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Item does not contain fulltext Even though entirely digitized microscopic tissue sections (whole slide images, WSIs) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists' reading time. This study aimed to compare the time required to perform the microscopic assessment by pathologists between a conventional workflow (an optical microscope) and digitized WSIs. WSI was used in primary diagnostics at the Laboratory for Pathology Eastern Netherlands for several years (LabPON, Hengelo, The Netherlands). Cases were read either in a traditional workflow, with the pathologist recording the time required for diagnostics and reporting, or entirely digitally. Reading times were extracted from image management system log files, and the digitized workflow was fully integrated into the laboratory information system. The digital workflow saved time in the majority of case categories, with prostate biopsies saving the most (68% time gain). Taking into account case distribution, the digital workflow produced an average gain of 12.3%. Using WSI instead of conventional microscopy significantly reduces pathologists' reading times. Pathologists must work in a fully integrated environment to fully reap the benefits of a digital workflow.

Introduction A melanoma can originate at the subcutis without any visible skin lesion. Case presentation A 73-year old patient came to the outpatient clinic with a subcutaneous nodule on the right thigh without any visible lesion of the skin. It turned out to be a primary subcutaneous melanoma that could be classified as a primary dermal melanoma (PDM). Discussion A PDM is a very rare subtype of melanoma that stands out for its excellent prognosis in comparison to cutaneous melanomas. No valid reliable staging system or treatment guideline exists for this entity, Breslow depth might overestimate the clinical aggressiveness possibly leading to overtreatment. Conclusion It is of great importance for the clinician to be familiar with a primary dermal melanoma. It deserves an appropriate place in the current AJCC system and a treatment guideline for this unique melanoma subtype with relativity excellent prognosis would be beneficial., Highlights • A melanoma can originate at the subcutis without any visible skin lesion. • This entity can be classified as a primary dermal melanoma and stands out for its excellent prognosis. • There is no adequate staging system yet for patients with a primary dermal melanoma. • Breslow depth might overestimate the clinical aggressiveness in patients with a primary dermal melanoma, possibly leading to overtreatment.

Aims: Even though entirely digitized microscopic tissue sections (whole slide images) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists’ reading time. This study aimed to compare the time required to perform microscopic assessment by pathologists between a conventional workflow (using an optical microscope and glass slides) and digitized whole slide images (WSI).Methods: The study was performed in the Laboratory for Pathology Eastern Netherlands (LabPON, Hengelo, The Netherlands), which has been using WSI in primary diagnostics for several years. Cases were either read in a conventional workflow, while the pathologist recorded the time required for diagnostics and reporting or were read using a fully digitized workflow. The digitized workflow was fully integrated with the laboratory information system, and reading times were extracted from the log files of the image management system. Results: Analysis of over 3800 cases showed a time gain in favour of the digital workflow for most case categories, with the highest gain (68% time gain) for prostate biopsies (prostate care program). On average, a gain of 12.3% was found for the digital workflow, taking the case distribution into account. Conclusions: It was concluded that transitioning from conventional microscopy to use of WSI significantly reduces the pathologists’ reading times. A fully integrated pathologist working environment is required to fully benefit from the benefits of a digital workflow.

AimsMethods and resultsMost validation studies on digital pathology diagnostics have been performed in single institutes. Because rapid consultation on cases with extramural experts is one of the most important uses for digital pathology laboratory networks, the aim of this study was to validate a whole-slide image-based teleconsultation network between three independent laboratories.Each laboratory contributed 30 biopsies and/or excisions, totalling 90 specimens (776 slides) of varying difficulty and covering a wide variety of organs and subspecialties. All slides were scanned centrally at x40 scanning magnification and uploaded, and subsequently assessed digitally by 16 pathologists using the same image management system and viewer. Each laboratory was excluded from digital assessment of their own cases. Concordance rates between the two diagnostic modalities (light microscopic versus digital) were compared. Loading speed of the images, zooming latency and focus quality were scored. Leaving out eight minor discrepancies without any clinical significance, the concordance rate between remote digital and original microscopic diagnoses was 97.8%. The two cases with a major discordance (for which the light microscopic diagnoses were deemed to be the better ones) resulted from a different interpretation of diagnostic criteria in one case and an image quality issue in the other case. Average scores for loading speed of the images, zooming latency and focus quality were 2.37 (on a scale up to 3), 2.39 (scale up to 3) and 3.06 (scale up to 4), respectively.ConclusionsThis validation study demonstrates the suitability of a teleconsultation network for remote digital consultation using whole-slide images. Such networks may contribute to faster revision and consultation in pathology while maintaining diagnostic standards.

