Cells In Smears Research Articles

Classification and identification of human papillomavirus based on its prevalence and development of cervical lesion among Iranian women.

Introduction: Cervical cancer is the most common female cancer in large areas of the developing world, and almost half of these cases (54%) arises in Asia, where cervical cancer is still threatening women’s health and survival, which makes it a considerable public problem. Human papillomavirus (HPV) is one of the most powerful human carcinogens. Today, it has been proven that all cervical cancers and primary precancerous lesions are caused by carcinogenic types of HPV infections. HPV genotyping can therefore evaluate the screening programs. Methods: Five hundred fifty women referring to the gynecological centers were subjected to Pap smear cell samples. The cytopathological diagnosis of obtained cervical samples was based on the Bethesda system. HPV genotyping was carried out using the INNO-LiPA HPV Genotyping Extra II Amp assay. Results: In a total of 244 HPV positive cases, single‑type HPV infec­tion was observed in 49.6%, while multi‑type HPV infections (including ≥ 2 types) were found in 45.5% of cases. Among the 110 cases with abnormal cytology results, going-over analyses led to the identification of atypical squamous cell of unknown significance (ASCUS) in 73 cases, low‑grade squamous intraepithelial lesions (LSIL) in 24 cases, and high‑grade squamous intraepithelial lesion (HSIL) in 12 cases. In these groups, the infection rate of high-risk HPV (HR-HPV) was 89%, 82%, and 100%, respectively. Conclusion: In this study, the total population of women suffering from different cervical lesions and malignancy was found to be infected with various HPV genotypes. High prevalence of HPV- 53 and HPV- 16 detected among participants with normal cytology can be considered as a tip-off development of cervical cancer among Iranian women.

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

MAST CELLS AFTER BONE MARROW AUTOTRANSPLANTATION

Purpose: to study bone marrow mast cells in mice after autotransplantation. Materials and methods. Mast cells and sulphatedness degree of heparin mucopolysaccharide in the bone marrow were identified using Unna staining with polychromatic toluidine blue. Study results. Data on mast cells distribution in the bone marrow after autotransplantation in 40 minutes and in 2 hours were analyzed. Two types of mast cells’ reaction to autologous bone marrow introduction were identified: sequential degranulation and exocytosis. As a result, secretion of mast cells causes production of biologically active substances in the microenvironment of a mast cell; these substances form chains and conglomerates from granules, changes in the number of which can contribute to changing the process of mitotic division. According to morphological criteria three populations of mast cells were identified: small-sized cells, with blue-colored nuclei stained orthochromatically and located asymmetrically; metachromatic oval-shaped cells with a nucleus located in the centre; large, degranulated, gamma-metachromatic mast cells. Most part of mast cells in bone marrow smears is presented by integral forms. Heparin in mast cells takes part in regulating metabolism in the bone marrow after autotransplantation.

Diagnostic accuracy of E-cadherin for malignanteffusions: a systematic review and meta-analysis

BackgroundThe differential diagnosis of malignant effusion remains a clinical challenge. We aim to summarise all relevant literature studies in order to determine the overall clinical value of E-cadherin in the...

Color models of interference images of thin stratified objects in optical microscopy

Algorithms for the analysis of polychromatic interference patterns in images of thin stratified objects in optical microscopy are considered. The algorithms allow one to measure the thin-film optical thickness. A measurement method based on the comparison of colors of the interference image under study and a numerically simulated image is discussed. We discuss a mathematical model for the calculation and numerical simulation of interference patterns and algorithms for interference pattern processing. Color comparison in an RGB color model is described and limitations of such a method are shown. The feasibility of using a Lab color model is shown and algorithms of interference color comparison in this model are presented. Results of application of the presented algorithms to measuring the optical thickness of red blood cells in a blood smear are discussed. The estimation of the error and robustness of the proposed algorithms is conducted.

