Mitosis Count Research Articles

Counting mitoses: SI(ze) matters

The assessment of proliferative activity by counting mitoses is something that most if not all histopathologists practice, as it is required for diagnosis and/or grading of many tumours. In the past, mitoses were often counted ‘per high power field’, which was convenient, but was regarded as scientifically questionable even 40 years ago, as it ignored the fact that microscope fields may differ substantially, even at the same high power (×400) magnification.1 Other features such as vessels, cell numbers or apoptoses are also counted in some circumstances, and the same issues apply.

Intratumoural spatial distribution of S100B\u2009+\u2009folliculostellate cells is associated with proliferation and expression of FSH and ER\u03b1 in gonadotroph tumours

Folliculostellate cells are S100B-expressing cells with numerous functions in the normal anterior pituitary. These cells have also been identified in pituitary neuroendocrine tumours (PitNETs), where their precise role remains elusive. Here, we aimed to build a refined cartography of S100B-expressing cells to characterise their interpatient and intratumoural spatial distribution, and to start identifying their potential functions in PitNETs. High-throughput histological analysis of S100B-stained tumour sections of 54 PitNETs revealed a significant decrease in S100B + cells in PitNETs compared to the normal anterior pituitary. A Ki67 index ≥ 3, a mitosis count > 2/10 per high power fields, and a proliferative status, were all associated with fewer S100B + cells in gonadotroph tumours. Gonadotroph tumours also showed interpatient and intratumoural heterogeneity in the spatial distribution of S100B + cells. The existence of an intratumoural heterogeneity was further confirmed by the incorporation to our spatial analysis of additional markers: Ki67, FSH, LH, ERα and SSTR2. The tumour areas with fewer S100B + cells displayed a higher percentage of Ki67 + cells, whereas strong positive correlations were observed between S100B + , FSH + , and ERα + cells. Such spatial associations suggest that S100B + folliculostellate cells could play a role in gonadotroph tumorigenesis, and may contribute to the maintenance of tumour cells in a low proliferating, FSH + /ERα + differentiated state. Albeit, further in-depth functional studies are required to decipher the mechanisms underlying these spatial associations and to potentially identify a therapeutic use.

Mitotic counts in one high power field in breast core biopsies is equivalent to counts in 10 high power fields.

Mitotic rate is an important prognostic predictor in invasive breast carcinoma. Current guidelines recommend counting mitoses from 10 contiguous high power fields (HPFs) in the core biopsy. We propose a method to score mitotic activity in 1 HPF at the most mitotically active area of the tumour edge, or the interface between invasive tumour and benign breast tissue. We propose a score of 1, 2, or 3, corresponding to ≤1, 2, or ≥3 mitoses in 1 HPF, respectively. A total of 141 breast core biopsies with corresponding surgical excisions were blindly examined. We counted the number of mitotic figures in 1 HPF and in 10 contiguous HPFs in the core biopsy and compared with the mitotic count from 10 contiguous HPFs in the excision which is considered the gold standard. Concordance rates and interobserver agreement rates were calculated. The concordance rate was 82.3%, 78.7% and 82.3% between 1 HPF versus 10 HPFs in the core biopsy, 1 HPF in the core biopsy versus 10 HPFs in the excision and 10 HPFs in the core biopsy vs 10 HPFs in the excision, respectively. In the core biopsy, all three investigators agreed in 73.8% and 83.7% of the cases using the 1 HPF method and the 10 HPFs method, respectively; in the excision specimen, agreement was reached in 82.3% of the cases. The 1 HPF method showed similar concordance rate and interobserver agreement compared to the conventional method in the prediction of the mitotic score in the excision in all score groups. When stratified by mitotic score, the 1 HPF method predicted superior correlation with excision in the score 1 group than the 10 HPFs method, but not in the score 2 or 3 groups. From these findings we conclude that the proposed 1 HPF method can be used in clinical practice to grade invasive breast carcinomas in core biopsies, with the possibility of being utilised in small biopsies with less than 10 HPFs of invasive carcinoma.

Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

AbstractCancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides. Among the three factors involved in breast cancer grading (mitosis count, nuclear atypia, and tubule formation), mitotic cell counting is the most challenging task for pathologists. It is possible to automate this task by applying computational algorithms on pathology slides images. Lack of sufficiently large datasets and class imbalance between mitotic and non‐mitotic cells in slide images are the two major challenges in developing effective deep learning‐based methods for mitosis detection. In this paper, we propose a new approach and a method based on that to address these challenges. The high training data requirement of the advanced deep neural network is met by combining two datasets from different sources after a color‐normalization process. Class imbalance is addressed by the augmentation of the mitotic samples in a context‐preserving manner. Finally, a customized convolutional neural network classifier is used to classify the candidate cells into the target classes. We have used the publicly available datasets MITOS‐ATYPIA and MITOS for the experiments. Our method outperforms most of the recent methods that are based on independent datasets and at the same time offers adaptability to the combination of datasets from different sources.

MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images

Breast cancer is the second most commonly diagnosed type of cancer among women as of 2021. Grading of histopathological images is used to guide breast cancer treatment decisions and a critical component of this is a mitotic score, which is related to tumor aggressiveness. Manual mitosis counting is an extremely tedious manual task, but automated approaches can be used to overcome inefficiency and subjectivity. In this paper, we propose an automatic mitosis and nuclear segmentation method for a diverse set of H&E breast cancer pathology images. The method is based on a conditional generative adversarial network to segment both mitoses and nuclei at the same time. Architecture optimizations are investigated, including hyper parameters and the addition of a focal loss. The accuracy of the proposed method is investigated using images from multiple centers and scanners, including TUPAC16, ICPR14 and ICPR12 datasets. In TUPAC16, we use 618 carefully annotated images of size 256×256 scanned at 40×. TUPAC16 is used to train the model, and segmentation performance is measured on the test set for both nuclei and mitoses. Results on 200 held-out testing images from the TUPAC16 dataset were mean DSC = 0.784 and 0.721 for nuclear and mitosis, respectively. On 202 ICPR12 images, mitosis segmentation accuracy had a mean DSC = 0.782, indicating the model generalizes well to unseen datasets. For datasets that had mitosis centroid annotations, which included 200 TUPAC16, 202 ICPR12 and 524 ICPR14, a mean F1-score of 0.854 was found indicating high mitosis detection accuracy.

High regional mortality due to malignant melanoma in Eastern Finland may be explained by the increase in aggressive melanoma types

BackgroundA regional skin cancer prevention program in Eastern Finland revealed a relatively high age-standardized mortality due to malignant melanoma during 2013–2017. An explanation for this is needed.PurposeTo analyse the 543 melanoma samples in 524 subjects collected during 2000–2013 at Kuopio University Hospital and reposited in the Biobank of Eastern Finland. A focus was directed to factors related to metastasis.MethodsThe samples were analysed anonymously by examining the histopathological report, referral text and the list of diagnoses. A possible state of immunosuppression was evaluated.ResultsThe mean age at the diagnosis of malignant melanoma (MM), lentigo maligna (LM) and melanoma in situ was relatively high, i.e., 66.2, 72.1 and 63.3, respectively. Especially the MM type increased markedly during 2000–2013. In further analyses of a representative cohort of 337 samples, the proportion of nodular melanoma and LM/LMM melanoma was relatively high, 35.6 and 22.0%, respectively, but that from superficial spreading melanoma relatively low (33.8%). Metastasis correlated with immunosuppression, male gender, Clark level, Breslow thickness, ulceration, mitosis count, invasion into vessels and/or perineural area, microsatellites, melanoma subtype, body site, recidivism, and the absence of dysplastic nevus cells.ConclusionThe marked increase in aggressive melanomas with associated metastasis, and the relatively high age at diagnosis, can partially explain the mortality.

Predictive and Prognostic Value of BRAF and NRAS Mutation of 159 Sentinel Lymph Node Cases in Melanoma-A Retrospective Single-Institute Study.

