Mitotic Figure Count Research Articles

Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.

High Grade Differentiated Follicular Cell-Derived Thyroid Carcinoma Versus Poorly Differentiated Thyroid Carcinoma: A Clinicopathologic Analysis of 41 Cases.

Criteria overlap for separating between malignant follicular epithelial cell-derived thyroid gland neoplasms with high grade features of increased mitoses and tumor necrosis but lacking anaplastic histology. Patterns of growth, nuclear features, tumor necrosis, and various mitotic index cutoffs are suggested, but a reproducible Ki-67-based labeling index has not been established. Forty-one cases diagnosed as poorly differentiated thyroid carcinoma (PDTC) or high grade differentiated follicular cell-derived thyroid carcinoma (HGDFCDTC) were reviewed, with histologic features, mitotic figure counts, and Ki-67 labeling index reviewed on cases within Southern California Permanente Medical Group from 2010 to 2021 to establish any potential outcome differences. There were 17 HGDFCDTC (nine papillary thyroid carcinoma; eight oncocytic follicular thyroid carcinoma), median age 64years, affecting nine females and eight males. Tumors were large (median, 6.0cm), usually unifocal (n = 13), with only one tumor lacking invasion. Tumor necrosis was present in all; median mitotic count was 5/2 mm2 (median Ki-67 labeling index 8.3%). Three patients had metastatic disease at presentation, with additional metastases in four patients (41.2% developed metastases); 11 were without evidence of disease (median 21.2months); with the remaining six patients alive (n = 4) or dead (n = 2) with metastatic disease (median 25.8months). Criteria associated with an increased risk of developing metastatic disease: widely invasive tumors; age ≥ 55years; male; advanced tumor size and stage; extrathyroidal extension; but not increased mitotic rate or higher labeling index. There were 24 PDTC, median age 57.5years, affecting 13 females and 11 males. Tumors were large (median, 6.9cm), with 50% part of multifocal disease, with three tumors lacking invasion. Insular/trabecular/solid architecture was seen in all tumors; tumor necrosis was present in 23; and median mitotic count was 6/2 mm2 (median Ki-67 labeling index 6.9%). Five patients had metastatic disease at presentation, with additional metastases in 3 patients (29.2% developed metastases); 16 were without evidence of disease (median, 48.1months); with the remaining 8 patients alive (n = 3) or dead (n = 5) with metastatic disease (median, 22.4months). Criteria associated with an increased risk of developing metastatic disease: widely invasive tumors; male; advanced tumor size and stage; extrathyroidal extension; but not increased mitotic rate or higher labeling index. HGDFCDTC shows tumor necrosis, a median Ki-67 labeling index of 8.3%, with a high percentage (41%) of patients developing metastatic disease. Extent of invasion (non-invasive, minimally invasive, angioinvasive, widely invasive) correlates strongly with developing metastatic disease. PDTC presents at a slightly younger age, with large tumors, often in a background of multifocal tumors, with tumor necrosis nearly always seen, a median Ki-67 labeling index of 6.9%, with 29% of patients developing metastatic disease. Separation between groups is meaningful as early metastatic disease is relatively common, but mitotic counts/labeling indices are not different between the groups nor able to potentially risk stratify development of metastatic disease.

Quantitative Nuclear Grading: An Objective, Artificial Intelligence–Facilitated Foundation for Grading Noninvasive Papillary Urothelial Carcinoma

In nonmuscle invasive bladder cancer, grade drives important treatment and management decisions. However, grading is complex and qualitative, and it has considerable interobserver and intraobserver variability. Previous literature showed that nuclear features quantitatively differ between bladder cancer grades, but these studies were limited in size and scope. In this study, we aimed to measure morphometric features relevant to grading criteria and build simplified classification models that objectively distinguish between the grades of noninvasive papillary urothelial carcinoma (NPUC). We analyzed 516 low-grade and 125 high-grade 1.0-mm diameter image samples from a cohort of 371 NPUC cases. All images underwent World Health Organization/International Society of Urological Pathology 2004 consensus pathologist grading at our institution that was subsequently validated by expert genitourinary pathologists from 2 additional institutions. Automated software segmented the tissue regions and measured the nuclear features of size, shape, and mitotic rate for millions of nuclei. Then, we analyzed differences between grades and constructed classification models, which had accuracies up to 88% and areas under the curve as high as 0.94. Variation in the nuclear area was the best univariate discriminator and was prioritized, along with the mitotic index, in the top-performing classifiers. Adding shape-related variables improved accuracy further. These findings indicate that nuclear morphometry and automated mitotic figure counts can be used to objectively differentiate between grades of NPUC. Future efforts will adapt the workflow to whole slides and tune grading thresholds to best reflect time to recurrence and progression. Defining these essential quantitative elements of grading has the potential to revolutionize pathologic assessment and provide a starting point from which to improve the prognostic utility of grade.

