserver concordance. Digital microscopy is an adequate means to assess lymph node cytopathology specimens.

The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board.

Objective: The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered. Methods: On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results: Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion: Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.

Value of Artificial Intelligence in Evaluating Lymph Node Metastases.

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

Effect of Radio-Chemotherapy on PD-L1 Immunohistochemical Expression in Head and Neck Squamous Cell Carcinoma.

BACKGROUND: Programmed death-ligand 1 (PD-L1) checkpoint inhibitors represent a mainstay of therapy in head and neck squamous cell cancer (HNSCC). However, little is known about the influence of combined therapy on PD-L1 expression. The study aims to gather evidence on this topic. METHODS: A systematic search was carried out in electronic databases Pubmed-MEDLINE and Embase to retrieve studies on the comparison of PD-L1 expression before and after conventional therapy. Data were extracted and a quantitative analysis with pooled odds ratios (ORs) was performed when applicable. RESULTS: Of 5688 items, 15 were finally included. Only a minority of studies assessed PD-L1 with the recommended combined positive score (CPS). The results are highly heterogeneous, with some studies reporting an increase in PD-L1 expression and others reporting a decrease. Three studies allowed for quantitative analysis and showed a pooled OR of 0.49 (CI 0.27-0.90). CONCLUSIONS: From the present evidence, a clear conclusion towards an increase or decrease in PD-L1 expression after combined therapy cannot be drawn, but even with few studies available, a trend towards an increase in expression in tumor cells at a cutoff of 1% can be noted in patients undergoing platinum-based therapy. Future studies will provide more robust data on the effect of combined therapy on PD-L1 expression.

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.

Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.

Digital diagnostic cytopathology: Has the pandemic brought us closer?

The COVID-19 pandemic has acted as a powerful change driver in the field of pathology and has had relevant consequences on the practice of cytopathology, in terms of changes in workload, rates of malignancy, and the performance of cytology. At the same time, regulatory authorities have relaxed their requirements for the deployment of digital pathology for remote diagnostic reporting. However, most of these improvements have concerned digital histopathology. Data from a literature search show that experiences in digital cytopathology during the pandemic have concerned mainly educational and academic activities. From a broader point of view, when searching for all published literature on digital pathology, only a minority of papers deal with cytopathology, but a noticeable increase in publications has been seen in the last 10 years, with an upward trend toward a maximum of papers in 2021. Indeed, the pandemic has led to greater awareness of the possibility of digital for cytopathology as well.

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta.

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.

Relevance of the College of American Pathologists guideline for validating whole slide imaging for diagnostic purposes to cytopathology.

Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guideline includes three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been beyond the scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology. A systematic search was carried out in PubMed-MEDLINE and Embase databases up to November 2021 to identify all publications regarding validation of WSI in cytology. Each article was reviewed to determine if SRs and/or GPSs recommended by the CAP guideline were adequately satisfied. Of 3963 retrieved articles, 25 were included. Only 4/25 studies (16%) satisfied all three SRs, with only one publication (1/25, 4%) fulfilling all three SRs and nine GPSs. Lack of a suitable validation dataset was the main missing SR (16/25, 64%) and less than a third of the studies reported intra-observer variability data (7/25, 28%). Whilst the CAP guideline for WSI validation in clinical practice helped the widespread adoption of digital pathology, more evidence is required to routinely employ WSI for diagnostic purposes in cytopathology practice. More dedicated validation studies satisfying all SRs and/or GPSs recommended by the CAP are needed to help expedite the use of WSI for primary diagnosis in cytopathology.

WSI validation studies in breast and gynecological pathology.

BACKGROUND: In the last two decades, there has been marked development in virtual slide technology as well as its application in various subspecialties of pathology. In particular, there have been several studies examining the utility of whole slide imaging (WSI) in breast and gynecological pathology. The aim of this systematic review is to analyse published evidence regarding validation studies of WSI applied specifically to the female genital tract and breast pathology. METHODS: A systematic search was carried out in Pubmed and Embase databases and studies dealing with the validation of a WSI system for breast and gynaecological pathology. The topics evaluated concerned expertise of engaged pathologists, varied specimens, scanners, washout period, experience viewing WSI, and diagnostic concordance of WSI to traditional light microscopic diagnoses. RESULTS: Of 1467 publications retrieved, 23 studies were included. Most of these studies concerned breast pathology. Validation guidelines recommended by the College of American Pathologists pertaining to a dataset of at least 60 cases, washout period, and recording intra-observer variability were followed by most studies. Major challenges encountered with WSI included difficulty identifying high-grade nuclear atypia and mitotic count for borderline ovarian tumors, interpretation of squamous intraepithelial lesions in liquid-based cervical cytology, and grading breast cancer. DISCUSSION: Published data demonstrates the value of utilizing WSI in breast and gynecological pathology. Key issues reported with WSI systems were problems related to focus, resolution and the contrast and brightness of immunohistochemical staining patterns. Grading breast cancer and mitotic count remained challenging in WSI as in conventional microscopy.

Digital pathology all stars.