Estimating Cell Count in Overlapped Cluster of ThinPrep Pap Smear Cell Microscopic Image

The cell adequacy is one of the important factor is Pap smear test as per Bethesda reporting system. The adequate number of cells in a given sample helps to effectively check for abnormality of cells. The Pap smear sample with inadequate number of cells will be rejected and patient will be called for collecting sample once again. The cell adequacy is the number of squamous epithelial cells present in the given Pap smear sample. This paper presents an automated method which estimates the number of cells in a given Pap smear image. This method estimates a cell count even when cells are overlapped. The proposed approach first separates disjoint cells and overlapped cell cluster from cervical Pap smear image. Then the overlapped cluster is further segmented to obtain individual cells. The overlapped cluster is selected based on the area factor. That is area of overlapped cluster will be more than disjoint cells. This method uses Bitplane slicing technique for segmentation of overlapped cells. Then the total cell counts is estimated as sum of disjoint cells count and number of cells separated from overlapped cluster. The method is tested on the ThinPrep images publicly available in open access database.

Differential regulatory effects of chemotherapeutic protocol on CCL3_CCL4_CCL5/CCR5 axes in acute myeloid leukemia patients with monocytic lineage

AimsAML (Acute myeloid leukemia) is characterized as a heterogeneous cancer. Chemokines play fundamental roles in the onset, progression cellular, migration, survival and improvement of AML therapy outcomes. The CCR5 receptors together with their ligands have indirect effects on the progression of cancer. In the present study, we have decided to investigate the impact of chemotherapy on the expression of CCR5 and its related ligands (CCL5, CCL4 and CCL3). Main methodsIn this study, peripheral blood and bone marrow specimens were collected prior and post the first stage of (7 + 3) chemotherapy from 25 AML-M4/M5 patients. The expression of CCR by Lymphocytes in peripheral blood was examined by flow cytometry and QRT-PCR. The serum levels of chemokines were measured by ELISA. Key findingsThere was not observed leukemic blast cells in peripheral blood smear at post first stage of chemotherapy. We found that the expression of CCR5 was attenuated in patients post the first stage of chemotherapy and the healthy control subjects. We have also observed that the serum levels of chemokines were elevated in AML patients prior to chemotherapy. Although in post-chemotherapy stage, only CCL3 was found to reach to the baseline level, CCL5 and CCL4 have not returned to the basal level and were significantly higher than healthy control subjects. SignificanceThe current chemotherapy protocol was not able to completely inhibit CCL5 and CCL4. In conclusion, our findings in harmony with previous studies suggest that inhibition of chemokines along with chemotherapy in AML patients may aid therapy.

Automatic Bone Marrow Cell Identification and Classification By Deep Neural Network

