Background: The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit. Methods: This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented. Results: It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining. Conclusion: The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

Introduction: Centralization of cancer treatment entails a reassessment of the diagnostic tissue specimens. Packaging and shipment of glass slides from the local to the central pathology unit means that the standard procedure is time-consuming and that it is difficult to comply with governmental requirements. The aim was to evaluate whether real-time digital microscopy for urological cancer specimens during the primary diagnostic process can replace subsequent physical slide referral and reassessment without compromising diagnostic safety. Methods: From May to October 2014, tissue specimens from 130 patients with urological cancer received at Næstved Hospital's Pathology Department, and expected to be referred for further treatment at cancer unit of a university hospital, were diagnosed using standard light microscopy. In the event of diagnostic uncertainty, the VisionTek digital microscope (Sakura Finetek) was employed. The Pathology Department at Næstved Hospital was equipped with a digital microscope and three consultant pathologists were stationed at Rigshospitalet with workstations optimized for digital microscopy. Representative slides for each case were selected for consultation and live digital consultation took place over the telephone using remote access software. Time of start and finish for each case was logged. For the physically referred cases, time from arrival to sign-out was logged in the national pathology information system, and time spent on microscopy and reporting was noted manually. Diagnosis, number of involved biopsies, grade, and stage were compared between digital microscopy and conventional microscopy. Results: Complete data were available for all 130 cases. Standard procedure with referral of urological cancer specimens took a mean of 8 min 56 s for microscopy, reporting and sign-out per case. For live digital consultations, a mean of 18 min 37 s was spent on each consultation with 4 min 43 s for each case, depending on the number of digital slides included. Only in two cases could a consensus regarding the diagnosis not be reached during live consultation; this did not, it should be noted, affect patient treatment. Complete agreement between conventional and digital histopathology diagnosis was reached in all the 53 patients referred to central pathology units. The participating pathologists were in general comfortable using live digital microscopy, but they emphasized that a fast internet connection was essential for a smooth consultation. Discussion and Conclusion: An almost perfect agreement between live digital and conventional microscopy was observed in this study. Live digital consultation allowed cases to be referred from local hospitals to central cancer units without the standard delay caused by shipment. Only a few preselected specimen slides for each patient were presented in live consultation, which reduced the time spent on diagnosis compared to using the conventional method. Implementation of real-time digital microscopy would result in quicker turnaround and patient referral time, and with careful selection of relevant specimen slides for consultation, diagnostic safety would not be compromised.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Prostatic intraepithelial neoplasia (PIN) represents premalignant tissue involving epithelial growth confined in the lumen of prostatic acini. In the attempts to understand oncogenesis in the human prostate, early neoplastic changes can be modeled in the mouse with genetic manipulation of certain tumor suppressor genes or oncogenes. As with many early pathological changes, the PIN lesions in the mouse prostate are macroscopically small, but microscopically spanning areas often larger than single high magnification focus fields in microscopy. This poses a challenge to utilize full potential of the data acquired in histological specimens. We use whole prostates fixed in molecular fixative PAXgene™, embedded in paraffin, sectioned through and stained with H&E. To visualize and analyze the microscopic information spanning whole mouse PIN (mPIN) lesions, we utilize automated whole slide scanning and stacked sections through the tissue. The region of interests is masked, and the masked areas are processed using a cascade of automated image analysis steps. The images are normalized in color space, after which exclusion of secretion areas and feature extraction is performed. Machine learning is utilized to build a model of early PIN lesions for determining the probability for histological changes based on the calculated features. We performed a feature-based analysis to mPIN lesions. First, a quantitative representation of over 100 features was built, including several features representing pathological changes in PIN, especially describing the spatial growth pattern of lesions in the prostate tissue. Furthermore, we built a classification model, which is able to align PIN lesions corresponding to grading by visual inspection to more advanced and mild lesions. The classifier allowed both determining the probability of early histological changes for uncategorized tissue samples and interpretation of the model parameters. Here, we develop quantitative image analysis pipeline to describe morphological changes in histological images. Even subtle changes in mPIN lesion characteristics can be described with feature analysis and machine learning. Constructing and using multidimensional feature data to represent histological changes enables richer analysis and interpretation of early pathological lesions.

Background: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. Results: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.