AIM: To evaluate the prognostic value of the tumor-stroma ratio (TSR) in rectal cancer. METHODS: TSR was determined on hematoxylin and eosin stained histological sections of 154 patients treated for rectal adenocarcinoma without prior neoadjuvant treatment in the period 1996-2006 by two observers to assess reproducibility. Patients were categorized into three categories: TSR-high [carcinoma percentage (CP) ≥ 70%], TSR-intermediate (CP 40%, 50% and 60%) and TSR-low (CP ≤ 30%). The relation between categorized TSR and survival was analyzed using Cox proportional hazards model. RESULTS: Thirty-six (23.4%) patients were scored as TSR-low, 70 (45.4%) as TSR-intermediate and 48 (31.2%) as TSRhigh. TSR had a good interobserver agreement (κ = 0.724, concordance 82.5%). Overall survival (OS) and disease free survival (DFS) were significantly better for patients with a high TSR (P = 0.01 and P = 0.02, respectively). A similar association existed for disease specific survival (P = 0.06). In multivariate analysis, patients without lymph node metastasis and an intermediate TSR had a higher risk of dying from rectal cancer (HR = 5.27, 95%CI: 1.54-18.10), compared to lymph node metastasis negative patients with a high TSR. This group also had a worse DFS (HR = 6.41, 95%CI: 1.84-22.28). An identical association was seen for OS. These relations were not seen in lymph node metastasis positive patients. CONCLUSION: The TSR has potential as a prognostic factor for survival in surgically treated rectal cancer patients, especially in lymph node negative cases.

Contains fulltext : 204300.pdf (Publisher’s version ) (Open Access) PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images. METHODS: Histological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a 'stroma-high' or 'stroma-low' group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times. RESULTS: With stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio = 2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio = 2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis. CONCLUSIONS: This work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.

In pathology laboratory it recently becomes possible to make digital images with a whole slide scanner. This provides the pathologist with the possibility to view microscopic images on a computer screen. It is pivotal that the scanned microscopic images are of good quality and resemble the images seen through the microscope as much as possible. It is expected that digitalization in pathology diagnostics will improve in quality, safety and efficiency. The Laboratory for Pathology East Netherlands (LabPON) in liaison with other partners started testing several scanners and digital pathology software to improve the workflow for digital diagnostics. This thesis describes this process. After the transition to digital diagnostics LabPON put a lot of effort into optimizing the working conditions for pathologists. It is well known that there are health issues related to working with the microscope. In principle, these ergonomic complaints can be prevented when working digitally for diagnosing tissues. On the other hand, new health issues (like repetitive strain injury) may arise for the pathologist. At LabPON we tested different types of computers and monitors as well as new user interphases were used and adapted to create an optimal work environment for the pathologist. In order to work efficiently for the pathologist the Laboratory Information and Management System (LIMS) and Image Management System (IMS) are completely integrated into the LabPON workflow. Permutations of patient information and material can be prevented by working in this manner. Another interesting feature of working with the digital method of diagnosing is that it is possible to work outside of LabPON and enables you to easily consult specialists from other pathology laboratories. We compared the quality of consultation with tissue slides and the digital method of consulting tissues. The digital consultation was more efficient than sending patient material (i.e. tissue slides) by post and the time for diagnosing a patient was reduced by 1 to several days. In addition, the risk of damaging or losing unique patient material when sending by post is eliminated. In chapter 4 we demonstrate that with the transition to digital diagnostics we saved 19 hours of lab and administrative work per day in LabPON. This makes digital diagnostics more efficient and more cost effective. With diagnosing digitally the pathologist seems to be 12% more efficient than with the microscope. With the use of special software, digital tissue images can be analysed as well. This software can improve the efficiency but also the quality of the diagnosis. An additional benefit of using software is that it can do a lot of pre-work for the pathologist. At LabPON together with our partners we demonstrated that the tumor cell and tumor stroma ratio in colon cancer can be determined more accurately when using digital pathology software. In general, digitalization of diagnostics in pathology laboratories and the use of image analysis software will help improve the quality and efficiency in pathology practice, as well as improving patient care.