Simple SummarySentinel lymph node (SLN) status is still the most important prognostic factor for melanoma patients; however, the efficacy of completing lymph node dissection remains questionable. The aim of our study was to assess the correlation between known prognostic factors, mutational occurrence of BRAF and NRAS in the primary tumor, and SLN status. Statistical analysis revealed that Breslow thickness was associated with SLN status; however, neither NRAS nor BRAF showed a predictive value. Furthermore, NRAS mutation in primary tumors proved to be an independent factor of tumor progression. This suggests that regardless of the SLN status, the NRAS-mutant subgroup of patients requires closer monitoring.Purpose: To assess the prognostic role of sentinel lymph node status (SLN) in melanoma patients, a statistical comparison was performed with the application of already known prognostic factors, mutational occurrence of BRAF and NRAS in the primary tumor, as well as disease outcome. Methods: Our retrospective single-center study involved 159 melanoma cases, who underwent SLN biopsy. The following clinico-pathological data were collected: age, gender, location of primary tumor, Breslow thickness, ulceration degree, histological subtype, mitosis count, lymphovascular and perineural invasion, presence of tumor-infiltrating lymphocytes, regression signs, mutations of BRAF and NRAS of the primary tumors, and SLN status. Results: From the studied clinico-pathological factors, only Breslow thickness increased the risk of SLN positivity (p = 0.025) by multivariate analysis, while neither BRAF nor NRAS mutation of the primary tumor proved to be a predictor of the SLN status. While the NRAS-mutant subgroup showed the most unfavorable outcome for progression-free and distant metastasis-free survival, their rate of positive SLNs proved to be relatively lower than that of patient groups with BRAF mutation and double-wild-type phenotypes. Conclusion: Similarly to the importance of SLN positivity, NRAS mutation of the primary tumor proved to be an independent prognostic factor of progression. Therefore, despite negative SLN, this NRAS-mutant subgroup of patients still requires closer monitoring to detect disease progression.

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.

Neuroendocrine neoplasms of the digestive tract : incorporating the 2019 WHO grading schema in the South African context

Background The classification of gastrointestinal (GI) neuroendocrine neoplasms (NENs) has been updated in the WHO classification of tumours of the digestive tract. Mitotic rate and Ki-67 proliferation index are central to the histopathological grading of these tumours. Assessing these variables is not standardised in local practice. This study addresses this deficit in the South African context. Methods This is a retrospective audit of archival material of resection specimens of GI NENs (excluding appendix) at Groote Schuur Hospital from 2004 to 2017. We performed a mitotic count and immunohistochemistry for Ki-67 to reclassify tumours according to the 2019 WHO criteria. Results Thirty-five resection specimens were examined. These represented stomach (n = 5), jejunoileum (n = 17), colon (n = 7) and rectum (n = 6). The mean age at the time of resection was 56 years (range 32-72). Twenty-one cases (60%) were women and 14 (40%) were men. The majority (24/35) of tumours were classified as grade 1 well-differentiated neuroendocrine tumours (WD NETs), 8/35 were classified as grade 2 WD NETs and 3/35 were classified as small cell poorly differentiated neuroendocrine carcinomas (PD NECs). Re-classification resulted in 7/35 (20%) changes in tumour grade (4 upgraded, 3 downgraded). In most instances (7/8), the Ki-67 proliferation index was the determining factor in assigning tumour grade. Conclusion This study has increased awareness of the recently updated WHO classification system. We conclude that an objective and systematic approach to counting of mitoses and determination of the Ki-67 proliferation index is recommended in routine practice.

340 Poster - Deep learning enables fully automated mitotic density assessment in breast cancer histopathology

Background: Mitosis counting is an important part of breast cancer grading, yet known to suffer from observer variability. Advances in machine learning enable fully automated analysis of digitized glass slides. The present study evaluated automatic mitosis counting and demonstrated applicability on triple negative breast cancers (TNBC).

The correlation between IGF-1R and HER-2 expression with mitosis count and histopathological grade in invasive breast carcinoma of no special type