Purpose Differential counting of blood cells is the basis of diagnostic hematology. In many circumstances, identification of cells in bone marrow smears is the golden standard for diagnosis. Presently, methods for automatic differential counting of peripheral blood are readily available commercially. However, morphological assessment and differential counting of bone marrow smears are still performed manually. This procedure is tedious, time-consuming and laden with high inter-operator variation. In recent years, deep neural networks have proven useful in many medical image recognition tasks, such as diagnosis of diabetic retinopathy, and detection of cancer metastasis in lymph nodes. However, there has been no published work on using deep neural networks for complete differential counting of entire bone marrow smear. In this work, we present the results of using deep convolutional neural network for automatic differential counting of bone marrow nucleated cells. Materials & Methods The bone marrow smears from patients with either benign or malignant disorders in National Taiwan University Hospital were recruited in this study. The bone marrow smears are stained with Liu's stain, a modified Romanowsky stain. Digital images of the bone marrow smears were taken using 1000x oil immersion lens and 20MP color CCD camera on a single microscope with standard illumination and white-balance settings. The contour of each nucleated cell was artificially defined. These cells were then divided into a training/validation set and a test set. Each cell was then classified into 1 of the 11 categories (blast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, neutrophils, eosinophils and precursors, basophil, monocyte and precursors, lymphocyte, erythroid lineage cells, and invalid cell). In training/validation set, the classification of each cell was annotated once by experienced medical technician or hematologist. The annotated dataset was used to train a Path-Aggregation Network for instance segmentation task. In test set, cell classification was annotated by three medical technicians or hematologists; only over 2/3 consensus was regarded as valid. After the neural network model was fully trained, the ability of the model to classify and detect bone marrow nucleated cells was evaluated in terms of precision, recall and accuracy. During the model training, we used group normalization and stochastic gradient descent optimizer for training. Random noise, Gaussian blur, rotation, contrast and color shift were also used as means for data augmentation. Results The digital images of 150 bone marrow aspirate smears were taken for this study. They included 61 for acute leukemia, 39 for lymphoma, 2 for myelodysplastic syndrome (MDS), 2 for myeloproliferative neoplasm (MPN), 10 for MDS/MPN, 12 for multiple myeloma, 4 for hemolytic anemia, 9 for aplastic anemia, 8 for infectious etiology and 3 for solid cancers. The final data contained 5927 images and 187730 nucleated bone marrow cells, which were divided into 2 sets: 5630 images containing 170966 cells as the training/validation set, and 297 images containing 16764 cells as the test set. Among the 16764 cells annotated in test set, 15676 cells (93.6 %) reached over 2/3 consensus. The trained neural network achieved 0.832 recall and 0.736 precision for cell detection task, 0.79 mean intersection over union (IOU) for cell segmentation task, mean average precision of 0.659 and accuracy of 0.801 for cell classification. For individual cell categories, the model performs the best with "erythroid-lineage-cells" (0.971 recall, 0.935 precision) and the worst with "monocyte-and-precursors" (0.825 recall, 0.337 precision). Conclusions We have created the largest and the most comprehensive annotated bone marrow smear image dataset for deep neural network training. Compared with previous works, our approach is more practical for clinical application because it is able to take in an entire field of smear and generate differential counts without any other preprocessing steps. Current results are highly encouraging. With continued expansion of dataset, our model would be more precise and clinically useful. Figure Disclosures Yeh: aether AI: Other: CEO and co-founder. Yang:aether AI: Employment. Tien:Novartis: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Research Funding; Roche: Honoraria; Johnson &Johnson: Honoraria; Alexion: Honoraria; BMS: Honoraria; Roche: Research Funding; Celgene: Honoraria; Pfizer: Honoraria; Abbvie: Honoraria. Hsu:aether AI: Employment.

A Novel Automated Image Analysis System Using Deep Convolutional Neural Networks to Diagnose MDS

Detection of dysmorphic cells in peripheral blood (PB) smears is essential for diagnosis of hematological malignancies. Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem cell disorders that can lead to acute leukemia. Although examinations of bone marrow aspiration and biopsy as well as chromosomal and genetic tests are essential to diagnose these disorders, conventional tests such as complete blood count (CBC) and peripheral blood (PB) smear examinations remain to be initial diagnostic work ups. Detection of dysplastic cells in PB smears and evaluation of CBCs are particularly useful for rapid screening. Thanks to the recent advancement of computational and laboratory technologies, routine manual microscopic examinations have been replaced by automated hematology analyzers in many hematology laboratories. However, detection of dysmorphic cells is still challenging. Therefore, more sophisticated image recognition systems need to be developed. In this study, we developed a novel MDS diagnostic system using PB smears. The system consists of a convolutional neural network (CNN)-based image recognition deep learning system (DLS) and an EGB-based decision-making algorithm (XGBoost). All PB smears were prepared at Juntendo University Hospital. The slides were stained with May Grunwald-Giemsa using a fully automated slide-maker. A total of 703,970 digitalized cell images were collected. First, we trained the CNN-based image-recognition system using 695,030 blood cell images taken from 3,261 PB smears of which 1,165 were obtained from patients with hematological disorders. The hematological disorders included MDS (n=94 cases), myeloproliferative neoplasms (n=127), acute myeloid leukemia (n= 38), acute lymphoblastic leukemia (ALL, n=27), malignant lymphoma (n=324), multiple myeloma (n=82) and AA (n=42). Of all images, these 695,030 images were used to train the CNN-based image-recognition system (Fig 1), and rest of the images (n=8,940) were used for validation. The datasets were classified into 17 cell types and 97 abnormal morphological features by two board-certified laboratory technologists and one senior hematopathologist using the morphological criteria of the Clinical and Laboratory Standards Institute (CLSI) H20-A2 guideline and the 2017 WHO classification of myeloid neoplasms and acute leukemia. After accumulating the image patterns using the training datasets, the performance of the DLS was evaluated using the validation datasets that were prepared for testing the DLS. The internal features learnt by the DLS using t-distributed Stochastic Neighbor Embedding (t-SNE)(Fig 2). In this context, our CNN-based image-recognition system exhibited a sensitivity of >93.5% and a specificity of >96.0% when classifying cells in subsets of the validation datasets. We then created an automated MDS diagnostic system by combining the CNN-based image-recognition system with a form of XGBoost. To establish diagnostic algorithm, the training datasets obtained from 75 MDS and 36 Aplastic Anemia (AA) cases were used for learning of the cell image pattern for each disease. The diagnosis of all datasets was validated by independent hematopathologists using clinical information, laboratory, flow cytometric, and genetic data, and bone marrow aspiration and biopsy findings. The accuracy of the system was tested using validation datasets (26 MDS and 11 AA cases). The system differentiated MDS from AA with the sensitivity and specificity of 96.2% and 100%, respectively (AUC 0.990). In conclusion, this is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated image diagnostic systems for various hematological disorders. Currently, we are collecting more data to improve the accuracy. Disclosures Kimura: Sysmex Corporation: Employment. Takehara:Sysmex Corporation: Employment. Uchihashi:Sysmex Corporation: Employment.

Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images.

Objective:Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods:This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result:Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion:The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.

Reappraisal of cytology-histology correlation in cervical cytology based on the recent American Society of Cytopathology guidelines (2017) at a cancer research centre.

To assess the impact of recently published American Society of Cytopathology (ASC) guidelines (2017) on the conduct of cervical cytology-histology correlation (CHC). A retrospective review was conducted for cervical biopsies with their corresponding conventional cervical smears over a 7.5-year period (January 2011-June 2018). As per the ASC guidelines, a discrepancy assessment grid was prepared. Major cytology-histology discordance was defined as a diagnosis of high-grade squamous intraepithelial lesion (HSIL) or CIN2+ in one of the tests with negative result in the other. Smears and biopsies of all discordant cases were reviewed for reasons of overcall and undercall. Of the 341 cervical biopsies with corresponding Papanicolaou smear, cytology-histology agreement was noted in 249 (73%) cases. Major discordance was observed in 22 cases (6.4%)-16 undercalls and six overcalls on cytology-while minor discrepancies were noted in 70 cases. Atypical metaplasia and repair changes were the main reasons for overcall while small HSIL cells in atrophic smear and scant HSIL cells were important causes of undercall on cytology review. Using the ASC guidelines, we could improvise upon the existing CHC methodology for categorisation of cyto-histological pairs of cases with a cytological diagnosis of atypical glandular cells. The present study demonstrates, for the first time, that the recent ASC guidelines facilitate cervical CHC, especially for categorisation of cases with atypical glandular cells on cytology. Uniform application of these guidelines would standardise the conduct of cervical CHC internationally and provide scope for inter-laboratory comparison of data as well as enhance self-learning and peer learning.

Morphometric analysis of atypical glandular cells correctly classifies normal, reactive, and atypical cells in cervical smears.

The 2014 Bethesda System diagnostic criteria for atypical glandular cells (AGC) aid in the classification of atypical cells in cervical cytology. Anyway, AGC diagnosis remains challenging, due to low frequencies of this finding (approximately 0.5%-1% of Pap test results), abundance of AGC mimics, and significant interobserver variability. We developed an algorithm based on nuclear areas parameter that can help to differentiate AGC from Normal and Reactive glandular cells. Nuclear areas and perimeters were measured on 16 Pap smears with AGC and 18 with Reactive glandular cells of women aged between 30 and 77. Glandular cells from nonpathological Pap smears were used as controls. For each case, the means, medians, standard deviations, and the minimum and maximum values of both nuclear areas and perimeters of the cells of interest were calculated. The nuclear area analysis showed a 100% specificity in discriminating Normal from Altered cells (either Reactive or AGC), whereas the nuclear perimeter analysis showed a lower specificity (87.5%). Both nuclear area and perimeter variability analysis resulted in high specificity values in distinguishing Reactive cells from AGC. Therefore, a stepwise two-step algorithm using nuclear areas to discriminate Normal from Altered cells, and nuclear area variability to distinguish Reactive from AGC, allowed us to reliably classify the cells into these three categories. The morphometric analysis of nuclear area is a valuable and reliable aid in AGC diagnosis and standardization, easily integrable into common automatic algorithms.

