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

Flowcytometric data of intermediate-large cell gastrointestinal lymphoma presenting a gross mass in 32 cats – “let them glow in the flow”

Gastrointestinal lymphoma is the most common form of lymphoma in domestic cats. Aggressive phenotypes are much less common but do bear and unfavorable prognosis. Immunophenotyping by flow cytometry (FCM) is not systematically performed in these patients, because of difficulties in the acquisition of suitable sample material from the gastrointestinal tract. A multimodal diagnostic approach is recommended to improve identification of subtypes targeting patient tailored therapeutic strategies. The aim of this prospective study was to present results of multicolor FCM immunophenotyping in surgically removed gastrointestinal mass and relate them with histopathology using the World Health Organization (WHO) classification and clonality PCR testing. Thirty-two patients were included. Eight cats (25%) had gastric, 23 (72%) had intestinal lymphoma and 1 (3%) had gastric/jejunal lymphoma. Intestinal lymphoma sites were represented by 18 small intestinal, 4 ileocaecal, 1 large intestinal. All gastric lymphomas were diffuse large B-cell lymphoma (DLBCL). Small intestinal lymphomas were 10 enteropathy associated T-cell lymphoma type I (EATL I), 2 enteropathy associated T-cell lymphoma type II (EATL II), 2 peripheral T-cell lymphoma (PTCL), 3 DLBCL and one DLBCL+EATL II. The most common small intestinal FCM T-cell phenotype was CD3+CD21− CD4−CD8−CD18+ CD5−CD79− in 7/10 EATL I and one EATL II. The most frequent FCM B-cell phenotype was CD3−CD21+ CD4−CD8−CD18+ CD5−CD79+ in 13/17 DLBCL and the DLBCL+EATL II. Clonality PCR results were positive in 87.5% (28/32) of all cases. No cross-lineage rearrangement was observed. IHC and FCM results agreed in 87.5% (28/32) of all cases. When all 3 methods were combined, consistent results were seen in 75% (24/32). This is the first demonstration of a multicolor FCM approach set in context to the gold standard histopathology and clonality testing results

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Isolation and Characterization of Novel Canine Osteosarcoma Cell Lines from Chemotherapy-Naïve Patients

The present study aimed to establish novel canine osteosarcoma cell lines (COS3600, COS3600B, COS4074) and characterize the recently described COS4288 cells. The established D-17 cell line served as a reference. Analyzed cell lines differed notably in their biological characteristics. Calculated doubling times were between 22 h for COS3600B and 426 h for COS4074 cells. COS3600B and COS4288 cells produced visible colonies after anchorage-independent growth in soft agar. COS4288 cells were identified as cells with the highest migratory capacity. All cells displayed the ability to invade through an artificial basement membrane matrix. Immunohistochemical analyses revealed the mesenchymal origin of all COS cell lines as well as positive staining for the osteosarcoma-relevant proteins alkaline phosphatase and karyopherin α2. Expression of p53 was confirmed in all tested cell lines. Gene expression analyses of selected genes linked to cellular immune checkpoints (CD270, CD274, CD276), kinase activity (MET, ERBB2), and metastatic potential (MMP-2, MMP-9) as well as selected long non-coding RNA (MALAT1) and microRNAs (miR-9, miR-34a, miR-93) are provided. All tested cell lines were able to grow as multicellular spheroids. In all spheroids except COS4288, calcium deposition was detected by von Kossa staining. We believe that these new cell lines serve as useful biological models for future studies

