Histopathology Of Carcinoma Research Articles

Serum Exosomal MiR-874 as a Potential Biomarker for Nonsmall Cell Lung Cancer Diagnosis and Prognosis

AbstractLung cancer, primarily nonsmall cell lung cancer (NSCLC), is a leading cause of cancer-related fatalities globally. Due to late detection, the 5-year survival rate for NSCLC remains low. Therefore, the current research aimed to assess the diagnostic and prognostic value of serum exosomal miR-874 levels in NSCLC patients. This study involved 161 NSCLC patients and 80 control subjects. Blood samples were collected from all participants, and serum exosomal MiR-874 levels were quantified using quantitative reverse transcription-polymerase chain reaction. The study revealed a significant decrease in MiR-874 levels among NSCLC patients compared to controls. The receiver operating characteristic analysis demonstrated the diagnostic value of serum exosomal MiR-874 in effectively distinguishing NSCLC patients from controls.Furthermore, associations were observed between serum exosomal MiR-874 expression and adverse clinical factors such as young age, male sex, smoking, high tumor grade, squamous cell carcinoma histopathology, advanced tumor stage, and lymphatic involvement. Patients with high levels of MiR-874 had significantly longer overall and disease-free survival compared to those with lower levels. The study demonstrates that levels of serum exosomal miR-874 are considerably lower in NSCLC patients, indicating its potential as a diagnostic biomarker. The study's findings suggest that the expression of MiR-874 may predict the prognosis of NSCLC patients based on clinical features.

Challenges in early detection and endoscopic resection of esophageal cancer: There is a long way to go.

The publication by Qu et al provided a comprehensive discussion about the epidemiology, etiology, histopathology, early detection, and endoscopic treatment of esophageal carcinoma (EC) and summarized the progress in the advanced technologies for screening and endoscopic resection for EC. In this editorial, we will provide deeper insight into the challenges that hinder practical application of these advanced technologies along with the role of these technologies in upper endoscopy quality. More efforts need to be made to overcome the challenges and add the value of these technologies in upper endoscopy quality. Clinical outcomes of management strategies after noncurative endoscopic dissection for early EC patients need further investigation. The experiences with noncurative endoscopic resection of other organs may have certain implications for noncurative resection of early EC.

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence's Pioneering Role in Colorectal Carcinoma Histopathology.

Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.

Clinical and Bronchoscopy Assessment in Diagnosing the Histopathology Type of Primary Central Lung Tumors.

The location and type of a tumor influence the prognosis of lung cancer. Primary Central Lung Tumors (PCLTs) are correlated with poor prognoses and certain histologic types. This study aimed to present a comprehensive exploration of clinical and bronchoscopic assessments for diagnosing the histopathology types of PCLTs and identified the factors associated with certain histologic types. This was an observational cross-sectional study of PCLTs, defined as tumors in direct contact with hilar structures or located within the inner two-thirds of the hemithorax. We gathered demographic and clinical data, as well as data on bronchoscopy assessment and histopathology type. Tumor stage, symptoms of superior vena cava syndrome, and enlargement of lymph nodes in the paratracheal and subcarinal regions were also documented. Of the 895 patients, 37.87% had primary lung tumors, with 17.76% classified as PCLTs. Notably, PCLT cases exhibited a higher proportion of stage III (28.9% vs. 18.3%; p = 0.03) and Squamous Cell Carcinoma (SCC) histopathology (37.1% vs. 17.2%; p = 0.00) compared with non-PCLT cases. Bronchoscopic findings in PCLTs revealed a predilection for central airway masses (25.2%) and compressive distal airway stenosis (25.2%). Subgroup analysis of 159 PCLT cases identified 37.10% as SCC. Multivariate analysis underscored that intraluminal masses predict central SCC (odds ratio 2.075, 95% confidence interval 1.07-3.99; p = 0.028). The proportion of stage III, SCC histopathological type, and intraluminal lesions was higher in patients with PCLT than in non-PCLT cases. The presence of intraluminal lesions can predict the histopathological type of SCC in patients with PCLTs.

PD-L1 Immunoexpression on Cytology and its Paired Comparison with Histopathology in Lung Carcinoma

PD-L1 Immunoexpression on Cytology and its Paired Comparison with Histopathology in Lung Carcinoma

Categorization of Breast Carcinoma Histopathology Images by Utilizing Region-Based Convolutional Neural Networks

Categorization of Breast Carcinoma Histopathology Images by Utilizing Region-Based Convolutional Neural Networks

Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images

BackgroundApplying deep learning to digital histopathology is hindered by the scarcity of manually annotated datasets. While data augmentation can ameliorate this obstacle, its methods are far from standardized. Our aim was to systematically explore the effects of skipping data augmentation; applying data augmentation to different subsets of the whole dataset (training set, validation set, test set, two of them, or all of them); and applying data augmentation at different time points (before, during, or after dividing the dataset into three subsets). