Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Current Landscape of Advanced Imaging Tools for Pathology Diagnostics.

Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.

Slide Over: Advances in Slide-Free Optical Microscopy as Drivers of Diagnostic Pathology.

Conventional analysis using clinical histopathology is based on bright-field microscopy of thinly sliced tissue specimens. Although bright-field microscopy is a simple and robust method of examining microscope slides, the preparation of the slides needed is a lengthy and labor-intensive process. Slide-free histopathology, however, uses direct imaging of intact, minimally processed tissue samples using advanced optical-imaging systems, bypassing the extended workflow now required for the preparation of tissue sections. This article explains the technical basis of slide-free microscopy, reviews common slide-free optical microscopy techniques, and discusses the opportunities and challenges involved in clinical implementation.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

AI in Pathology: What could possibly go wrong?

The field of medicine is undergoing rapid digital transformation. Pathologists are now striving to digitize their data, workflows, and interpretations, assisted by the enabling development of whole-slide imaging. Going digital means that the analog process of human diagnosis can be augmented or even replaced by rapidly evolving AI approaches, which are just now entering into clinical practice. But with such progress comes challenges that reflect a variety of stressors, including the impact of unrepresentative training data with accompanying implicit bias, data privacy concerns, and fragility of algorithm performance. Beyond such core digital aspects, considerations arise related to difficulties presented by changing disease presentations, diagnostic approaches, and therapeutic options. While some tools such as data federation can help with broadening data diversity while preserving expertise and local control, they may not be the full answer to some of these issues. The impact of AI in pathology on the field's human practitioners is still very much unknown: installation of unconscious bias and deference to AI guidance need to be understood and addressed. If AI is widely adopted, it may remove many inefficiencies in daily practice and compensate for staff shortages. It may also cause practitioner deskilling, dethrilling, and burnout. We discuss the technological, clinical, legal, and sociological factors that will influence the adoption of AI in pathology, and its eventual impact for good or ill.

Utility of fluorescence imitating brightfield imaging microscopy for the diagnosis of feline chronic enteropathy.

Fluorescence imitating brightfield imaging (FIBI) is a novel microscopy method that allows for real-time, nondestructive, slide-free tissue imaging of fresh, formalin-fixed, or paraffin-embedded tissue. The nondestructive nature of the technology permits tissue preservation for downstream analyses. The objective of this observational study was to assess the utility of FIBI compared with conventional hematoxylin and eosin (H&E)-stained histology slides in feline gastrointestinal histopathology. Formalin-fixed paraffin-embedded full-thickness small intestinal tissue specimens from 50 cases of feline chronic enteropathy were evaluated. The ability of FIBI to evaluate predetermined morphological features (epithelium, villi, crypts, lacteals, fibrosis, submucosa, and muscularis propria) and inflammatory cells was assessed on a 3-point scale (0 = FIBI cannot identify the feature; 1 = FIBI can identify the feature; 2 = FIBI can identify the feature with more certainty than H&E). H&E and FIBI images were also scored according to World Small Animal Veterinary Association (WSAVA) Gastrointestinal Standardization Group guidelines. FIBI identified morphological features with similar or, in some cases, higher confidence compared with H&E images. The identification of inflammatory cells was less consistent. FIBI and H&E images showed an overall poor agreement with regard to the assigned WSAVA scores. While FIBI showed an equal or better ability to identify morphological features in intestinal biopsies, its ability to identify inflammatory cells is currently inferior compared with H&E-based imaging. Future studies on the utility of FIBI as a diagnostic tool for noninflammatory histopathologic lesions are warranted.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study.

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.

Pilot study of fluorescence imitating brightfield imaging for rapid, slide-free dermatopathology.

BACKGROUND: Fluorescence imitating brightfield imaging (FIBI) is a novel alternative microscopy method that can image freshly excised, non-sectioned tissue. We examine its potential utility in dermatopathology by examining readily available specimens embedded in paraffin blocks. METHODS: Nine skin samples embedded in paraffin blocks were superficially deparaffinized using xylene and ethanol and stained with H&E. FIBI captured tissue surface histopathology images using simple microscope optics and a color camera. We then applied deep-learning-based models to improve resemblance to standard H&E coloration and contrast. FIBI images were compared with corresponding standard H&E slides and concordance was assessed by two dermatopathologists who numerically scored epidermal and dermal structure appearance and overall diagnostic utility. RESULTS: Dermatopathologist scores indicate that FIBI images are at least equivalent to standard H&E slides for visualizing structures such as epidermal layers, sweat glands, and nerves. CONCLUSION: Images acquired with FIBI are comparable to traditional H&E-stained slides, suggesting that this rapid, inexpensive, and non-destructive microscopy technique is a conceivable alternative to standard histopathology processes especially for time-sensitive procedures and in settings with limited histopathology resources.

Banff Digital Pathology Working Group: Going digital in transplant pathology.

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

New Technologies to Image Tumors.

The premise of this book is the importance of the tumor microenvironment (TME). Until recently, most research on and clinical attention to cancer biology, diagnosis, and prognosis were focused on the malignant (or premalignant) cellular compartment that could be readily appreciated using standard morphology-based imaging.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Microscopy with ultraviolet surface excitation (MUSE): A novel approach to real-time inexpensive slide-free dermatopathology.

Traditional histology relies on processing and physically sectioning either frozen or formalin-fixed paraffin-embedded (FFPE) tissue into thin slices (typically 4-6 µm) prior to staining and viewing on a standard wide-field microscope. Microscopy using ultraviolet (UV) surface excitation (MUSE) represents a novel alternative microscopy method that works with UV excitation using oblique cis-illumination, which can generate high-quality images from the cut surface of fresh or fixed tissue after brief staining, with no requirement for fixation, embedding and histological sectioning of tissue specimens. We examined its potential utility in dermatopathology. Concordance between MUSE images and hematoxylin and eosin (H&E) slides was assessed by the scoring of MUSE images on their suitability for identifying 10 selected epidermal and dermal structures obtained from minimally fixed tissue, including stratum corneum, stratum granulosum, stratum spinosum, stratum basale, nerve, vasculature, collagen and elastin, sweat glands, adipose tissue and inflammatory cells, as well as 4 cases of basal cell carcinoma and 1 case of pseudoxanthoma elasticum deparaffinized out of histology blocks. Our results indicate that MUSE can identify nearly all normal skin structures seen on routine H&E as well as some histopathologic features, and appears promising as a fast, reliable and cost-effective diagnostic approach in dermatopathology.

Early BAFF receptor blockade mitigates murine Sjögren's syndrome: Concomitant targeting of CXCL13 and the BAFF receptor prevents salivary hypofunction.

Sjögren's syndrome (SS) is a debilitating autoimmune disease. Patients with SS may develop xerostomia. This process is progressive, and there are no therapeutics that target disease etiology. We hypothesized BAFF receptor (BAFFR) blockade would mitigate SS disease development, and neutralization of CXCL13 and BAFF signaling would be more efficacious than BAFFR blockade alone. We treated NOD/ShiLtJ SS mice with soluble BAFF receptor (BAFFR-Fc) or anti-CXCL13/BAFFR-Fc in combination, prior to the development of clinical disease. Our results show treatment with BAFFR-Fc reduced peripheral B cell numbers and decreased sialadenitis. In addition, this treatment reduced total serum immunoglobulin as well as IgG and IgM specific anti-nuclear autoantibodies. NOD/ShiLtJ mice treated with BAFFR-Fc and anti-CXCL13 antibody were protected from salivary deficits. Results from this study suggest blockade of CXCL13 and BAFFR together may be an effective therapeutic strategy in preventing salivary hypofunction and reducing autoantibody titers and sialadenitis in patients with SS.