Morphological difference between pleural mesothelioma cells in effusion smears with either BAP1 loss or 9p21 homozygous deletion and reactive mesothelial cells without the gene alterations.

We previously characterized the morphological characteristics of malignant pleural mesothelioma (MPM) cells with 9p21 homozygous deletion (HD) using a combination of the virtual microscopy and fluorescence in situ hybridization (FISH). In this study, we investigated whether MPM cells with BRCA1-associated protein 1 (BAP1) loss show the same morphological characteristics identified in MPM cells with 9p21 HD. MPM cells with either BAP1 loss detected by immunocytochemistry (ICC) or 9p21 HD detected by FISH were identified via virtual microscopy prior to ICC or FISH, followed by analysis and quantification of their morphological characteristics. MPM cells with BAP1 loss or 9p21 HD exhibited significantly more frequent cell-in-cell engulfment, multinucleation, and larger multicellular clusters composed of more than 10 cells than reactive mesothelial cells. In conclusion, MPM cells with BAP1 loss or 9p21 HD share similar cytological features, indicating that the same morphological criteria can be used to detect MPM cells harboring such genetic aberrations.

Cell block processing is optimal for assessing endoscopic ultrasound fine needle aspiration specimens of pancreatic mucinous cysts

AimsThe cell block technique for assessing endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) specimens from pancreatic mucinous cystic lesions (MCLs) was systematically evaluated for the first time, including comparisons with three...

Detection and isolation of disseminated tumor cells in bone marrow of patients with clinically localized prostate cancer.

Disseminated tumor cells (DTCs) have been reported in the bone marrow (BM) of patients with localized prostate cancer (PCa). However, the existence of these cells continues to be questioned, and few methods exist for viable DTC isolation. Therefore, we sought to develop novel approaches to identify and, if detected, analyze localized PCa patient DTCs. We used fluorescence-activated cell sorting (FACS) to isolate a putative DTC population, which was negative for CD45, CD235a, alkaline phosphatase, and CD34, and strongly expressed EPCAM. We examined tumor cell content by bulk cell RNA sequencing (RNA-Seq) and whole-exome sequencing after whole genome amplification. We also enriched for BM DTCs with α-EPCAM immunomagnetic beads and performed quantitative reverse trancriptase polymerase chain reaction (qRT-PCR) for PCa markers. At a threshold of 4 cells per million BM cells, the putative DTC population was present in 10 of 58 patients (17%) with localized PCa, 4 of 8 patients with metastatic PCa of varying disease control, and 1 of 8 patients with no known cancer, and was positively correlated with patients' plasma PSA values. RNA-Seq analysis of the putative DTC population collected from samples above (3 patients) and below (5 patients) the threshold of 4 putative DTCs per million showed increased expression of PCa marker genes in 4 of 8 patients with localized PCa, but not the one normal donor who had the putative DTC population present. Whole-exome sequencing also showed the presence of single nucleotide polymorphisms and structural variants in the gene characteristics of PCa in 2 of 3 localized PCa patients. To examine the likely contaminating cell types, we used a myeloid colony formation assay, differential counts of cell smears, and analysis of the RNA-Seq data using the CIBERSORT algorithm, which most strongly suggested the presence of B-cell lineages as a contaminant. Finally, we used EPCAM enrichment and qRT-PCR for PCa markers to estimate DTC prevalence and found evidence of DTCs in 21 of 44 samples (47%). These data support the presence of DTCs in the BM of a subset of patients with localized PCa and describe a novel FACS method for isolation and analysis of viable DTCs.

