Radiologic-pathologic correlation of lesions in resected liver specimens with an ex vivo MRI-compatible localization device.

OBJECTIVE: Liver lesion characterization is limited by the lack of an established gold standard for precise correlation of radiologic characteristics with their histologic features. The objective of this study was to demonstrate the feasibility of using an ex vivo MRI-compatible sectioning device for radiologic-pathologic co-localization of lesions in resected liver specimens. METHODS: In this prospective feasibility study, adults undergoing curative partial hepatectomy from February 2018 to January 2019 were enrolled. Gadoxetic acid was administered intraoperatively prior to hepatic vascular inflow ligation. Liver specimens were stabilized in an MRI-compatible acrylic lesion localization device (27 × 14 × 14 cm3) featuring slicing channels and a silicone gel 3D matrix. High-resolution 3D T1-weighted fast spoiled gradient echo and 3D T2-weighted fast-spin-echo images were acquired using a single channel quadrature head coil. Radiologic lesion coordinates guided pathologic sectioning. A final histopathologic diagnosis was prepared for all lesions. The proportion of successfully co-localized lesions was determined. RESULTS: A total of 57 lesions were identified radiologically and sectioned in liver specimens from 10 participants with liver metastases (n = 8), primary biliary mucinous cystic neoplasm (n = 1), and hepatic adenomatosis (n = 1). Of these, 38 lesions (67%) were < 1 cm. Overall, 52/57 (91%) of radiologically identified lesions were identified pathologically using the device. Of these, 5 lesions (10%) were not initially identified on gross examination but were confirmed histologically using MRI-guided localization. One lesion was identified grossly but not on MRI. CONCLUSIONS: We successfully demonstrated the feasibility of a clinical method for image-guided co-localization and histological characterization of liver lesions using an ex vivo MRI-compatible sectioning device. KEY POINTS: • The ex vivo MRI-compatible sectioning device provides a reliable method for radiologic-pathologic correlation of small (< 1 cm) liver lesions in human liver specimens. • The sectioning method can be feasibly implemented within a clinical practice setting and used in future efforts to study liver lesion characterization. • Intraoperative administration of gadoxetic acid results in enhancement in ex vivo MRI images of liver specimens hours later with excellent image quality.

Cardiac macrophage density in Covid-19 infection: relationship to myocyte necrosis and acute lung injury.

SARS-Cov-2 infection is not limited to the respiratory tract and can involve other organs including the heart, blood vessels, kidneys, liver, gastrointestinal tract, placenta, and skin. Covid-19 patients with cardiac involvement usually have higher morbidity and mortality compared to those without cardiac involvement. The frequency and the specificity of the myocardial pathological changes in patients who die after documented infection with SARS-Cov-2 is uncertain. Macrophages can be found in the normal heart (interstitium, around the endothelial cells and in the epicardial adipose tissue), and they are considered part of the major immune cell population in the heart. In this case-control autopsy study, we compare the gross and microscopic cardiac findings, and the available clinical characteristics between a group of 10 Covid-19 decedents and a control group of 20 patients who died with non-SARS-Cov-2 severe bronchopneumonia and/or diffuse alveolar damage. The objectives of this semi-quantitative study are to study single myocyte necrosis and its relation to the strain on the heart caused by lung injury as a causative mechanism, and to study the density of myocardial and epicardial macrophages in Covid-19 hearts in comparison to the control group, and in Covid-19 hearts with single myocyte necrosis in comparison to Covid-19 hearts without single myocyte necrosis. Lymphocytic myocarditis was not identified in any of the hearts from the Covid-19 or the control group. Single myocyte necrosis is more frequent in the Covid-19 group compared to the control group, suggesting that it is unrelated to the strain on the heart caused by underlying lung injury. The density of the macrophages in the epicardium and myocardium in the hearts of the Covid-19 group is higher compared to those in the control group. The density of epicardial macrophages is higher in the Covid-19 hearts with single myocyte necrosis than in those without. These observations contribute to our increasing appreciation of the role of macrophages in the pathophysiologic response to infection by SARS-CoV-2.

Appendiceal Goblet Cell Adenocarcinoma: A Historically Informed Reading of 6 Cases.

CONTEXT.­: Appendiceal goblet cell adenocarcinoma is an uncommon appendiceal neoplasm that is typically diagnosed incidentally to a presentation of acute appendicitis. Over the years, there have been many changes to the histologic evaluation and classification of this neoplasm, culminating in the most recent (2019) World Health Organization recommendations. OBJECTIVE.­: To understand the evolution of the concept of "goblet cell" neoplasia in the appendix, we explore a series of cases of goblet cell adenocarcinoma encountered at a county hospital over a 20-year period. We performed a historically informed review of these cases to highlight the significance and blind spots of our current definition and approach to this disease entity. DATA SOURCES.­: The cases were recovered from the archives of a county hospital (1999-2019). The literature was assembled through PubMed searches on the various names given to "goblet cell" neoplasms since the 1970s. We also refer to standard reference books of gastrointestinal pathology. CONCLUSIONS.­: Frequent and numerous changes in the nomenclature of "goblet cell" lesions of the appendix resulted in considerable confusion regarding the approach to this rare entity, especially pertaining to its grade, as well as to its management in the clinical setting. The newest approach, recommended by the World Health Organization, provides clarity to its pathologic and clinical management. Adherence to these recommendations will improve communication between pathologists, surgeons, and oncologists about the natural history and prognosis of this malignant neoplasm.

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.

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.

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer.

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.

High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access to and adoption of this technique. As a potential solution to these challenges, we present a deep learning-based approach making use of single image super-resolution (SISR) to reconstruct high-resolution histology images from low-resolution inputs. Such low-resolution images can easily be shared, require less storage, and can be acquired quickly using widely available low-cost slide scanners. The network consists of multi-scale fully convolutional networks capable of capturing hierarchical features. Conditional generative adversarial loss is incorporated to penalize blurriness in the output images. The network is trained using a progressive strategy where the scaling factor is sampled from a normal distribution with an increasing mean. The results are evaluated with quantitative metrics and are used in a clinical histopathology diagnosis procedure which shows that the SISR framework can be used to reconstruct high-resolution images with clinical level quality. We further propose a self-supervised color normalization method that can remove staining variation artifacts. Quantitative evaluations show that the SISR framework can generalize well on unseen data collected from other patient tissue cohorts by incorporating the color normalization method.

Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis.

The importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.

We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in which SR algorithms are designed, we are given multiple intermediate resolutions of the same image as well. The question remains how to best utilize such data to make the transformation learning problem inherent to SR more tractable and address the unique challenges that arises in this biomedical application. We propose a recurrent convolutional neural network model, to generate SR images from such multi-resolution WSI datasets. Specifically, we show that having such intermediate resolutions is highly effective in making the learning problem easily trainable and address large resolution difference in the low and high-resolution images common in WSI, even without the availability of a large size training data. Experimental results show state-of-the-art performance on three WSI histopathology cancer datasets, across a number of metrics.

A Taxonomic Index for Retrieval of Digitized Whole Slide Images from an Electronic Database for Medical School and Pathology Residency Education.

Since the advent of whole slide imaging, the utility of digitized slides for education in medical school and residency has been amply documented. Pathology departments at most major academic medical centers have made digitized slides available to pathology residents for study, even before the use of digitized slides for clinical purposes (i.e., primary diagnosis) has become commonplace. This article describes the experience of one academic medical center with the storage and indexing of large volumes of digitized slides. Our goal was to be able to retrieve scanned slides for a variety of educational applications and thereby maximize the heuristic value of the slides. This posed a formidable challenge in terms of development and deployment of an index system that would allow exemplary slides to be identified and retrieved irrespective of the purpose for which the slide was scanned. We used the structure inherent in Aperio's image management software (eSlide Manager) to build an educational database that allowed each image to be appended with a unique taxonomic identifier so that the individual files could be retrieved in a flexible and utilitarian manner.

CT detection of primary and metastatic ileal carcinoid tumor: rates of missed findings and associated delay in clinical diagnosis.

PURPOSE: To determine the rate of missed CT findings of ileal carcinoid tumor prior to pathologic diagnosis and the resultant diagnostic delay. METHODS: Initially, 74 patients with abdominal and pelvic CT prior to pathologically-proven diagnosis of ileal carcinoid were identified. Patients were excluded when the original CT study (n = 6) or report (n = 4) was not available, resulting in a final cohort of 64 patients (mean age, 58.3 years; 29 M/35F); 27 (42%) patients had more than one abdominal CT prior to diagnosis. All available CT studies prior to diagnosis were retrospectively reviewed for the presence of the primary ileal tumor and metastatic disease (mesenteric and hepatic). RESULTS: Primary ileal tumors were prospectively missed on at least one CT scan in 64% (32/50) of patients with retrospectively identifiable disease. CT findings of mesenteric spread were missed at least once in 46% (25/54) of cases where present in retrospect. By the final pre-operative CT, hepatic metastases and bowel wall thickening were present in 55% (35/64) and 52% (33/64) of cases, respectively. In patients with missed ileal and/or mesenteric findings resulting in diagnostic delay, mean delay was 40 months (range 4-98 months). CONCLUSION: Initial presentation of ileal carcinoid tumor, even with mesenteric involvement, is often missed prospectively at abdominal CT, leading to delay in diagnosis until bowel or mesenteric findings become more obvious, or hepatic metastatic disease manifests. Radiologists should make a concerted effort to evaluate the bowel and mesentery in patients with long-standing vague abdominal symptoms.

MR visible localization device for radiographic-pathologic correlation of surgical specimens.

PURPOSE: The detection of small parenchymal hepatic lesions identified by preoperative imaging remains a challenge for traditional pathologic methods in large specimens. We developed a magnetic resonance imaging (MRI) compatible localization device for imaging of surgical specimens aimed to improve identification and localization of hepatic lesions ex vivo. MATERIALS AND METHODS: The device consists of two stationary and one removable MR-visible grids lined with silicone gel, creating an orthogonal 3D matrix for lesion localization. To test the device, five specimens of swine liver with a random number of lesions created by microwave ablation were imaged on a 3T MR scanner. Two readers independently evaluated lesion coordinates and size, which were then correlated with sectioning guided by MR imaging. RESULTS: All lesions (n=38) were detected at/very close to the expected localization. Inter-reader agreement of lesion localization was almost perfect (0.92). The lesion size estimated by MRI matched macroscopic lesion size in cut specimen (±2mm) in 34 and 35, respectively, out of 38 lesions. CONCLUSION: Use of this MR compatible device for ex vivo imaging proved feasible for detection and three-dimensional localization of liver lesions, and has potential to play an important role in the ex vivo examination of surgical specimens in which pathologic correlation is clinically important.

Human pancreatic stellate cells modulate 3D collagen alignment to promote the migration of pancreatic ductal adenocarcinoma cells.

A hallmark of pancreatic ductal adenocarcinoma (PDAC) is the ability for cancer cells to aggressively infiltrate and navigate through a dense stroma during the metastatic process. Key features of the PDAC stroma include an abundant population of activated pancreatic stellate cells (PSCs) and highly aligned collagen fibers; however, important questions remain regarding how collagen becomes aligned and what the biological manifestations are. To better understand how PSCs, aligned collagen, and PDAC cells might cooperate during the transition to invasion, we utilized a microchannel-based in vitro tumor model and advanced imaging technologies to recreate and examine in vivo-like heterotypic interactions. We found that PSCs participate in a collaborative process with cancer cells by orchestrating the alignment of collagen fibers that, in turn, are permissive to enhanced cell migration. Additionally, direct contact between PSCs, collagen, and PDAC cells is critical to invasion and co-migration of both cell types. This suggests PSCs may accompany and assist in navigating PDAC cells through the stromal terrain. Together, our data provides a new role for PSCs in stimulating the metastatic process and underscores the importance of collagen alignment in cancer progression.