Digital pathology plays an important role in accelerating the progression of healthcare and the potential benefits of adopting digital technologies have been solidly established. Despite this, real-world data suggest that a fully digital approach to the histological workflow has been implemented in a minority only of pathology laboratories. The e-learning event "Digital Pathology All Stars" was conceived by the University and Hospital Trust of Verona and comprised traditional lectures made by well-recognized experts in Digital Pathology from all over the world. The meeting aimed to promote the exchange of knowledge to support and strengthen digital pathology adoption and implementation.

Livedoid skin reaction to 2nd dose of mRNA Covid-19 vaccine.

Skin often represents a target organ for adverse drug reactions and this also applies to the mRNA vaccines against Sars-CoV-2. Here we present a case of extensive livedoid reaction after 2nd dose of BNT162b-2 vaccine with massive blood skin extravasation and no systemic symptoms apart from anemization. The 30-year-old woman developed progressively enlarging livedoid lesions on limbs and abdomen. Histology showed a near-normal epidermis and a very mild interstitial mixed inflammatory infiltrate with extensive blood extravasation in mid- and deep dermis. Diagnosis was adverse reaction to vaccine with skin capillary hyperpermeability and anaemization with lower than diagnostic features of cutaneous small vessel vasculitis. To date, no cases of a livedoid skin reaction associated to Covid-19 vaccine have been reported, and this case illustrates that massive livedoid reaction can be another kind of skin reaction to mRNA Covid-19 vaccine.

Comparison of three validated PD-L1 immunohistochemical assays in urothelial carcinoma of the bladder: interchangeability and issues related to patient selection.

Different programmed cell death-ligand 1 (PD-L1) assays and scoring algorithms are being used in the evaluation of PD-L1 expression for the selection of patients for immunotherapy in specific settings of advanced urothelial carcinoma (UC). In this paper, we sought to investigate three approved assays (Ventana SP142 and SP263, and Dako 22C3) in UC with emphasis on implications for patient selection for atezolizumab/pembrolizumab as the first line of treatment. Tumors from 124 patients with invasive UC of the bladder were analyzed using tissue microarrays (TMA). Serial sections were stained with SP263 and SP142 on Ventana Benchmark Ultra and with 22C3 on Dako Autostainer Link 48. Stains were evaluated independently by two observers and scored using the combined positive score (CPS) and tumor infiltrating immune cells (IC) algorithms. Differences in proportions (DP), overall percent agreement (OPA), positive percent agreement (PPA), negative percent agreement (NPA), and Cohen κ were calculated for all comparable cases. Good overall concordance in analytic performance was observed for 22C3 and SP263 with both scoring algorithms; specifically, the highest OPA was observed between 22C3 and SP263 (89.6%) when using CPS. On the other hand, SP142 consistently showed lower positivity rates with high differences in proportions (DP) compared with 22C3 and SP263 with both CPS and IC, and with a low PPA, especially when using the CPS algorithm. In conclusion, 22C3 and SP263 assays show comparable analytical performance while SP142 shows divergent staining results, with important implications for the selection of patients for both pembrolizumab and atezolizumab.

Risk factors of extraneural spreading in astrocytomas and oligodendrogliomas in donors with gliomas: A systematic review.

BACKGROUND: Patients with a history of primary brain tumors can be eligible for organ donation under extended criteria. The risk assessment of tumor transmission via organ transplant in primary brain tumors is primarily based on the assessment of tumor histotype and grade. Previous surgeries, chemo-/radiotherapy, and ventriculo-peritoneal shunt placement can lead to a disruption of the blood-brain barrier, concurring to an increase in the transmission risk. AIM: To investigate the role of tumor transmission risk factors in donors with oligodendrogliomas and astrocytomas. METHODS: We searched PubMed and EMBASE databases for studies reporting extraneural spreading of oligodendrogliomas and astrocytomas and extracted clinical-pathological data on the primary tumor histotype and grade, the elapsed time from the diagnosis to the onset of metastases, sites and number of metastases, prior surgeries, prior radiotherapy and/or chemotherapy, ventriculo-atrial or ventriculo-peritoneal shunt placement, and the presence of isocitrate dehydrogenase 1/2 mutation and 1p/19q codeletion. Statistical analysis was performed using R software. Statistical correlation between chemotherapy or radiotherapy and the presence of multiple extra-central nervous system metastases was analyzed using χ 2 and Fischer exact test. The Kaplan-Meier method was used to evaluate the presence of a correlation between the metastasis-free time and: (1) Localization of metastases; (2) The occurrence of intracranial recurrences; and (3) The occurrence of multiple metastases. RESULTS: Data on a total of 157 patients were retrieved. The time from the initial diagnosis to metastatic spread ranged from 0 to 325 mo in patients with oligodendrogliomas and 0 to 267 mo in those with astrocytomas. Respectively, 19% and 39% of patients with oligodendroglioma and astrocytoma did not receive any adjuvant therapy. The most frequent metastatic sites were bone, bone marrow, and lymph nodes. The lungs and the liver were the most commonly involved visceral sites. There was no significant correlation between the occurrence of multiple metastases and the administration of adjuvant chemo-/radiotherapy. Patients who developed intracranial recurrences/metastases had a significantly longer extraneural metastasis-free time compared to those who developed extraneural metastases in the absence of any intra- central nervous system spread. CONCLUSION: A long follow-up time does not exclude the presence of extraneural metastases. Therefore, targeted imaging of bones and cervical lymph nodes may improve safety in the management of these donors.