Mitotic index of invasive breast carcinoma. Achieving clinically meaningful precision and evaluating tertial cutoffs

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 F1 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.

Mitosis domain generalization in histopathology images - The MIDOG challenge

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F₁ score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.

Defining the area of mitoses counting in invasive breast cancer using whole slide image

Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm² area was the most representative regarding saturation and concordance levels. Counting in area <2 mm² resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm² was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.

Assessment of mitotic activity in breast cancer: revisited in the digital pathology era

The assessment of cell proliferation is a key morphological feature for diagnosing various pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic figures in routine histological sections remains the gold-standard method to evaluate the proliferative activity and grading of cancer. Despite the apparent simplicity of such a well-established method, visual assessment of mitotic figures in breast cancer (BC) remains a challenging task with low concordance among pathologists which can lead to under or overestimation of tumour grade and hence affects management. Guideline recommendations for counting mitoses in BC have been published to standardise methodology and improve concordance; however, the results remain less satisfactory. Alternative approaches such as the use of the proliferation marker Ki67 have been recommended but these did not show better performance in terms of concordance or prognostic stratification. The advent of whole slide image technology has brought the issue of mitotic counting in BC into the light again with more challenges to develop objective criteria for identifying and scoring mitotic figures in digitalised images. Using reliable and reproducible morphological criteria can provide the highest degree of concordance among pathologists and could even benefit the further application of artificial intelligence (AI) in breast pathology, and this relies mainly on the explicit description of these figures. In this review, we highlight the morphology of mitotic figures and their mimickers, address the current caveats in counting mitoses in breast pathology and describe how to strictly apply the morphological criteria for accurate and reliable histological grade and AI models.

International Guidelines for Veterinary Tumor Pathology: A Call to Action

Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as "living documents" on a website (www.vetcancerprotocols.org), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.

Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images

CONTEXT.—

Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the "hot spot." There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma.

OBJECTIVE.—

To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness.

DESIGN.—

Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases.

RESULTS.—

Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides.

CONCLUSIONS.—

Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.

Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors

CONTEXT.—

Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification.

OBJECTIVE.—

To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis.

DESIGN.—

Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted.

RESULTS.—

Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size.

CONCLUSIONS.—

Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.

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.

Measurement(s)	Mitotic Figure • Slide Image • non-mitotic structures • anatomical phenotype annotation
Technology Type(s)	Pathology Report • hematoxylin and eosin stain • machine learning
Factor Type(s)	breast cancer tissue
Sample Characteristic - Organism	Canis

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13182857

The Evolution of Our Understanding of the Biology of Cancer Is the Key to Avoiding Overdiagnosis and Overtreatment

There has been a tremendous evolution in our thinking about cancer since the 1880s. Breast cancer is a particularly good example to evaluate the progress that has been made and the new challenges that have arisen due to screening that inadvertently identifies indolent lesions. The degree to which overdiagnosis is a problem depends on the reservoir of indolent disease, the disease heterogeneity, and the fraction of the tumors that have aggressive biology. Cancers span the spectrum of biological behavior, and population-wide screening increases the detection of tumors that may not cause harm within the patient's lifetime or may never metastasize or result in death. Our approach to early detection will be vastly improved if we understand, address, and adjust to tumor heterogeneity. In this article, we use breast cancer as a case study to demonstrate how the approach to biological characterization, diagnostics, and therapeutics can inform our approach to screening, early detection, and prevention. Overdiagnosis can be mitigated by developing diagnostics to identify indolent disease, incorporating biology and risk assessment in screening strategies, changing the pathology rules for tumor classification, and refining the way we classify precancerous lesions. The more the patterns of cancers can be seen across other cancers, the more it is clear that our approach should transcend organ of origin. This will be particularly helpful in advancing the field by changing both our terminology for what is cancer and also by helping us to learn how best to mitigate the risk of the most aggressive cancers.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.

A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor

We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.

Mitotic counts in breast cancer should be standardized with a uniform sample area

Background

Mitotic rate is routinely assessed in breast cancer cases and based on the assessment of 10 high power fields (HPF), a non-standard sample area, as per the College of American Pathologists cancer checklist. The effect of sample area variation has not been assessed.

Methods

A computer model making use of the binomial distribution was developed to calculate the misclassification rate in 1,000,000 simulated breast specimens using the extremes of field diameter (FD) and mitotic density cutoffs (3 and 8 mitoses/mm²), and for a sample area of 5 mm². Mitotic counts were assumed to be a random sampling problem using a mitotic rate distribution derived from an experimental study (range 0–16.4 mitoses/mm²). The cellular density was 2500 cell/mm².

Results

For the smallest microscopes (FD = 0.40 mm, area 1.26 mm²) 16% of cases were misclassified, compared to 9% of the largest (FD 0.69 mm, area 3.74 mm²), versus 8% for 5 mm². An excess of 27% of score 2 cases were misclassified as 1 or 3 for the lower FD.

Conclusion

Mitotic scores based on ten HPFs of a small field area microscope are less reliable measures of the mitotic density than in a bigger field area microscope; therefore, the sample area should be standardized. When mitotic counts are close to the cut-offs the score is less reproducible. These cases could benefit from using larger sample areas. A measure of mitotic density variation due to sampling may assist in the interpretation of the mitotic score.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-016-0301-z) contains supplementary material, which is available to authorized users.

Regulation of mammalian nucleotide metabolism and biosynthesis

Nucleotides are required for a wide variety of biological processes and are constantly synthesized de novo in all cells. When cells proliferate, increased nucleotide synthesis is necessary for DNA replication and for RNA production to support protein synthesis at different stages of the cell cycle, during which these events are regulated at multiple levels. Therefore the synthesis of the precursor nucleotides is also strongly regulated at multiple levels. Nucleotide synthesis is an energy intensive process that uses multiple metabolic pathways across different cell compartments and several sources of carbon and nitrogen. The processes are regulated at the transcription level by a set of master transcription factors but also at the enzyme level by allosteric regulation and feedback inhibition. Here we review the cellular demands of nucleotide biosynthesis, their metabolic pathways and mechanisms of regulation during the cell cycle. The use of stable isotope tracers for delineating the biosynthetic routes of the multiple intersecting pathways and how these are quantitatively controlled under different conditions is also highlighted. Moreover, the importance of nucleotide synthesis for cell viability is discussed and how this may lead to potential new approaches to drug development in diseases such as cancer.

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry

BACKGROUND

In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated.

MATERIALS AND METHODS

A digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides.

RESULTS

The specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes.

CONCLUSIONS

Digital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.