Highly aligned stromal collagen is a negative prognostic factor following pancreatic ductal adenocarcinoma resection.

Risk factors for pancreatic ductal adenocarcinoma (PDAC) progression after surgery are unclear, and additional prognostic factors are needed to inform treatment regimens and therapeutic targets. PDAC is characterized by advanced sclerosis of the extracellular matrix, and interactions between cancer cells, fibrillar collagen, and other stromal components play an integral role in progression. Changes in stromal collagen alignment have been shown to modulate cancer cell behavior and have important clinical value in other cancer types, but little is known about its role in PDAC and prognostic value. We hypothesized that the alignment of collagen is associated with PDAC patient survival. To address this, pathology-confirmed tissues from 114 PDAC patients that underwent curative-intent surgery were retrospectively imaged with Second Harmonic Generation (SHG) microscopy, quantified with fiber segmentation algorithms, and correlated to patient survival. The same tissue regions were analyzed for epithelial-to-mesenchymal (EMT), α-SMA, and syndecan-1 using complimentary immunohistostaining and visualization techniques. Significant inter-tumoral variation in collagen alignment was found, and notably high collagen alignment was observed in 12% of the patient cohort. Stratification of patients according to collagen alignment revealed that high alignment is an independent negative factor following PDAC resection (p = 0.0153, multivariate). We also found that epithelial expression of EMT and the stromal expression of α-SMA and syndecan-1 were positively correlated with collagen alignment. In summary, stromal collagen alignment may provide additional, clinically-relevant information about PDAC tumors and underscores the importance of stroma-cancer interactions.

Comparison of Picrosirius Red Staining With Second Harmonic Generation Imaging for the Quantification of Clinically Relevant Collagen Fiber Features in Histopathology Samples.

Stromal collagen alignment has been shown to have clinical significance in a variety of cancers and in other diseases accompanied by fibrosis. While much of the biological and clinical importance of collagen changes has been demonstrated using second harmonic generation (SHG) imaging in experimental settings, implementation into routine clinical pathology practice is currently prohibitive. To translate the assessment of collagen organization into routine pathology workflow, a surrogate visualization method needs to be examined. The objective of the present study was to quantitatively compare collagen metrics generated from SHG microscopy and commonly available picrosirius red stain with standard polarization microscopy (PSR-POL). Each technique was quantitatively compared with established image segmentation and fiber tracking algorithms using human pancreatic cancer as a model, which is characterized by a pronounced stroma with reorganized collagen fibers. Importantly, PSR-POL produced similar quantitative trends for most collagen metrics in benign and cancerous tissues as measured by SHG. We found it notable that PSR-POL detects higher fiber counts, alignment, length, straightness, and width compared with SHG imaging but still correlates well with SHG results. PSR-POL may provide sufficient and additional information in a conventional clinical pathology laboratory for certain types of collagen quantification.

The Phenotypic Characterization of the Human Renal Mononuclear Phagocytes Reveal a Co-Ordinated Response to Injury.

Mammalian tissues contain networks of mononuclear phagocytes (MPh) that sense injury and orchestrate the response to it. In mice, this is affected by distinct populations of dendritic cells (DC), monocytes and macrophages and recent studies suggest the same is true for human skin and intestine but little is known about the kidney. Here we describe the analysis of MPh populations in five human kidneys and show they are highly heterogeneous and contain discrete populations of DC, monocytes and macrophages. These include: plasmacytoid DC (CD303+) and both types of conventional DC-cDC1 (CD141+ cells) and CD2 (CD1c+ cells); classical, non-classical and intermediate monocytes; and macrophages including a novel population of CD141+ macrophages clearly distinguishable from cDC1 cells. The relative size of the MPh populations differed between kidneys: the pDC population was bi-modally distributed being less than 2% of DC in two kidneys without severe injury and over 35% in the remaining three with low grade injury in the absence of morphological evidence of inflammation. There were profound differences in the other MPh populations in kidneys with high and low numbers of pDC. Thus, cDC1 cells were abundant (55 and 52.3%) when pDC were sparse and sparse (12.8-12.5%) when pDC were abundant, whereas the proportions of cDC2 cells and classical monocytes increased slightly in pDC high kidneys. We conclude that MPh are highly heterogeneous in human kidneys and that pDC infiltration indicative of low-grade injury does not occur in isolation but is part of a co-ordinated response affecting all renal DC, monocyte and macrophage populations.