Practice patterns for reporting digestive system neuroendocrine neoplasms: results from a large, comprehensive international survey.

Criteria for the interpretation of digestive system neuroendocrine neoplasms (NENs) continue to evolve. Although there are some literature recommendations regarding workup and diagnosis of these lesions, different practice patterns exist among pathologists when signing out these specimens. The aim of this study was to assess practice trends among pathologists worldwide when reporting these neoplasms. We created an online survey with multiple questions pertaining to digestive NENs. The results were analysed based on type of practice setting, years of sign-out experience, and practice location. Respondents included 384 practicing pathologists: 70% academic, 30% private practice; 63% gastrointestinal (GI) pathology-subspecialised, 37% not; 39% North American, 42% European, 19% others; 45% with ≤10 years in practice; 55% with >10 years. Some question responses were chosen by the majority (e.g. 85% use both mitotic count and Ki67 index for grading NENs, 82% complete a synoptic, and Ki67 stain even for small incidental appendiceal neuroendocrine tumours [NETs], and 96% utilize the diagnosis of grade 3 NET). However, some questions showed varying responses, including counting mitotic figures, Ki67 stain interpretation, and pancreatic grade 3 NEN workup. Pathologists also had some variability in interpreting regional metastatic foci of small bowel NETs and in choosing blocks for Ki67 staining in multifocal lesions. There existed scenarios wherein practice patterns varied despite recommendations in the literature, and there were also scenarios lacking clear guidelines wherein pathologists used varying judgement. This survey highlights current key grey areas in digestive system NEN evaluation, leading to variation in practice patterns.

Artificial intelligence in breast cancer histopathology.

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images

The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most commonly used method in predicting tumor proliferation speed is the counting of mitotic figures in Hematoxylin and Eosin (H&E) histological slides. Manual mitosis counting is known to suffer from reproducibility problems. This paper presents a fully automated system for tumor proliferation prediction from whole slide images via mitosis counting. First, by considering the epithelial tissue as mitosis activity regions, we build a deep-learning-based region of interest detection method to select the high mitosis activity regions from whole slide images. Second, we learned a set of deep neural networks to detect mitosis detection from selected areas. The proposed mitosis detection system is designed to effectively overcome the mitosis detection challenges by two novel deep preprocessing and two-step hard negative mining approaches. Third, we trained a Support Vector Machine (SVM) classifier to predict the final tumor proliferation score. The proposed method was evaluated on the dataset of the Tumor Proliferation Assessment Challenge (TUPAC16) and achieved a 73.81 % F-measure and 0.612 weighted kappa score, respectively, outperforming all previous approaches significantly. Experimental results demonstrate that the proposed system considerably improves the tumor proliferation prediction accuracy and provides a reliable automated tool to support health care make-decisions.

The Relationship Between Epidermal Mitotic Density, Atypical Mitotic FigureDensity, Breslow Depth, Ulceration, and Dermal Mitotic Rate in Cutaneous Melanoma: A Retrospective Cohort Study.

Prognostic factors for melanoma include Breslow depth (BD), ulceration, and dermal mitotic rate (DMR). No studies have queried the effect of epidermal mitotic density (EMD) or atypical mitotic figure density (AMD) in an outcome-based assessment. Our objective was to determine if there is a relationship between EMD, AMD, BD, DMR, and ulceration and patient outcomes. This was a retrospective cohort study of 185 cases of thick and thin melanomas. Univariate and multivariate cause-specific regression analysis was performed. There was a positive correlation between EMD and BD (P = .0001). The difference between AMD in thick and thin melanomas was statistically significant. For every unit increase in EMD, patients had a 2.8-fold increase in the risk of distant metastasis; however, statistical significance was lost in the multivariate analysis. In adjusted analyses, ulceration, DMR, and BD were associated with outcomes. There were no statistically significant correlations between AMD and outcomes. This study is limited by its small sample size, diminution of the epidermis in some thick melanomas preventing EMD estimates, and reproducibility of mitotic figure counting. EMD and AMD do not seem to have any independent value in multivariate analyses for melanoma. Ulceration, BD, and DMR were significantly associated with outcomes and further solidify these known predictors of prognosis.

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.

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

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. 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. 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. 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. 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.

Mitotic Figures-Normal, Atypical, and Imposters: A Guide to Identification.

Counting mitotic figures (MF) in hematoxylin and eosin-stained histologic sections is an integral part of the diagnostic pathologist's tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm2). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Mitotic figure count acts as a proliferative marker to measure aggressiveness of the breast cancer tumor. In this article, we have proposed a novel framework named SmallMitosis to detect mitotic cells particularly very small size mitosis from hematoxylin and eosin (H&E) stained breast histology images. SmallMitosis framework consists of an atrous fully convolution based segmentation (A-FCN) model and a deep multiscale (MS-RCNN) detector. In intended A-FCN model, the concept of atrous convolution helps to estimate mitosis mask and bounding box annotations of very small size mitotic cells. Meanwhile, the architecture of MS-RCNN internally lifts poor representations of small mitosis to “super-resolved” ones, that are similar to real large mitosis thus more discriminative for detection of small size blurred mitotic cells, as well as a fully convolution layer at detection stage, decreases computational cost. The A-FCN model trained on fully labeled mitosis datasets (all pixels of mitosis are labeled) is applied on weakly labeled datasets (only centroid pixel is labeled) to obtain mitosis mask and bounding box annotations. Using these estimated bounding box annotations, MS-RCNN detector is trained to detect small size mitosis from weakly labeled datasets. The performance of the proposed scheme is tested on three publicly available mitosis datasets, namely ICPR 2012, ICPR 2014, and AMIDA13. On challenging ICPR 2012 dataset, we obtained F score of 0.902, outperforming all prior detection systems significantly. On ICPR 2014 and AMIDA13 datasets, we achieved a 0.495 and 0.644 F score respectively. The results demonstrated that our method impressively outperforms state-of-the-art approaches. SmallMitosis is available at https://github.com/tasleem-hello/SmallMitosis.

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.

Proliferation does not contribute to murine models of renin cell recruitment

AimRenin cells are essential for regulation of blood pressure and fluid‐electrolyte homeostasis. During homeostatic threat, the number of renin cells in the kidney increases, a process termed as recruitment. It has been proposed that recruitment occurs by proliferation, yet no systematic studies have been performed. We sought to determine the extent to which proliferation contributes to the recruitment process.MethodsMice were subjected to recruitment before analysing the renin cells’ cell cycle. For acute threats, we subjected SV129 and C57Bl6 mice to a low sodium diet plus captopril. Tissue sections from treated mice were co‐stained for proliferation markers (Ki67, PCNA, pH3 and BrdU) and renin. Chronic recruitment was studied in deletion models of aldosterone synthase and angiotensinogen through co‐immunostaining and counting mitotic figures in periodic acid‐Schiff‐stained sections. Finally, RNA‐seq of renin cells isolated from recruited mice was performed to study mitotic signature.ResultsMice subjected to low salt and captopril displayed increases in renin cell number (312 ± 40 in controls to 692 ± 85 in recruited animals, P<.0001), 10‐fold increases in renin mRNA and fourfold increases in circulating renin. Co‐staining these kidney sections for proliferation markers revealed negligible proliferation of renin cells (<2%), indistinguishable from control animals. Similarly, chronic models of recruitment—aldosterone synthase KO and angiotensinogen KO—had negligible proliferation. Additionally, the transcriptome of recruited renin cells revealed overall downregulation of mitotic pathways when compared to proliferative cell lines.ConclusionAcute and chronic physiological threats to homeostasis produced a distinct increase in renin‐synthesizing cells, but we found no evidence to suggest the involvement of proliferation.

Effectiveness of crystal violet stain for localization of mitotic activity in oral squamous cell carcinoma

Background: Mitotic figure counting is simplest and oldest method for determining proliferative activity of cell. It is considered as one of the important diagnostic aid in cancer pathology. Though advanced methods to evaluate dysplastic features are more precise and definite but expense and time makes them less practicable for routine use. Therefore an effort was made to use economical as well as simple approach involving crystal violet stain (1%) to study the mitotic figures in oral squamous cell carcinoma.&#x0D; Materials and Methods: This descriptive research included samples, consisting of thirty three cases of the oral squamous cell carcinoma (OSCC). Representative sections were stained with H&amp;E stain and 1% crystal violet stain respectively. The stained sections were viewed under optical microscope to count mitotic figures for evaluating the effectiveness of 1% crystal violet stain. Data obtained was statistically analyzed by using sample t-test.&#x0D; Results: There was noteworthy increase in the mean mitotic count among the crystal violet stained sections of OSCC in contrast to the OSCC sections stained with H&amp;E (P = 0.00).&#x0D; Conclusion:1% Crystal violet stain can be considered as one of the optimum stains to observe the mitotic figure. Practice of staining with 1% crystal violet during routine histopathological procedures will be cost effective and may be used as a selective stain.

