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.

Neuropathological Applications of Microscopy with Ultraviolet Surface Excitation (MUSE): A Concordance Study of Human Primary and Metastatic Brain Tumors.

Whereas traditional histology and light microscopy require multiple steps of formalin fixation, paraffin embedding, and sectioning to generate images for pathologic diagnosis, Microscopy using Ultraviolet Surface Excitation (MUSE) operates through UV excitation on the cut surface of tissue, generating images of high resolution without the need to fix or section tissue and allowing for potential use for downstream molecular tests. Here, we present the first study of the use and suitability of MUSE microscopy for neuropathological samples. MUSE images were generated from surgical biopsy samples of primary and metastatic brain tumor biopsy samples (n = 27), and blinded assessments of diagnoses, tumor grades, and cellular features were compared to corresponding hematoxylin and eosin (H&E) images. A set of MUSE-treated samples subsequently underwent exome and targeted sequencing, and quality metrics were compared to those from fresh frozen specimens. Diagnostic accuracy was relatively high, and DNA and RNA integrity appeared to be preserved for this cohort. This suggests that MUSE may be a reliable method of generating high-quality diagnostic-grade histologic images for neuropathology on a rapid and sample-sparing basis and for subsequent molecular analysis of DNA and RNA.

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.

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.

Improving quantification of renal fibrosis using Deep-DUET.

Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis. However, trichrome stains are not always reproducible, can underestimate collagen content and are not sensitive to subtle fibrotic patterns. Using the Dual-mode emission and transmission (DUET) microscopy approach, it is possible to capture both brightfield and fluorescence images from the same area of a tissue stained with hematoxylin and eosin (H&E) enabling reproducible extraction of collagen with high sensitivity and specificity. Manual extraction of spectrally overlapping collagen signals from tubular epithelial cells and red blood cells is still an intensive task. We employed a UNet++ architecture for pixel-level segmentation and quantification of collagen using 760 whole slide image (WSI) patches from six cases of varying stages of fibrosis. Our trained model (Deep-DUET) used the supervised extracted collagen mask as ground truth and was able to predict the extent of collagen signal with a MSE of 0.05 in a holdout testing set while achieving an average AUC of 0.94 for predicting regions of collagen deposits. Expanding this work to the level of the WSI can greatly improve the ability of pathologists and machine learning (ML) tools to quantify the extent of renal fibrosis reproducibly and reliably.

N-dihydrogalactochitosan reduces mortality in a lethal mouse model of SARS-CoV-2.

The rapid emergence and global dissemination of SARS-CoV-2 that causes COVID-19 continues to cause an unprecedented global health burden resulting in nearly 7 million deaths. While multiple vaccine countermeasures have been approved for emergency use, additional treatments are still needed due to sluggish vaccine rollout, vaccine hesitancy, and inefficient vaccine-mediated protection. Immunoadjuvant compounds delivered intranasally can guide non-specific innate immune responses during the critical early stages of viral replication, reducing morbidity and mortality. N-dihydrogalactochitosan (GC) is a novel mucoadhesive immunostimulatory polymer of ß-0-4-linked N-acetylglucosamine that is solubilized by the conjugation of galactose glycans with current applications as a cancer immunotherapeutic. We tested GC as a potential countermeasure for COVID-19. GC was well-tolerated and did not produce histopathologic lesions in the mouse lung. GC administered intranasally before and after SARS-CoV-2 exposure diminished morbidity and mortality in humanized ACE2 receptor expressing mice by up to 75% and reduced infectious virus levels in the upper airway. Fluorescent labeling of GC shows that it is confined to the lumen or superficial mucosa of the nasal cavity, without involvement of adjacent or deeper tissues. Our findings demonstrate a new application for soluble immunoadjuvants such as GC for preventing disease associated with SARS-CoV-2 and may be particularly attractive to persons who are needle-averse.

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining.

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

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.

A new approach for microstructure imaging.

A recurring issue with microstructure studies is specimen lighting. In particular, microscope lighting must be deployed in such a way as to highlight biological elements without enhancing caustic effects and diffraction. We describe here a high frequency technique due to address this lighting issue. First, an extensive study is undertaken concerning asymptotic equations in order to identify the most promising algorithm for 3D microstructure analysis. Ultimately, models based on virtual light rays are discarded in favor of a model that considers the joint computation of phase and irradiance. This paper maintains the essential goal of the study concerning biological microstructures but offers several supplementary notes on computational details which provide perspectives on analyses of the arrangements of numerous objects in biological tissues.

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.

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.

Imaging peripheral nerve micro-anatomy with MUSE, 2D and 3D approaches.

Understanding peripheral nerve micro-anatomy can assist in the development of safe and effective neuromodulation devices. However, current approaches for imaging nerve morphology at the fiber level are either cumbersome, require substantial instrumentation, have a limited volume of view, or are limited in resolution/contrast. We present alternative methods based on MUSE (Microscopy with Ultraviolet Surface Excitation) imaging to investigate peripheral nerve morphology, both in 2D and 3D. For 2D imaging, fixed samples are imaged on a conventional MUSE system either label free (via auto-fluorescence) or after staining with fluorescent dyes. This method provides a simple and rapid technique to visualize myelinated nerve fibers at specific locations along the length of the nerve and perform measurements of fiber morphology (e.g., axon diameter and g-ratio). For 3D imaging, a whole-mount staining and MUSE block-face imaging method is developed that can be used to characterize peripheral nerve micro-anatomy and improve the accuracy of computational models in neuromodulation. Images of rat sciatic and human cadaver tibial nerves are presented, illustrating the applicability of the method in different preclinical models.

CoreView: fresh tissue biopsy assessment at the bedside using a millifluidic imaging chip.

Minimally invasive core needle biopsies for medical diagnoses have become increasingly common for many diseases. Although tissue cores can yield more diagnostic information than fine needle biopsies and cytologic evaluations, there is no rapid assessment at the point-of-care for intact tissue cores that is low-cost and non-destructive to the biopsy. We have developed a proof-of-concept 3D printed millifluidic histopathology lab-on-a-chip device to automatically handle, process, and image fresh core needle biopsies. This device, named CoreView, includes modules for biopsy removal from the acquisition tool, transport, staining and rinsing, imaging, segmentation, and multiplexed storage. Reliable removal from side-cutting needles and bidirectional fluid transport of core needle biopsies of five tissue types has been demonstrated with 0.5 mm positioning accuracy. Automation is aided by a MATLAB-based biopsy tracking algorithm that can detect the location of tissue and air bubbles in the channels of the millifluidic chip. With current and emerging optical imaging technologies, CoreView can be used for a rapid adequacy test at the point-of-care for tissue identification as well as glomeruli counting in renal core needle biopsies.

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.

Real-time polarization microscopy of fibrillar collagen in histopathology.

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.

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.