Ezrin and moesin expression in canine and feline osteosarcoma

Biological features of canine osteosarcomas (OS) differ markedly from those found in feline and resemble more human osteosarcomas, in particular for their high rate of metastasis and poor prognosis. Ezrin, radixin and moesin are members of the ERM protein family and link the actin cytoskeleton with the cell membrane. Ezrin and moesin have been shown to be of prognostic significance in tumor progression due to their role in the metastatic process. The objective of this study was to analyze ezrin and moesin protein expression in a series of dog (n=16) and cat (n=8) osteosarcoma samples using immunohistochemistry and western blot techniques. We found that cat OS have a higher moesin expression compared to dog OS, however, the active phosphorylated forms of moesin and ezrin Tyr353 were more abundant in the dog samples. A statistically significant difference was found for the low and high immunohistochemical scores of ezrin and pan-phosphoERM proteins between cat and dog. Although phosphoezrin Thr567 was higher in feline OS, the membranous localization in dog OS samples indicates the presence of the biologically active form. Therefore, the observed differences in phosphorylated forms of ezrin and moesin status should be further studied to demonstrate if they are relevant for different biological behavior between dog and cat OS

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing

AbstractHistopathological 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

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

Authentication of primordial characteristics of the CLBL-1 cell line prove the integrity of a canine B-cell lymphoma in a murine in vivo model.

Cell lines are key tools in cancer research allowing the generation of neoplasias in animal models resembling the initial tumours able to mimic the original neoplasias closely in vivo. Canine lymphoma is the major hematopoietic malignancy in dogs and considered as a valuable spontaneous large animal model for human Non-Hodgkin's Lymphoma (NHL). Herein we describe the establishment and characterisation of an in vivo model using the canine B-cell lymphoma cell line CLBL-1 analysing the stability of the induced tumours and the ability to resemble the original material. CLBL-1 was injected into Rag2(-/-)γ(c) (-/-) mice. The generated tumor material was analysed by immunophenotyping and histopathology and used to establish the cell line CLBL-1M. Both cell lines were karyotyped for detection of chromosomal aberrations. Additionally, CLBL-1 was stimulated with IL-2 and DSP30 as described for primary canine B-cell lymphomas and NHL to examine the stimulatory effect on cell proliferation. CLBL-1 in vivo application resulted in lymphoma-like disease and tumor formation. Immunophenotypic analysis of tumorous material showed expression of CD45(+), MHCII(+), CD11a(+) and CD79αcy(+). PARR analysis showed positivity for IgH indicating a monoclonal character. These cytogenetic, molecular, immunophenotypical and histological characterisations of the in vivo model reveal that the induced tumours and thereof generated cell line resemble closely the original material. After DSP30 and IL-2 stimulation, CLBL-1 showed to respond in the same way as primary material. The herein described CLBL-1 in vivo model provides a highly stable tool for B-cell lymphoma research in veterinary and human medicine allowing various further in vivo studies

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area ≥9.0μm2) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area ≥10.9μm2) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair (κ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Isolation and Characterization of Novel Canine Osteosarcoma Cell Lines from Chemotherapy-Naïve Patients

The present study aimed to establish novel canine osteosarcoma cell lines (COS3600, COS3600B, COS4074) and characterize the recently described COS4288 cells. The established D-17 cell line served as a reference. Analyzed cell lines differed notably in their biological characteristics. Calculated doubling times were between 22 h for COS3600B and 426 h for COS4074 cells. COS3600B and COS4288 cells produced visible colonies after anchorage-independent growth in soft agar. COS4288 cells were identified as cells with the highest migratory capacity. All cells displayed the ability to invade through an artificial basement membrane matrix. Immunohistochemical analyses revealed the mesenchymal origin of all COS cell lines as well as positive staining for the osteosarcoma-relevant proteins alkaline phosphatase and karyopherin α2. Expression of p53 was confirmed in all tested cell lines. Gene expression analyses of selected genes linked to cellular immune checkpoints (CD270, CD274, CD276), kinase activity (MET, ERBB2), and metastatic potential (MMP-2, MMP-9) as well as selected long non-coding RNA (MALAT1) and microRNAs (miR-9, miR-34a, miR-93) are provided. All tested cell lines were able to grow as multicellular spheroids. In all spheroids except COS4288, calcium deposition was detected by von Kossa staining. We believe that these new cell lines serve as useful biological models for future studies