Systematic Review of Methods and Prognostic Value of Mitotic Activity. Part 1: Feline Tumors

Increased proliferation is a key driver of tumorigenesis, and quantification of mitotic activity is a standard task for prognostication. The goal of this systematic review is scholarly analysis of all available references on mitotic activity in feline tumors, and to provide an overview of the measuring methods and prognostic value. A systematic literature search in PubMed and Scopus and a manual search in Google Scholar was conducted. All articles on feline tumors that correlated mitotic activity with patient outcome were identified. Data analysis revealed that of the eligible 42 articles, the mitotic count (MC, mitotic figures per tumor area) was evaluated in 39 instances and the mitotic index (MI, mitotic figures per tumor cells) in three instances. The risk of bias was considered high for most studies (26/42, 62%) based on small study populations, insufficient details of the MC/MI methods, and lack of statistical measures for diagnostic accuracy or effect on outcome. The MC/MI methods varied markedly between studies. A significant association of the MC with survival was determined in 21/29 (72%) studies, while one study found an inverse effect. There were three tumor types with at least four studies and a prognostic association was found in 5/6 studies on mast cell tumors, 5/5 on mammary tumors and 3/4 on soft tissue sarcomas. The MI was shown to correlate with survival by two research groups, however a comparison to the MC was not conducted. An updated systematic review will be needed with of new literature for different tumor types

Atypical Mitotic Figures Are Prognostically Meaningful for Canine Cutaneous Mast Cell Tumors

Cell division through mitosis (microscopically visible as mitotic figures, MFs) is a highly regulated process. However, neoplastic cells may exhibit errors in chromosome segregation (microscopically visible as atypical mitotic figures, AMFs) resulting in aberrant chromosome structures. AMFs have been shown to be of prognostic relevance for some neoplasms in humans but not in animals. In this study, the prognostic relevance of AMFs was evaluated for canine cutaneous mast cell tumors (ccMCT). Histological examination was conducted by one pathologist in whole slide images of 96 cases of ccMCT with a known survival time. Tumor-related death occurred in 11/18 high-grade and 2/78 low-grade cases (2011 two-tier system). The area under the curve (AUC) was 0.859 for the AMF count and 0.880 for the AMF to MF ratio with regard to tumor-related mortality. In comparison, the AUC for the mitotic count was 0.885. Based on our data, a prognostically meaningful threshold of ≥3 per 2.37 mm2 for the AMF count (sensitivity: 76.9%, specificity: 98.8%) and >7.5% for the AMF:MF ratio (sensitivity: 76.9%, specificity: 100%) is suggested. While the mitotic count of ≥ 6 resulted in six false positive cases, these could be eliminated when combined with the AMF to MF ratio. In conclusion, the results of this study suggests that AMF enumeration is a prognostically valuable test, particularly due to its high specificity with regard to tumor-related mortality. Additional validation and reproducibility studies are needed to further evaluate AMFs as a prognostic criterion for ccMCT and other tumor types

A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.Comment: 12 pages, 5 figure

Rhabditid Nematode-Associated Ophthalmitis and Meningoencephalomyelitis in Captive Asian Horned Frogs (\u3ci\u3eMegophrys montana\u3c/i\u3e) [Case reports]

Between 2006 and 2008, 4 captive Asian horned frogs (Megophrys montana) were diagnosed with ocular and neurologic disease associated with rhabditid nematodiasis. Mortality, either spontaneous or by humane euthanasia, was high (3/4, 75%). Gross and histologic findings included varying degrees of ulcerative keratitis, histiocytic uveitis and retinitis, meningoencephalomyelitis, and epidermal chromatophore (iridophore) hyperplasia with intralesional nematodes. Entry into the host was presumed to be by direct invasion of the skin and the cornea with migration through the optic nerve to the brain and spinal cord. One frog was diagnosed with rhabditid nematodiasis antemortem, and clinical signs and lesions in the frog did not progress after unilateral enucleation and anthelminthic treatment were completed. Gross and tissue morphology of the nematodes were consistent with the order Rhabditida. DNA was extracted separately from 2 individual nematodes that were isolated from frozen and ethanol-preserved eye and brain tissue. These DNA templates were used for polymerase chain reaction amplification and sequencing of nuclear 28S large subunit (LSD) and internal transcribed spacer (ITS) ribosomal DNA regions. Comparison of the LSD and ITS sequences to those deposited in GenBank revealed an exact match for Caenorhabditis elegans

Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard

The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility

Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.Comment: 6 pages, 2 figures, 2 table

Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs

A comprehensive multi-domain dataset for mitotic figure detection

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species

Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today’s deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types