Periductal stromal collagen topology of pancreatic ductal adenocarcinoma differs from that of normal and chronic pancreatitis.

Pancreatic ductal adenocarcinoma continues to be one of the most difficult diseases to manage with one of the highest cancer mortality rates. This is due to several factors including nonspecific symptomatology and subsequent diagnosis at an advanced stage, aggressive metastatic behavior that is incompletely understood, and limited response to current therapeutic regimens. As in other cancers, there is great interest in studying the role of the tumor microenvironment in pancreatic ductal adenocarcinoma and whether components of this environment could serve as research and therapeutic targets. In particular, attention has turned toward the desmoplastic collagen-rich pancreatic ductal adenocarcinoma stroma for both biological and clinical insight. In this study, we used quantitative second harmonic generation microscopy to investigate stromal collagen organization and structure in human pancreatic ductal adenocarcinoma pathology tissues compared with non-neoplastic tissues. Collagen topology was characterized in whole-tissue microarray cores and at specific pathology-annotated epithelial-stroma interfaces representing 241 and 117 patients, respectively. We quantitatively demonstrate that a unique collagen topology exists in the periductal pancreatic ductal adenocarcinoma stroma. Specifically, collagen around malignant ducts shows increased alignment, length, and width compared with normal ducts and benign ducts in a chronic pancreatitis background. These findings indicate that second harmonic generation imaging can provide quantitative information about fibrosis that complements traditional histopathologic insights and can serve as a rich field for investigation into pathogenic and clinical implications of reorganized collagen as a pancreatic ductal adenocarcinoma disease marker.

Discordance of Histologic Grade Between Primary and Metastatic Neuroendocrine Carcinomas.

BACKGROUND: The prognosis and management of neuroendocrine carcinoma are largely driven by histologic grade as assessed by mitotic activity. The authors reviewed their institutional experience to determine whether the histologic grade of neuroendocrine carcinoma can differ between primary and metastatic tumors. METHODS: This study examined patients who underwent operative resection of both primary and metastatic foci of neuroendocrine carcinoma. Resected tumors were independently reviewed and categorized as low, intermediate, or high grade as determined by mitotic count. RESULTS: The authors identified 20 patients with metastatic neuroendocrine carcinoma treated at their institution between 1997 and 2013 for whom complete pathologic review of primary and metastatic tumors was possible. Primary lesions were found in the small intestine (n = 12), pancreas (n = 7), ampulla (n = 1), stomach (n = 1), and rectum (n = 1). The timing of hepatic metastasis was synchronous in 15 cases and metachronous in 5 cases. The histologic grade was concordant between primary and metastatic tumors in 9 cases and discordant in 11 cases. Among the discordant cases, 7 had a higher metastatic grade than primary grade, and 4 had a lower metastatic grade than primary grade. Metachronous presentation was associated with a higher likelihood of grade discordance (p = 0.03). The histologic grade of all metachronous metastases differed from that of the primary tumors. CONCLUSION: There is a high prevalence of histologic grade discordance between primary and metastatic foci of neuroendocrine carcinoma, particularly among patients with a metachronous metastatic presentation. Given the importance of histologic grade in disease prognostication and treatment planning, this finding may be informative for the management of patients with metastatic neuroendocrine carcinoma.