scholarly journals Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent

2019 ◽  
Vol 57 (2) ◽  
pp. 214-226 ◽  
Author(s):  
Christof A. Bertram ◽  
Marc Aubreville ◽  
Corinne Gurtner ◽  
Alexander Bartel ◽  
Sarah M. Corner ◽  
...  
Keyword(s):  
Mast Cell ◽  
High Power ◽  
Ground Truth ◽  
Mitotic Count ◽  
Automated Image Analysis ◽  
High Power Field ◽  
Borderline Cases ◽  
Area Selection ◽  
Cutaneous Mast Cell ◽  
Mitotic Figures

Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marc Aubreville ◽  
Christof A. Bertram ◽  
Christian Marzahl ◽  
Corinne Gurtner ◽  
Martina Dettwiler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Mitotic Count ◽  
Mitotic Figure ◽  
Two Stage ◽  
Data Set ◽  
Area Selection ◽  
Tumor Region ◽  
Cutaneous Mast Cell ◽  
Mitotic Figures

Abstract 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.

scholarly journals Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

2021 ◽  
Author(s):  
Christof A Bertram ◽  
Marc Aubreville ◽  
Taryn A Donovan ◽  
Alexander Bartel ◽  
Frauke Wilm ◽  
...  
Keyword(s):  
Mast Cell ◽  
High Performance ◽  
Ground Truth ◽  
Mitotic Count ◽  
Computer Assisted ◽  
Malignant Neoplasms ◽  
Mast Cell Tumors ◽  
Computer Assistance ◽  
Phosphohistone H3 ◽  
Cutaneous Mast Cell

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/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, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal 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 MF and improving classification against imposters. The inter-observer 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 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 prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

scholarly journals Is There Pathological Uniformity between the Periphery and Center of a Gastrointestinal Stromal Tumor?

2021 ◽  
Vol 10 (4) ◽  
pp. 687
Author(s):  
Seong Ji Choi ◽  
Kwan Hong Lee ◽  
Chan Kyoo Yoo ◽  
Jai Hoon Yoon ◽  
Ki Seok Jang ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Mitotic Index ◽  
High Power ◽  
Malignant Potential ◽  
Mitotic Count ◽  
Pathological Diagnosis ◽  
High Power Field ◽  
Gastric Gists ◽  
Significant Difference ◽  
The Mean

Background: Gastrointestinal stromal tumors (GISTs) are mesenchymal tumors and have some malignant potential. Mitotic count is important for predicting the malignant potential of GISTs. Proper treatment of GISTs requires accurate pathological diagnosis. In general, endoscopic ultrasound-guided fine-needle aspiration and deep biopsy are used for pathological diagnosis of GIST before making decisions about surgery. This study sought to evaluate the pathological uniformity of gastric GISTs for mitotic index of the center and periphery of the GIST. Methods: We retrospectively reviewed the data of 37 gastric GIST patients who underwent wedge resection at Hanyang University Hospital. We used Armed Forces Institute of Pathology criteria to classify gastric GISTs. To determine the pathological uniformity of gastric GISTs, we compared GIST risk stratification between the center and periphery of GISTs. Results: The mean size of GISTs was 3.56 ± 2.10 cm. Three lesions were located in the antrum, 11 in the fundus, 9 in the cardia, and 14 in the body. The mean age of patients was 58.65 ± 9.44 years; 18 patients were male and 19 were female. Thirty-five patients (94.6%) showed the same level of risk stratification between the center and periphery of gastric GISTs, while two patients (5.4%) presented different levels of risk between the two sites. No significant difference in mitotic count was observed between the two sites (kappa value = 0.863; p = 0.001). Conclusions: Mitotic index category (either more than five mitoses per high-power field or five or fewer mitoses per high-power field) of GISTs showed good concurrence between the center and periphery.