Real-time, High-resolution, In Vivo Characterization of Superficial Skin With Microscopy Using Ultraviolet Surface Excitation (MUSE).

BACKGROUND: Skin care products make up the largest part (36%) of the cosmetic market globally, of which the United States plays the largest role. In 2015, approximately 115 billion USD was spent globally on skin care products. Skin care products, in contradistinction to pharmaceuticals, are not strictly regulated by the FDA. A key factor for evaluation of a skin care product or topical drug is skin barrier function and effect on super cial skin. Thus, it is critical to have quantitative and qualitative methods to study the effects of skin care products on skin barrier and the super cial skin. Currently, no imaging method exists that can evaluate and track super cial skin changes visually in real-time. OBJECTIVE: To report using a novel imaging modality, Microscopy using Ultraviolet Surface Excitation (MUSE), to provide real-time, high- resolution, in vivo characterization of super cial skin and moisturizing properties of topical moisturizer, and to highlight key bene ts of using MUSE to visualize the super cial skin and serve as an excellent complementary tool to current quantitative methods. METHODS AND MATERIALS: The methodology of MUSE is based upon two main principles inherent to ultraviolet (UV) light and uorescent staining agents. In this study, the author's (JJ) index ngertip was imaged using the MUSE instrument without and with moisturizer. RESULTS: Dermatoglyphics of the fingertip consists of ridges (cristae super ciales) and grooves (sulci super ciales) proved to be straightforward to visualize at high resolution. Desquamation of superficial corneocytes and opening of an acrosyringium (the most superficial portion of eccrine ducts) were visualized in high-resolution. Post-application of a moisturizer, a uniform layer of moisturizer could be seen superficial to the corneocytes along the ridges and CONCLUSIONS: Real-time, high-resolution, in vivo characterization of super cial skin and moisturizing properties of moisturizer using MUSE is feasible. Its utility can be enhanced with downstream quantification using imaging software. J Drugs Dermatol. 2016;15(11):1344-1346..

Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging.

The role of immunohistochemistry (IHC) in the management of cancer has expanded to provide improved diagnostic classification, as well as guidance on disease prognosis, therapy, and relapse. These new tasks require evaluation of an increasing number of protein targets; however, conventional multiplexing, usually achieved using serial tissue sections stained for a single analyte per slide, can exhaust small biopsy specimens, complicate slide-to-slide protein expression correlation, and leave insufficient material for additional molecular assays. A new approach, mass spectrometry immunohistochemistry (MSIHC), compatible with high levels of target multiplexing and suitable for use on formalin-fixed, paraffin-embedded samples can circumvent many of these issues. The strategy employs antibodies that are labeled with elemental mass tags, such as isotopically pure lanthanides not typically found in biological specimens, rather than with typical fluorophores or chromogens. The metal-labeled antibodies are then detected in tissue using lasers or ion beams to liberate the tags for subsequent mass spectrometry detection. Within a given multiplexed IHC panel, the metal labels are selected so that their respective masses do not overlap. More than 30 antibodies have been imaged simultaneously, and up to 100 antibodies could potentially be detected at once if the full available mass spectrum is deployed. MSIHC has a number of advantages over conventional IHC techniques. Background due to autofluorescence is absent and the dynamic range is 10(5), exceeding immunofluorescence and chromogenic IHC by 100-fold and 1000-fold, respectively. Detection of labeled primary antibodies improves assay linearity over both chromogenic and fluorescent IHC. Multiplexed mass-tagged antibodies incubated simultaneously with tissue do not appear to cross-interfere, and because the mass tags do not degrade, samples are stable indefinitely. The imaging resolution of multiplexed ion-beam imaging can be better than light microscopy. With appropriate instrumentation, MSIHC has the potential to transform research and clinical pathology practice.

Post-contrast myocardial T(1) and ECV disagree in a longitudinal canine study.