BackgroundThe IGF-1R signaling pathways have cross-talk with HER-2 signaling pathways and was thought to be one of the resistance mechanism intrastuzumab therapy. This research was proposed to analyze the correlation between IGF-1R and HER-2 expression with mitosis count andhistopathological grade in invasive breast carcinoma of no special type.MethodsThis research was a cross-sectional design. A total of fifty-five invasive breast carcinoma of no special type cases diagnosed in theAnatomical Pathology Centre Diagnostic of Medical Faculty of Andalas University in a period of 2014-2015 were collected and stainedimmunohistochemically with IGF-1R and HER-2 antibodies. IGF-1R and HER-2 expression were examined and their correlation with mitosiscount and histopathological grade were statistically analyzed with T-test, Oneway Anova and Chi-Square test.ResultsIGF-1R cytoplasm and membranous expression were found positive in 18.2% and 34.5% cases respectively, meanwhile, HER-2 expressionswere found positive in 23.6% cases of invasive breast carcinoma of no special type. There were significant correlations between IGF-1Rcytoplasm expression with mitosis count (p=0.049). There were no significant correlations between IGF-1R membranous expression withmitosis count (p=0.641) with histopathological grade (p=1.00). There were no significant correlations between HER-2 expression withmitosis count (p=0,495) and histopathological grade (p=1.000).ConclusionIGF-1R expressions have a significant correlation with mitosis count rather than HER-2 expressions. Inhibition it’s signaling pathways mayhave therapeutic value in breast carcinoma. Combination therapy of anti-HER-2 and anti-IGF-1R are expected to overcome resistance withtrastuzumab in HER-2 positive breast carcinoma

Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses

BackgroundThe mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial intelligence (AI) coupled with whole slide imaging offers a potential solution to this problem. The aim of this study was to accordingly critique an AI tool developed to quantify mitotic figures in whole slide images of invasive breast ductal carcinoma.MethodsA representative H&E slide from 320 breast invasive ductal carcinoma cases was scanned at 40x magnification. Ten expert pathologists from two academic medical centers labeled mitotic figures in whole slide images to train and validate an AI algorithm to detect and count mitoses. Thereafter, 24 readers of varying expertise were asked to count mitotic figures with and without AI support in 140 high-power fields derived from a separate dataset. Their accuracy and efficiency of performing these tasks were calculated and statistical comparisons performed.ResultsFor each experience level the accuracy, precision and sensitivity of counting mitoses by users improved with AI support. There were 21 readers (87.5%) that identified more mitoses using AI support and 13 reviewers (54.2%) that decreased the quantity of falsely flagged mitoses with AI. More time was spent on this task for most participants when not provided with AI support. AI assistance resulted in an overall time savings of 27.8%.ConclusionsThis study demonstrates that pathology end-users were more accurate and efficient at quantifying mitotic figures in digital images of invasive breast carcinoma with the aid of AI. Higher inter-pathologist agreement with AI assistance suggests that such algorithms can also help standardize practice. Not surprisingly, there is much enthusiasm in pathology regarding the prospect of using AI in routine practice to perform mundane tasks such as counting mitoses.

Mitosis detection in breast cancer histopathology images using hybrid feature space.

Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.

Retrospective analysis of prognostic factors affecting the recurrence and disease-free survival following surgical management of gastrointestinal stromal tumors.

The aim of this study was to evaluate the prognostic factors effecting recurrence risk and disease-free survival of the patients who were diagnosed as gastrointestinal stromal tumor after complete resection of the tumor with or without adjuvant therapy. Between the years 2005 and 2013, data of 71 patients including clinical and demographic features, tumor localizations, pathologic examinations, survival and recurrence rates were enrolled into this retrospective study. Male/female ratio was 1.71, and mean age was 60.27 ± 14.65 years. Forty-two (59.2%) patients had tumor in stomach, 16 (22.5%) in small bowel, whereas 12 (16.9%) had extra-gastrointestinal system and one patient (%1.4) had rectal localization. Modified NIH risk stratification scheme categorized 9 (12.68%) patients in very low-, 12 (16.90%) in low-, 21 (29.58%) patients in moderate-and 29 (40.85%) patients in high-risk group. Twenty-four (33.8%) patients had a metastatic disease at follow-up while 13 (18.3%) patients were metastatic at admission. R0 resection was successfully performed in 51 (71.8%) patients, while R1 resection in 9 (12.7%) and R2 resection in 11 (15.5%) were achieved. Mean follow-up time was 47.12 ± 33.52 months (range, 1-171 months). Nineteen (26.8%) patients demonstrated recurrence with a mean time of 22.16 ± 15.89 months (range, 3-57 months). During follow-up 17 (23.9%) patients were deceased. In univariate analysis, high-risk group, small bowel and extra-gastrointestinal system localization, R1-2 resection, necrosis, positive resection margin and invasion of surrounding tissues, metastatic disease and adjuvant therapy were statistically significant in terms of recurrence. Multivariate analysis presented small bowel and extra-gastrointestinal system localization, R2 resection, mitoses count, invasion and adjuvant therapy as independent prognostic risk factors affecting disease-free survival rates. The 1, 3 and 5 years of disease-free survival rates of the patients were 89.6%, 75.4%, 64.3%, respectively. As mentioned in the literature, the mainstay of curative therapy of gastrointestinal stromal tumor is surgery. In our study, not only small bowel, extra-gastrointestinal system localization and invasion of surrounding tissues by tumor, but also R2 resection that complicate the local control of the disease were represented as independent adverse prognostic factors for disease-free survival. Unfavourable clinical outcomes of adjuvant therapy over the disease-free survival was linked to higher tumor stage with metastatic disease and emphasized that prospective trials with more cases should be practiced.