Deep learning assisted mitotic counting for breast cancer

As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, the present study compares visual mitotic counting with deep learning based automated mitotic counting and fully automated hotspot selection. Two cohorts were used in this study. Cohort A comprised 90 prospectively included tumors which were selected based on the mitotic frequency scores given during routine glass slide diagnostics. This pathologist additionally assessed the mitotic count in these tumors in whole slide images (WSI) within a preselected hotspot. A second observer performed the same procedures on this cohort. The preselected hotspot was generated by a convolutional neural network (CNN) trained to detect all mitotic figures in digitized hematoxylin and eosin (H&E) sections. The second cohort comprised a multicenter, retrospective TNBC cohort (n = 298), of which the mitotic count was assessed by three independent observers on glass slides. The same CNN was applied on this cohort and the absolute number of mitotic figures in the hotspot was compared to the averaged mitotic count of the observers. Baseline interobserver agreement for glass slide assessment in cohort A was good (kappa 0.689; 95% CI 0.580–0.799). Using the CNN generated hotspot in WSI, the agreement score increased to 0.814 (95% CI 0.719–0.909). Automated counting by the CNN in comparison with observers counting in the predefined hotspot region yielded an average kappa of 0.724. We conclude that manual mitotic counting is not affected by assessment modality (glass slides, WSI) and that counting mitotic figures in WSI is feasible. Using a predefined hotspot area considerably improves reproducibility. Also, fully automated assessment of mitotic score appears to be feasible without introducing additional bias or variability.

Efficiency of Crystal Violet Stain to Study Mitotic Figures in Oral Epithelial Dysplasia.

AIM:To evaluate mitotic activity in the different grades of oral epithelial dysplasia using 1% crystal violet stain.MATERIAL AND METHODS:A descriptive study was conducted in the Department of Histopathology of the Post Graduate Medical Institute, Lahore on a total of thirty-three cases of the Oral Epithelial Dysplasia (OED). Fresh, frozen paraffin-embedded archival tissue blocks were collected from Lahore General Hospital, Lahore & Oral & Maxillofacial Surgery Department of Nawaz Sharif Hospital, Yakki Gate, Lahore. The representative sections were taken and, after processing, mounted on glass slides and stained with H&E and crystal violet stains. The stained slides were then examined under an optical microscope. The efficacy of 1% crystal violet stain to identify mitotic figures in the different grades of oral epithelial dysplasia was assessed with the sample t-test. A difference of p < 0.05 was considered to be significant.RESULTS:A comparison of the mitotic figure count in two categories in sections stained with both stains showed a statistically significant difference. An increase in the mean mitotic count was noted in the sections of OED stained with crystal violet in comparison to the sections of OED stained with H&E which was statistically significant (p = 0.00).CONCLUSION:Counting of mitotic cell is the rapid and simplest way of evaluating the proliferative activity of cells. Crystal violet stain can be a rationalised step in the staining of mitotic figures compared to the usual H&E staining and can be employed as a selective stain during routine histopathological procedures.

Expression and clinical significance of SSH1 in gastrointestinal stromal tumors

Objective: To investigate the correlations between the expression of the slingshot-1(SSH1) and the general clinicopathological factors in gastrointestinal stromal tumor (GIST) patients. Methods: Immunohistochemical staining was used to detect the expression of SSH1 protein in 96 gastrointestinal stromal tumors, and the correlations between expression level of SSH1 and general clinicopathological factors were analyzed. Results: Totally, there were 96 cases of GIST patients (49 males and 47 males), with a mean age of (55.80±12.33) years. The tumors were located in different organs: 62 cases (64.6%) in the stomach, 29 cases (30.2%) in the intestine, and 5 cases (5.2%) in other organs, with a mean tumor size of (5.11±3.73) cm. The low risk group contained 57 cases (59.4%), 18 cases (18.8%) in middle risk group and 21 cases (21.9%) in high risk group. The pathological examination showed that the positive expression rate of CD117, CD34, DOG-1, Ki-67, SMA, S-100 and Desmin was 85.4%, 84.4%, 86.5%, 90.6%, 10.4%, 3.1% and 2.1%, respectively. In 96 cases of GIST, 37 cases (38.5%) had high expression of SSH1, and SSH1 expression level was correlated with the tumor invasion depth, risk classification, tumor mitotic figure count, CD34(+) (P<0.05). Conclusions: SSH1 plays an important role in the development of GIST. High expression of SSH1 is closely related to the invasion depth, risk classification and tumor mitosis of GIST.