Both post-contrast myocardial T1 and extracellular volume (ECV) measurements have been associated with diffuse interstitial fibrosis. The cardiovascular magnetic resonance (CMR) field is migrating towards ECV, because it is largely insensitive to confounders that affect post-contrast myocardial T1 . Despite the theoretical advantages of myocardial ECV over post-contrast myocardial T1 , systematic experimental studies comparing the two measurements are largely lacking. We sought to measure the temporal changes in post-contrast myocardial T1 and ECV in an established canine model with chronic atrial fibrillation. Seventeen mongrel dogs, implanted with a pacemaker to induce chronic atrial fibrillation via rapid atrial pacing, were scanned multiple times for a total of 46 CMR scans at 3T. These dogs with different disease durations (0-22 months) were part of a separate longitudinal study aimed at studying the relationship between AF and pathophysiology. In each animal, we measured native and post-contrast T1 values and hematocrit. Temporal changes in post-contrast myocardial T1 and ECV, as well as other CMR parameters, were modeled with linear mixed effect models to account for repeated measurements over disease duration. In 17 animals, post-contrast myocardial T1 decreased significantly from 872 to 698 ms (p < 0.001), which corresponds to a 24.9% relative reduction. In contrast, ECV increased from 21.0 to 22.0% (p = 0.38), which corresponds to only a 4.5% relative increase. To partially investigate this discrepancy, we quantified collagen volume fraction (CVF) in post-mortem heart tissues of six canines sacrificed at different disease durations (0-22 months). CVF quantified by histology increased from 0.9 to 1.9% (p = 0.56), which agrees better with ECV than with post-contrast myocardial T1 . This study shows that post-contrast myocardial T1 and ECV may disagree in a longitudinal canine study. A more comprehensive study, including histologic, cardiac, and renal functional analyses, is warranted to test rigorously which CMR parameter (ECV or post-contrast myocardial T1 ) agrees better with CVF.

NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images.

Significance: Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required. Aim: Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample. Approach: We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack. Results: We found that a normalized Dice overlap ( Dice norm ) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the mean Dice norm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼ 5 mm along the nerve's length. Conclusions: Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.

A Pilot Validation Study Comparing Fluorescence-Imitating Brightfield Imaging, A Slide-Free Imaging Method, With Standard Formalin-Fixed, Paraffin-Embedded Hematoxylin-Eosin-Stained Tissue Section Histology for Primary Surgical Pathology Diagnosis.

CONTEXT.­: Digital pathology using whole slide images has been recently approved to support primary diagnosis in clinical surgical pathology practices. Here we describe a novel imaging method, fluorescence-imitating brightfield imaging, that can capture the surface of fresh tissue without requiring prior fixation, paraffin embedding, tissue sectioning, or staining. OBJECTIVE.­: To compare the ability of pathologists to evaluate direct-to-digital images with standard pathology preparations. DESIGN.­: One hundred surgical pathology samples were obtained. Samples were first digitally imaged, then processed for standard histologic examination on 4-µm hematoxylin-eosin-stained sections and digitally scanned. The resulting digital images from both digital and standard scan sets were viewed by each of 4 reading pathologists. The data set consisted of 100 reference diagnoses and 800 study pathologist reads. Each study read was compared to the reference diagnosis, and also compared to that reader's diagnosis across both modalities. RESULTS.­: The overall agreement rate, across 800 reads, was 97.9%. This consisted of 400 digital reads at 97.0% versus reference and 400 standard reads versus reference at 98.8%. Minor discordances (defined as alternative diagnoses without clinical treatment or outcome implications) were 6.1% overall, 7.2% for digital, and 5.0% for standard. CONCLUSIONS.­: Pathologists can provide accurate diagnoses from fluorescence-imitating brightfield imaging slide-free images. Concordance and discordance rates are similar to published rates for comparisons of whole slide imaging to standard light microscopy of glass slides for primary diagnosis. It may be possible, therefore, to develop a slide-free, nondestructive approach for primary pathology diagnosis.