CD56 Expression Is Associated with Biological Behavior of Pancreatic Neuroendocrine Neoplasms.

PurposeCD56 is a neural cell adhesion molecule that plays a role in the cohesiveness of neuroendocrine cells. The aim of this study was to explore the biological values of CD56 expression in pancreatic neuroendocrine neoplasms (PNENs) and its role in predicting PNENs grades.Patients and MethodsA total of 138 patients with histological-proven PNENs was included (66 G1, 46 G2 and 26 G3). The clinicopathological characteristics, including mitosis count, ki67 index, chromogranin A (CgA), synaptophysin (Syn) and CD56 expression, were evaluated. We assessed the diagnostic performance of markers in predicting PNEN G3 and the association between CD56 expression and risk of G3 or organs invasion.ResultsLack of CD56 immunoreaction (CD56-) was more common in PNEN G3 than G1/G2 (31% vs 0–2%, p < 0.01). The sizes of CD56- tumors were larger than CD56 positive tumors in PNEN G3 (p < 0.01). The odds ratio (OR) of CD56- expression was 13.6 [95% confidence interval (CI): 2.1–88.1] in predicting PNEN G3. The OR of CD56- expression was 6.5 (95% CI: 1.1–38.6) and 31.9 (95% CI: 1.09–938.3) in predicting organs invasion and neuroendocrine carcinoma in PNEN G3, respectively. Tumor size (area under the curve [AUC] = 0.77 and size+CD56- expression [AUC = 0.84]) had acceptable performance in predicating PNEN G3.ConclusionLack of CD56 immunoreaction may be a predictor and biological behavior marker for PNEN G3.

Characteristics of adrenocortical carcinoma in South Korea: a registry-based nationwide survey.

PurposeTo evaluate the clinical characteristics and prognostic factors in patients with adrenocortical carcinoma (ACC) in South Korea.MethodsA nationwide, registry-based survey was conducted to identify pathologically proven ACC at 25 tertiary care centers in South Korea between 2000 and 2014. Cox proportional hazard model and log-rank test were adopted for survival analysis.ResultsTwo hundred four patients with ACC were identified, with a median follow-up duration of 20 months (IQR 5–52 months). The median age at diagnosis was 51.5 years (IQR 40–65.8 years), and ACC was prevalent in women (n = 110, 53.9%). Abdominal pain was the most common clinical symptom (n = 70, 40.2%), and ENSAT stage 2 was most common (n = 62, 30.4%) at the time of diagnosis. One hundred sixty-nine patients underwent operation, while 17 were treated with other modalities. The remission rate was 48%, and median recurrence-free survival time was 46 months. Estimated 5-year recurrence-free rate was 44.7%. There were more women, large tumor, atypical mitosis, venous invasion, and higher mitotic count in cancer recurrence group. Estimated 5-year overall survival and disease-specific survival rates were 64.5 and 70.6%, respectively. Higher ENSAT stage and advanced pathologic characteristics were risk factors for all-cause mortality of ACC. Large tumor size and cortisol-secreting tumor were additional risk factors for ACC-specific death.ConclusionsWe report the first epidemiologic study regarding ACC in an Asian population. ENSAT stage 4; lymph node involvement; non-operative group; and invasion of vein, sinusoid, or capsule were associated with an increased risk for all-cause mortality.

Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade.

To assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade. This retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed. The inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05). The 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade. • Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade. • High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance. • The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.

Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors

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. 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. 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. 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. 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.

Imaging growth as a predictor of grade of malignancy and aggressiveness of IDH-mutant and 1p/19q-codeleted oligodendrogliomas in adults.

We quantified the spontaneous imaging growth rate of oligodendrogliomas. We assessed whether (i) it discriminates between World Health Organization (WHO) grade II and grade III oligodendrogliomas, and (ii) grade III oligodendrogliomas with neo-angiogenesis are associated with more fast growth rates (≥8 mm/y). This work employed a retrospective bicentric cohort study (2010-2016) of adult patients harboring a newly diagnosed supratentorial oligodendroglioma, isocitrate dehydrogenase (IDH) mutant and 1p/19q codeleted (WHO 2016 classification), with a minimum of 2 available MRIs before any treatment (minimum 6-week interval) to measure the spontaneous tumor growth rate. We included 108 patients (age 44.7 ± 14.1 y, 60 males). The tumor growth rate was higher in grade III oligodendrogliomas with neo-angiogenesis (n = 37, median 10.4 mm/y, mean 10.0 ± 6.9) than in grade III oligodendrogliomas with increased mitosis count only (cutoff ≥6 mitoses, n = 18, median 3.9 mm/y, mean 4.5 ± 3.2; P = 0.004), and higher than in grade II oligodendrogliomas (n = 53, median 2.3 mm/y, mean 2.8 ± 2.2; P < 0.001). There was increased prevalence of fast tumor growth rates in grade III oligodendrogliomas with neo-angiogenesis (54.1%) compared with grade III oligodendrogliomas with increased mitosis count only (11.1%; P < 0.001), and in grade II oligodendrogliomas (0.0%; P < 0.001). The tumor growth rate trends did not differ between centers (P = 0.121). Neo-angiogenesis (P < 0.001) and mitosis count at ≥9 (P = 0.013) were independently associated with tumor growth rates ≥8 mm/year. A tumor growth rate ≥8 mm/year was the only predictor independently associated with shorter progression-free survival (P = 0.041). The spontaneous tumor growth rate recapitulates oligodendroglioma aggressiveness, permits identification of grade III oligodendrogliomas preoperatively when ≥8 mm/year, and questions the grading by mitosis count.

Relation of PTEN and Ki67 expression with prognosis in gastrointestinal stromal tumors

Aim: The purpose of the present study of ours was to examine the relation of Ki67 and PTEN expression with the prognosis in the pathologies of patients who undergo gastrointestinal stromal tumor surgery in our clinic. Gastrointestinal stromal tumors; In addition to being small incidental tumors; can be tumors with large size and malignant behavior. For this reason, the relation of a great number of macroscopic and microscopic parameters with prognosis was examined. The diameter, location and mitotic index of the tumor rank the first among these parameters; however, several pathological parameters like PTEN and Ki67 expression are also important.Material and Methods: The present study was conducted between January 2005 and April 2015 with patients diagnosed with gastrointestinal stromal tumors. The demographic data (age and gender), tumor localizations, tumor sizes, mitotic activities, prognostic risk classification, follow-up times, PTEN expressions, Ki67 proliferation indices, relapse and metastasis status, andsurvival data of the patients were examined retrospectively.Results: A total of 76 patients were included in the present study. A total of 33 (43.4%) of the participants were women, and the average age was 59.7±11.7 years. The relations of PTEN with risk scores (p=0.330), tumor diameter (p=0.360), location (p=0.169), and mitosis count (p=0.579) were not significant. The relation of PTEN staining rate with relapse (p=0.832), metastasis (p=0.626) and survival (p=0.069) was not found to be statistically significant. The relation of Ki67 expression with risk scoring (p=0.018) and mitosis count (p=0.003) was significant. Although the relation between Ki67 and relapse (p=0.019) and metastasis (p=0.003) was significant, the relation between survival (p=0.655) was not significant at a statistical level.Conclusion: The Ki67 expression was a prognosis-indicating index for relapse and metastasis when the baseline value was taken as 5%; however, no relation was detected between PTEN expression and prognosis.