Case Presentation A 37-year-old woman presented with a 2-month history of recurrent hemoptysis and coughing. Her symptoms started 2 months after the delivery of her third child. In total, she endured four episodes of hemoptysis. All pregnancies were induced by intracytoplasmic sperm injections. She lacked a pulmonary or smoking history and had no history of foreign body aspiration or intubation. There was no dyspnea, dysphagia, fever, or chest pain, and the patient did not complain of purulent sputum. She currently did not use medication and was generally in good health.

Introduction: A whole slide image (WSI) is typically comprised of a macro image (low-power snapshot of the entire glass slide) and stacked tiles in a pyramid structure (with the lowest resolution thumbnail at the top). The macro image shows the label and all pieces of tissue on the slide. Many whole slide scanner vendors do not readily show the macro overview to pathologists. We demonstrate that failure to do so may result in a serious misdiagnosis. Materials and Methods: Various examples of errors were accumulated that occurred during the digitization of glass slides where the virtual slide differed from the macro image of the original glass slide. Such examples were retrieved from pathology laboratories using different types of scanners in the USA, Canada, Europe, and Asia. Results: The reasons for image errors were categorized into technical problems (e.g., automatic tissue finder failure, image mismatches, and poor scan coverage) and human operator mistakes (e.g., improper manual region of interest selection). These errors were all detected because they were highlighted in the macro image. Conclusion: Our experience indicates that WSI can be subject to inadvertent errors related to glitches in scanning slides, corrupt images, or mistakes made by humans when scanning slides. Displaying the macro image that accompanies WSIs is critical from a quality control perspective in digital pathology practice as this can help detect these serious image-related problems and avoid compromised diagnoses.

Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Contains fulltext : 193420.pdf (Publisher’s version ) (Open Access) Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

BackgroundAimsThe benefits of digital pathology for workflow improvement and thereby cost savings in pathology, at least partly outweighing investment costs, are being increasingly recognised. Successful implementations in a variety of scenarios have started to demonstrate the cost benefits of digital pathology for both research and routine diagnosis, contributing to a sound business case encouraging further adoption. To further support new adopters, there is still a need for detailed assessment of the impact that this technology has on the relevant pathology workflows, with an emphasis on time-saving.To assess the impact of digital pathology adoption on logistic laboratory tasks (i.e. not including pathologists' time for diagnosis-making) in the Laboratorium Pathologie Oost Nederland, a large regional pathology laboratory in The Netherlands.Methods and resultsConclusionsTo quantify the benefits of digitisation, we analysed the differences between the traditional analogue and new digital workflows, carried out detailed measurements of all relevant steps in key analogue and digital processes, and compared the time spent. We modelled and assessed the logistic savings in five workflows: (i) routine diagnosis; (ii) multidisciplinary meeting; (iii) external revision requests; (iv) extra stainings; and (v) external consultation. On average, >19 working hours were saved on a typical day by working digitally, with the highest savings in routine diagnosis and multidisciplinary meeting workflows.By working digitally, a significant amount of time could be saved in a large regional pathology laboratory with a typical case mix. We also present the data in each workflow per task and concrete logistic steps to allow extrapolation to the context and case mix of other laboratories.

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images., Published in Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) 2017