Automated image analysis of nuclear atypia in high-power field histopathological image

2015 ◽  
Vol 258 (3) ◽  
pp. 233-240 ◽  
Author(s):  
CHENG LU ◽  
MENGYAO JI ◽  
ZHEN MA ◽  
MRINAL MANDAL
Keyword(s):  
Image Analysis ◽  
High Power ◽  
Automated Image Analysis ◽  
High Power Field ◽  
Nuclear Atypia ◽  
Histopathological Image

Mastocytic Enterocolitis: Increased Mucosal Mast Cells in Chronic Intractable Diarrhea

2006 ◽  
Vol 130 (3) ◽  
pp. 362-367 ◽  
Author(s):  
Shriram Jakate ◽  
Mark Demeo ◽  
Rohan John ◽  
Mary Tobin ◽  
Ali Keshavarzian
Keyword(s):  
Mast Cell ◽  
Mast Cells ◽  
High Power ◽  
Chronic Diarrhea ◽  
Duodenal Biopsy ◽  
Mast Cell Tryptase ◽  
High Power Field ◽  
Intractable Diarrhea ◽  
Biopsy Specimens ◽  
Hematoxylin Eosin

Abstract Context.—In some adult patients with chronic intractable diarrhea, the diagnosis remains elusive even after detailed evaluations, and colonic or duodenal biopsy specimens may appear unremarkable on routine hematoxylin-eosin staining. Objectives.—To assess the concentration of mast cells in colonic or duodenal biopsy specimens by immunohistochemical analysis for mast cell tryptase from patients with chronic intractable diarrhea and to evaluate their response to drugs affecting mast cell function. Design.—Mast cells per high-power field were assessed in biopsy specimens from 47 patients with chronic intractable diarrhea, from 50 control subjects, and from 63 patients with other specific diseases that cause chronic diarrhea (inflammatory bowel disease, celiac disease, collagenous colitis, and lymphocytic colitis). Patients with chronic intractable diarrhea who had more than 20 mast cells per high-power field were administered drugs affecting mast cell mediator function and release. Results.—The mean ± SD concentration of mast cells in the 50 control subjects was 13.3 ± 3.5 cells per high-power field; hence, patients with more than 20 mast cells per high-power field were considered to have increased mast cells. Thirty-three (70%) of 47 patients with chronic intractable diarrhea had increased mast cells, and symptoms were controlled by drug therapy in 22 (67%) of the 33 patients. No patient had systemic or cutaneous mastocytosis. No increase in mast cells was seen in patients with other common causes of chronic diarrhea. Conclusions.—In chronic intractable diarrhea, colonic or duodenal biopsy specimens may appear unremarkable on routine hematoxylin-eosin staining, but increased mast cells may be demonstrated by immunohistochemistry for mast cell tryptase, with the novel term mastocytic enterocolitis describing this condition. Similar increases in mast cells are not apparent in control populations or in patients with other specific diseases that cause chronic diarrhea. The cause of the increased mast cells remains to be elucidated.

Performing Colonic Mast Cell Counts in Patients With Chronic Diarrhea of Unknown Etiology Has Limited Diagnostic Use

2015 ◽  
Vol 139 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Aisha Sethi ◽  
Dhanpat Jain ◽  
Bani Chander Roland ◽  
Jason Kinzel ◽  
Joanna Gibson ◽  
...  
Keyword(s):  
Mast Cell ◽  
Receiver Operating Characteristic ◽  
High Power ◽  
Operating Characteristic ◽  
Chronic Diarrhea ◽  
Unknown Etiology ◽  
High Power Field ◽  
Cell Counts ◽  
Cutoff Values ◽  
Receiver Operating

Context Mastocytic enterocolitis is a recently described entity defined by chronic diarrhea of unknown etiology and normal colon biopsy results with increased mast cells (MCs) seen on special stains. These patients may benefit from mast cell stabilizers; however, the clinical utility of MC counts remains unknown. Objective To determine the clinical utility of colonic MC counts on normal biopsies in patients with chronic diarrhea of unknown etiology. Design Blinded MC counts using a c-Kit stain were performed in 76 consecutive patients with chronic diarrhea of unknown etiology who had normal colon biopsy results and in 89 consecutive control patients presenting for screening colonoscopy. Mast cells were counted per single high-power field in the highest-density area. A t test was used to compare the counts, and receiver operating characteristic curves were generated to examine sensitive and specific cutoff values. Results Overall, MC counts averaged 31 MCs per high-power field in the study group versus 24 MCs per high-power field in the control group (P &lt; .001). When stratified by location, a significant increase was seen in biopsies from the left colon only. Receiver operating characteristic analysis revealed that overall MC counts, left-sided MC counts, and the difference between right- and left-sided MC counts did not yield discriminatory cutoff values (area under the curve, 0.68, 0.74, and 0.81, respectively). Conclusions Mast cell counts were increased in patients with chronic diarrhea of unknown etiology, primarily in the left colon. However, receiver operating characteristic analysis demonstrates no discriminatory cutoff values. Quantitative MC stains yield little useful diagnostic information, and further studies are necessary to determine whether mastocytic enterocolitis truly represents a distinct entity.

