9 research outputs found
Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification
Sclerosing encapsulating peritonitis in cats: a two-case report and literature review
Case series summary Two castrated male domestic shorthair cats (aged 8 months [case 1] and 13 years [case 2]) were presented at the Small Animal Clinic of the Veterinary Medicine University of Vienna, Austria, both with acute vomiting and distended abdomen, as well as a history of chronic apathy, recurrent vomiting and diarrhoea. Both cats underwent invasive diagnostic procedures approximately 1 month before the diagnosis of sclerosing encapsulating peritonitis (SEP), namely an exploratory laparotomy and a bronchoscopy, respectively. Abdominal ultrasound revealed severely corrugated intestinal loops and, in case 2, the presence of peritoneal effusion. A thick and diffuse fibrous capsule around the intestine was detected and removed surgically, and biopsies were taken from the affected organs confirming the SEP. Case 1 recovered well, was discharged some days after surgery and was clinically unremarkable for the next 2 years. Case 2 showed unsatisfactory improvement directly after surgery and was euthanased a few days later, as the owner declined any further therapy. Relevance and novel information SEP is a very rare condition of unclear origins in cats. Here we describe the clinical and diagnostic imaging features, surgical treatment, and outcome of SEP in two cats. The results indicate that prompt diagnosis and appropriate interventions may improve the outcome
Validation of digital microscopy: Review of validation methods and sources of bias
Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias
Ex Vivo Generated Natural Killer Cells Acquire Typical Natural Killer Receptors and Display a Cytotoxic Gene Expression Profile Similar to Peripheral Blood Natural Killer Cells
FOXF1 Mediates Endothelial Progenitor Functions and Regulates Vascular Sprouting
Endothelial colony forming cells (ECFC) or late blood outgrowth endothelial cells (BOEC) have been proposed to contribute to neovascularization in humans. Exploring genes characteristic for the progenitor status of ECFC we have identified the forkhead box transcription factor FOXF1 to be selectively expressed in ECFC compared to mature endothelial cells isolated from the vessel wall. Analyzing the role of FOXF1 by gain- and loss-of-function studies we detected a strong impact of FOXF1 expression on the particularly high sprouting capabilities of endothelial progenitors. This apparently relates to the regulation of expression of several surface receptors. First, FOXF1 overexpression specifically induces the expression of Notch2 receptors and induces sprouting. Vice versa, knock-down of FOXF1 and Notch2 reduces sprouting. In addition, FOXF1 augments the expression of VEGF receptor-2 and of the arterial marker ephrin B2, whereas it downmodulates the venous marker EphB4. In line with these findings on human endothelial progenitors, we further show that knockdown of FOXF1 in the zebrafish model alters, during embryonic development, the regular formation of vasculature by sprouting. Hence, these findings support a crucial role of FOXF1 for endothelial progenitors and connected vascular sprouting as it may be relevant for tissue neovascularization. It further implicates Notch2, VEGF receptor-2, and ephrin B2 as downstream mediators of FOXF1 functions.(VLID)470090
Hypoxia- and radiation-inducible, breast cell-specific targeting of retroviral vectors
To facilitate a more efficient radiation and chemotherapy of mammary tumours, synthetic enhancer elements responsive to hypoxia and ionizing radiation were coupled to the mammary-specific minimal promoter of the murine whey acidic protein (WAP) encoding gene. The modified WAP promoter was introduced into a retroviral promoter conversion (ProCon) vector. Expression of a transduced reporter gene in response to hypoxia and radiation was analysed in stably infected mammary cancer cell lines and an up to 9-fold increase in gene expression demonstrated in comparison to the respective basic vector. Expression analyses in vitro, moreover, demonstrated a widely preserved mammary cell-specific promoter activity. For in vivo analyses, xenograft tumours consisting of infected human mammary adenocarcinoma cells were established in SCID/beige mice. Immunohistochemical analyses demonstrated a hypoxia-specific, markedly increased WAP promoter-driven expression in these tumours. Thus, this retroviral vector will facilitate a targeted gene therapeutic approach exploiting the unique environmental condition in solid tumours
Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users\u27 trust in computer-assisted image classification
The VEGF-regulated transcription factor HLX controls the expression of guidance cues and negatively regulates sprouting of endothelial cells
Interferon γ–Induced Human Guanylate Binding Protein 1 Inhibits Mammary Tumor Growth in Mice
Interferon γ (IFN-γ) has recently been implicated in cancer immunosurveillance. Among the most abundant proteins induced by IFN-γ are guanylate binding proteins (GBPs), which belong to the superfamily of large GTPases and are widely expressed in various species. Here, we investigated whether the well-known human GBP-1 (hGBP-1), which has been shown to exert antiangiogenic activities and was described as a prognostic marker in colorectal carcinomas, may contribute to an IFN-γ–mediated tumor defense. To this end, an IFN-independent, inducible hGBP-1 expression system was established in murine mammary carcinoma (TS/A) cells, which were then transplanted into syngeneic immune-competent Balb/c mice. Animals carrying TS/A cells that had been given doxycycline for induction of hGBP-1 expression revealed a significantly reduced tumor growth compared with mock-treated mice. Immunohistochemical analysis of the respective tumors demonstrated a tightly regulated, high-level expression of hGBP-1. No signs of an enhanced immunosurveillance were observed by investigating the number of infiltrating B and T cells. However, hemoglobin levels as well as the number of proliferating tumor cells were shown to be significantly reduced in hGBP-1–expressing tumors. This finding corresponded to reduced amounts of vascular endothelial growth factor A (VEGF-A) released by hGBP-1–expressing TS/A cells in vitro and reduced VEGF-A protein levels in the corresponding mammary tumors in vivo. The results suggest that hGBP-1 may contribute to IFN-γ–mediated antitumorigenic activities by inhibiting paracrine effects of tumor cells on angiogenesis. Consequently, owing to these activities GBPs might be considered as potent members in an innate, IFN-γ–induced antitumoral defense system