Evaluation of Occupational Exposure To Xylene in Petroleum Workers by Assessing Urinary Methyl Hippuric Acid and Cellular Changes of Exfoliated Epithelial Cells in Buccal Mucosa Smears

Xylene is an important component of petrol and a widely distributed environmental contaminant. About 98% of Xylene is derived from the petrochemical and petroleum refining industries. When human get expose to xylene, which is one of the major Geno toxicants, may be associated with a range of acute/chronic diseases and cancer still literature is not available. Taking into our mind that occupational exposure to such derivatives may possess genotoxic risk or not. Hence our study aims to investigate and correlate the cellular changes in exfoliated oral epithelial cells oral smears with urinary methyl hippuric acid level estimation in petrol pump workers and to identify the significant role of xylene on oral mucosa. Materials and Methods: Urine samples and oral buccal mucosa smears were collected from 30 healthy individual (control) and 30 petroleum pump workers (case) working in petroleum station who are above 18 years of age. The urine was collected before exposure/work shift and after completion of work shift. The urinary methyl hippuric acid (MHA) level was analyzed by using Shimadzu UV-Visible Spectrophotometer procedure. The smeared slides were stained with PAP stain and analyses the cytomorphometric changes of exfoliated epithelial cells by using Axio Vision SE64 Rel 4.9.1. Ink Software. Results: The urinary Methyl hippuric acid level was substantially higher in cases than in controls (p<0.001). The Micronuclei (MN) frequency was drastically increased in cases than in controls and was statistically highly significant (P<0.0001). The frequency of MN gradually increased along with increased urinary MHA level in petroleum pump workers (case). Conclusion: The urinary MHA level and MN frequency is a useful index to recognize the occupational exposure to the petroleum product. Thus, our study emphasizes that appropriate precaution and regular biomonitoring must be taken among petrol pump workers which shall help to reduce their potential levels of risk associated with the occupation.

Acute Myeloid Leukemia After Treatment of Early Breast Cancer: Case Report and Literature Review

Therapy-related acute myeloid leukemia has been widely accepted for decades as one of the most serious long-term complications of treatment for early breast cancer. We report the case of a patient with therapy-related acute myeloid leukemia that may have been caused by regional radiation, chemotherapy, or endocrine therapy after surgery. A 35-year-old female was diagnosed 7 years ago as having stage IIA left breast cancer; she was treated by conservative surgery, followed by chemotherapy, radiotherapy, and endocrine therapy for 5 years. After 1 year of active follow-up, she presented with complaints of mild fever, weakness, loss of appetite, weight loss, and multiple bruises all over the body. Complete blood count revealed a hemoglobin level of 4 g/dl and total leukocyte count of 40,000 cells/mm3, with multiple blast cells in peripheral smear. Bone marrow biopsy and immunophenotyping established the diagnosis as AML. Before starting the treatment, she died following multiple organ failure.

Recours au test virologique HPV dans la prise en charge des atypies glandulaires en Alsace entre 2014 et 2016

ObjectivesThe new recommendations by the National Institute of Cancer (January 2017) recommend the use of a complementary human papillomavirus (HPV) virologic test during the diagnosis of atypical glandular cells in pap smear. The aim of this study was the performance analysis of the HPV virologic test for the detection of significant histological cervical abnormalities (CIN2 or more) in case of atypical glandular cells before the new recommendations were published. MethodsWe performed a descriptive and retrospective cohort study in Alsace between January 2014 and December 2016. We have included, from the EVE-association database, the patients with atypical glandular cells in pap smear. ResultsIn total, 1074 patients had a pap smear with atypical glandular cells ; 0.18% of total pap smears. This study included 152 patients who had a HPV test. We observed 6 cases of CIN2 (3.9%) and 12 cases of CIN3 (7.9%). No in situ adenocarcinoma nor invasive carcinoma were detected. The sensitivity of the HPV test was 88.9% (95% CI: [0.65; 0.99]), the specificity was 65.9% (95% CI: [0.55; 0.76]), the positive predictive value was 34% (95% CI: [0.21; 0.49]) and the negative predictive value was 96.8% (95% CI: [0.89; 0.99]). ConclusionThe detection of HPV in atypical glandular cells seems to be powerful with an excellent negative predictive value but, because of moderate sensitivity and due to the risk of histologic lesion progression, the current recommendations should to be applied with care.

