Yildizfisher3895

The European Society for Digital and Integrative Pathology (ESDIP) was formally founded in 2016 in Berlin. After a well-participated annual general meeting, ESDIP members elected a new active structure for the next term of office. The priority goals of this new and highly motivated team will be to support the digital transformation in the pathology laboratories, to build inter-institutional bridges for cooperation, to establish a solid educational program, and to increase the collaboration with industry partners.

The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations - manually drawn by pathologists in digital slide viewers - is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling.

We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool.

Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations.

We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.

We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.

Recently, research data are increasingly shared through social media and other digital platforms. Traditionally, the influence of a scientific article has been assessed by the publishing journal's impact factor (IF) and its citation count. The Altmetric scoring system, a new bibliometric that integrates research "mentions" over digital media platforms, has emerged as a metric of online research distribution. The aim of this study was to explore the relationship of the Altmetric Score with IF and citation number within the pathology literature.

Citation count and Altmetric scores were obtained from the top 10 most-cited articles from the 15 pathology journals with the highest IF for 2013 and 2016. These variables were analyzed and correlated with each other, as well as the age of the publishing journal's Twitter account.

Three hundred articles were examined from the two cohorts. The total citation count of the articles decreased from 21,043 (2013) to 14,679 (2016), while the total Altmetric score increasicle citation count, suggesting that the Altmetric score and conventional bibliometrics can be treated as complementary metrics. Given the trend towards increasing use of social media, additional investigation is warranted to evaluate the evolving role of social media metrics to assess the dissemination and impact of scientific findings in the field of pathology.

Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website.

ThinPrep

Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI.

51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (

< 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.

The tumor microenvironment is highly heterogeneous, and it is understood to affect tumor progression and patient outcome. A number of studies have reported the prognostic significance of tumor-infiltrating lymphocytes and tumor budding in colorectal cancer (CRC). However, the significance of the intratumoral heterogeneity present in the spatial distribution of these features within the tumor immune microenvironment (TIME) has not been previously reported. Evaluating this intratumoral heterogeneity may aid the understanding of the TIME's effect on patient prognosis as well as identify novel aggressive phenotypes which can be further investigated as potential targets for new treatment.

In this study, we propose and apply two spatial statistical methodologies for the evaluation of the intratumor heterogeneity present in the distribution of CD3

and CD8

lymphocytes and tumor buds (TB) in 232 Stage II CRC cases. Getis-Ord hotspot analysis was applied to quantify the cold and hotspots, defined as regions with a significantly low or high number of each feature of interest, respectively. A novel spatial heatmap methodology for the quantification of the cold and hotspots of each feature of interest, which took into account both the interpatient heterogeneity and the intratumor heterogeneity, was further developed.

Resultant data from each analysis, characterizing the spatial intratumor heterogeneity of lymphocytes and TBs were used for the development of two new highly prognostic risk models.

Our results highlight the value of applying spatial statistics for the assessment of the intratumor heterogeneity. Both Getis-Ord hotspot and our proposed spatial heatmap analysis are broadly applicable across other tissue types as well as other features of interest.

The code underpinning this publication can be accessed at https//doi.org/10.17630/c2306fe9-66e2-4442-ad89-f986220053e2.

The code underpinning this publication can be accessed at https//doi.org/10.17630/c2306fe9-66e2-4442-ad89-f986220053e2.

Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity.

We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested aptimal models are those that incorporate both diversity and quantity into their platforms.s.

This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

The study is aimed to verify Aperio AT2 scanner for reporting on the digital pathology platform (DP) and to validate the cohort of pathologists in the interpretation of DP for routine diagnostic histopathological services in Wales, United Kingdom.

This was a large multicenter study involving seven hospitals across Wales and unique with 22 (largest number) pathologists participating. 7491 slides from 3001 cases were scanned on Leica Aperio AT2 scanner and reported on digital workstations with Leica software of e-slide manager. A senior pathology fellow compared DP reports with authorized reports on glass slide (GS). A panel of expert pathologists reviewed the discrepant cases under multiheader microscope to establish ground truth. 2745 out of 3001 (91%) cases showed complete concordance between DP and GS reports. Two hundred and fifty-six cases showed discrepancies in diagnosis, of which 170 (5.6%) were deemed of no clinical significance by the review panel. There were 86 (2.9%) clinically significant discpathology practise standardization across the whole country contributed to intra-observer variations.

This study did not include Cytopathology and in situ hybridization slides. Difficulty in achieving surgical pathology practise standardization across the whole country contributed to intra-observer variations.

The COVID-19 pandemic accelerated the widespread adoption of digital pathology (DP) for primary diagnosis in surgical pathology. This paradigm shift is likely to influence how we function routinely in the postpandemic era. this website We present learnings from early adoption of DP for a live digital sign-out from home in a risk-mitigated environment.

We aimed to validate DP for remote reporting from home in a real-time environment and evaluate the parameters influencing the efficiency of a digital workflow. Eighteen pathologists prospectively validated DP for remote use on 567 biopsy cases including 616 individual parts from 7 subspecialties over a duration from March 21, 2020, to June 30, 2020. The slides were digitized using Roche Ventana DP200 whole-slide scanner and reported from respective homes in a risk-mitigated environment.

Following re-review of glass slides, there was no major discordance and 1.2% (

= 7/567) minor discordance. The deferral rate was 4.5%. All pathologists reported from their respective homes from laptops with an average network speed of 20 megabits per second.

We successfully validated and adopted a digital workflow for remote reporting with available resources and were able to provide our patients, an undisrupted access to subspecialty expertise during these unprecedented times.

We successfully validated and adopted a digital workflow for remote reporting with available resources and were able to provide our patients, an undisrupted access to subspecialty expertise during these unprecedented times.

Digital pathology has been increasingly implemented for primary surgical pathology diagnosis. In our institution, digital pathology was recently deployed in the gynecologic (GYN) pathology practice. A notable challenge encountered in the digital evaluation of GYN specimens was high rates of scanning failure of specimens with fragmented as well as scant tissue. To improve tissue detection failure rates, we implemented a novel use of the collodion bag cell block preparation method.

In this study, we reviewed 108 endocervical curettage (ECC) specimens, representing specimens processed with and without the collodion bag cell block method (

= 56 without collodion bag,

= 52 with collodion bag).

Tissue detection failure rates were reduced from 77% (43/56) in noncollodion bag cases to 23/52 (44%) of collodion bag cases, representing a 42% reduction. The median total area of tissue detection failure per level was 0.35 mm

(interquartile range [IQR] 0.14, 0.70 mm

) for noncollodion bag cases and 0.08 mm

(IQR 0.