The utility of phosphohistone H3 in early prediction of benign and borderline phyllodes tumor recurrence

Objective Phyllodes tumors (PTs) have an increased potential for local recurrence even if histologically benign, and some may follow a malignant course. PTs are often treated with simple surgical excision without margins (enucleation), rendering them more liable for local recurrence. Therefore, the analysis of tumor recurrence according to histologic grade would be especially useful in clinical practice. Grading of PTs is based on several histological parameters, but mitotic count (MC) is the only one that can be quantified, unlike most other histological criteria. Manual counting of mitotic figures is time consuming, and there are some difficulties in identifying mitotic figures. The aim of this study was to assess the reliability of phosphohistone H3 (PhH3) in detecting and counting mitotic figures instead of manual MC in benign and borderline PTs and correlation with manual MC and Ki67 in prediction of tumor recurrence.Materials and methods In all, 92 cases of PTs were selected from Tanta Pathology Department Database Section, Tanta University, Egypt, within the period from March 2014 to March 2019 after regrading the tumors based on counting mitotic figures manually at the highest stromal cellular area at 10 adjacent high-power fields. Benign and borderline PTs with free or close margins (<1 cm) were included, whereas malignant cases and cases with infiltrative margins were excluded from this study. Afterward, the selected cases were immunostained for both PhH3 and Ki67 for further correlation with manual MC.Results Of the studied 92 cases, 17 (18.5%) cases had local recurrence after resection of the primary tumor. On correlation of clinicopathological and histological features, Ki67 labeling index (LI), PhH3 MC, and manual MC with recurrence, it was found that there is a significant positive correlation of tumor size, tumor margin, and PhH3 MC with recurrence (P=0.019, 0.000, and 0.000, respectively). Manual MC and Ki67 LI showed a positive correlation with recurrence in studied cases but was statistically insignificant. Moreover, a significant positive correlation was found between both manual MC and Ki67 LI and PhH3 MC (r=0.884, P<0.001, and r=0.785, P<0.001, respectively).Conclusion PhH3 MC was superior to manual counting mitotic figures in recurrence risk stratification of PTs. Tumor histologic grading using PhH3 is as crucial as resection margin status in predicting tumor recurrence in benign and borderline PTs.

Interobserver Variability in Mitotic Count for Meningioma Grading: How Can We Reduce It?

To evaluate the interobserver variability in determining the number of mitoses in 10 high-power field (HPF) and thus the tumor grade, and to investigate how to reduce grade discordance between the observers and the most useful method to identify the patients who would receive an additional treatment. Two hundred and seventy cases with meningioma were re-evaluated by three experienced pathologists and five senior residents. They determined the number of mitotic figures in 10 HPF in each slide. Re-evaluation of the cases, which were found of different grades from the reference observers was requested by full scan method. Statistical analysis was performed by using SPSS V23.0. A moderate agreement was found between the observers and the reference observer. After the evaluation of mitotic activity with the full scan method, the mean numbers of mitoses found by the observers in 10 HPF were increased. In the first evaluation, 4?6 cases were defined as Grade II by the observers. Whereas, 23?27 cases were defined as Grade II after the full scan method. If there are less than 16 mitotic figures throughout the slide, it is more difficult to find the 10 HPF including 4 or more mitosis. Interobserver variability in mitotic figure counting can be reduced by full scan method, and examining the hematoxylin and eosin stained slides by the full scan method helps us to determine the true histologic grade of meningiomas in most cases, who would receive an additional treatment.

Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis

Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic settings and investigated independent location, interpretation, and enumeration of mitotic figures. Intermodality agreement was assessed employing conventional microscopy (CM40×), and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective magnifications. An aggregate 1647 mitotic figure count observations were available from conventional microscopy and whole slide images for comparison. The intraobserver concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784 to 0.794. Correlation coefficients between the 2 digital modes, and as compared to conventional microscopy, were similar and suggest noninferiority among modalities, including whole slide image acquired at lower 20× resolution. As mitotic figure counts serve for prognostic grading of several tumor types, including melanoma, 6 of 8 pathologists retrospectively predicted survival prognosis using whole slide images, compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image for mitotic figure prognostic grading. This study demonstrated agreement of replicate reads obtained across conventional microscopy and whole slide images. Hence, quantifying mitotic figures served as surrogate histological feature with which to further credential the interchangeability of whole slide images for primary diagnosis.