Diagnosing synovial sarcoma by fine‐needle aspiration cytology and molecular techniques

Synovial sarcomas (SS) are rare soft tissue tumours defined by the SYT-SSX fusion gene. The tumours are composed of mesenchymal cells with varying degrees of epithelial differentiation. Cytomorphological descriptive studies are limited to small series and single cases. In this study we systematically examined the cytological features of SS diagnosed at our institution. SS diagnosed by fine-needle aspiration (FNA) cytology at our institution between 2006 and 2018 were reviewed by a panel of senior cytopathologists. Clinical and cytopathological characteristics were categorised and described. A total of 38 SS FNAs were identified from 35 patients. The cytomorphology was uniform, presenting as highly cellular smears of clusters and individual cells with mixed round, oval and spindle cells. We frequently observed pericapillary arrangement and occasionally pink background stroma was seen. Glandular formation or epithelial components were identified in the majority of cases which on histology were subtyped as biphasic SS. Pleomorphism and mitoses were rare. Immunocytochemical analysis was frequently positive for vimentin, epithelial membrane antigen, Bcl2 and, in recent cases, TLE1. Pan-cytokeratins and CK7 could occasionally be positive in biphasic cases. The diagnostic SYT-SSX fusion gene was detected in all FNA specimens using polymerase chain reaction or fluorescence in situ hybridisation. SS have distinct and uniform cytopathological features. Molecular genetic analysis for SYT-SSX are invaluable for diagnosing SS with FNA and should be implemented in cytopathological laboratories that routinely perform soft tissue diagnostics.

Association of Ureaplasma diversum with reproductive disease in cattle

ABSTRACTAims: To examine the association between the detection of Ureaplasma diversum in vaginal swabs from dairy cows in north western Spain with the diagnosis of granular vulvovaginitis (GVV) and reproductive performance, and the association with subclinical endometritis (SE) in slaughterhouse material. The presence of this microorganism in cases of abortion was also investigated.Methods: From 106 dairy farms in the province of Lugo, 40 herds were randomly selected. Vaginal swabs were obtained from 10 randomly selected cows per farm, then pooled for analysis to detect the presence of U. diversum by quantitative real-time PCR (qPCR). In five of these herds samples from the 10 animals were individually tested for U. diversum, and the presence of GVV lesions and their reproductive efficiency (number of inseminations to achieve pregnancy over two subsequent pregnancies) were determined. Vaginal swabs from uteri of cattle obtained at a slaughterhouse (n = 100) were tested for U. diversum and the presence of SE, defined as >5% polymorphonuclear cells in cytobrush smears, was determined. Sixteen farms with abortion problems submitted samples for culture and PCR testing including for U. diversum.Results: Of the 40 herds, 39 (98%) tested positive for U. diversum. On the five farms, 25/50 (50%) cows tested positive for U. diversum, and more cows with GGV-lesions (16/25; 64%) tested positive than cows without lesions (9/25; 36%) (p = 0.047). There were more cows with poor reproductive efficacy that tested positive (8/11; 57%) than tested negative (3/17; 18%) for U. diversum (p = 0.029). Of the 100 uteri, five tested positive for U. diversum and there were more uteri with SE that tested positive (3/19; 16%) than uteri without SE (2/81; 2%) (p = 0.036). U. diversum was also diagnosed in 4/16 farms with abortion problems and liver appeared to be the best tissue for detecting U. diversum DNA in the fetuses analysed.Conclusions and Clinical Relevance: Infection with U. diversum was present in most of herds investigated and it was statistically associated with GVV, SE and poor reproductive performance. It was also detected in abortions and the liver may also be an additional tissue to be considered in the diagnosis of U. diversum abortion by PCR. The possible association with different diseases in the same area suggests that different presentations should be considered when studying the implications of U. diversum on the reproductive diseases of cattle.