Delayed Calcium Channel Blockade with Diltiazem Reduces Paracetamol Hepatotoxicity in Mice

1991 ◽  
Vol 10 (2) ◽  
pp. 119-123 ◽  
Author(s):  
Charles D. Deakin ◽  
Christopher D. Gove ◽  
Elizabeth A. Fagan ◽  
J. Michael Tredger ◽  
Roger Williams
Keyword(s):  
Body Weight ◽  
Liver Damage ◽  
High Power ◽  
Aspartate Aminotransferase ◽  
Channel Blockade ◽  
Aminotransferase Activity ◽  
High Power Field ◽  
Calcium Channel Blockade ◽  
Regenerative Activity ◽  
Mitotic Figures

1 Diltiazem (30 mg kg-1 body weight, intraperitoneally) given to mice 9 h after paracetamol (450 mg kg-1, orally) reduced liver damage, as judged by plasma aspartate aminotransferase activity (median 186, range 6-602 IU I-1, n = 18 vs 466, range 23-3872 IU I-1 in 18 saline-treated controls; P < 0.05) with comparable reductions in mortality (14% vs 33%, respectively; NS). 2 Regenerative activity, as judged by mitotic figures in tissue removed at 30 h after paracetamol, was significantly higher in mice treated at 9 h with diltiazem (median 0.83 per high power field vs 0.1 in saline-treated controls; P < 0.05). 3 Diltiazem administered earlier or later than 9 h showed reduced efficacy and in some cases potentiated toxicity, as did nifedipine (40 mg kg-1 in divided doses up to 9 h).

Mitotic Count in Canine Cutaneous Mast Cell Tumours – Not Accurate but Reproducible

2020 ◽  
Vol 174 ◽  
pp. 143
Author(s):  
C.A. Bertram ◽  
M. Aubreville ◽  
C. Gurtner ◽  
A. Bartel ◽  
S.M. Corner ◽  
...  
Keyword(s):  
Mast Cell ◽  
Mitotic Count ◽  
Cutaneous Mast Cell

scholarly journals Mast cell quantification in the skin of dogs with hormonal dermatosis

2019 ◽  
Vol 13 (3) ◽  
pp. 375
Author(s):  
Glícia Meneses Costa ◽  
Steffi Lima Araujo ◽  
Francisco Antônio Félix Xavier Júnior ◽  
Glayciane Bezerra de Morais ◽  
João Alison De Moraes Silveira ◽  
...  
Keyword(s):  
Mast Cell ◽  
Mast Cells ◽  
High Power ◽  
Infectious Agents ◽  
High Power Field ◽  
Mediating Role ◽  
Significant Difference ◽  
Mast Cell Count ◽  
Healthy Dogs ◽  
Superficial Dermis

The mast cells are important in physiological and pathological skin events. They play an important role in the homeostatic regulatory mechanisms in the skin and thyroid gland. Mast cells present a barrier to difference external environmental stimuli and play a mediating role in the presence of infectious agents under the epidermis. This study aimed to quantify the number of mast cells in histological sections of the skin of healthy dogs and dogs with hypothyroidism and hyperadrenocorticism and to determine the distribution of mast cell numbers in the superficial dermis and deep dermis.  When we compared the total mast cell count per high power field in dogs with hypothyroidism, hyperadrenocorticism and healthy dogs, only dogs with hypothyroidism had a significant difference in the quantification of mast cells per high power field, (p < 0.05). After analyzing our results, it was possible to conclude that animals with hypothyroidism produce greater amount of mast cells in the superficial dermis than patients with hyperadrenocorticism and healthy animals.

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

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christof A. Bertram ◽  
Marc Aubreville ◽  
Christian Marzahl ◽  
Andreas Maier ◽  
Robert Klopfleisch
Keyword(s):  
Mast Cell ◽  
Large Scale ◽  
Region Of Interest ◽  
Mitotic Figure ◽  
Low Grade ◽  
Detection Algorithms ◽  
Large Scale Dataset ◽  
Cutaneous Mast Cell ◽  
Mitotic Figures ◽  
Whole Slide Images

AbstractWe 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.

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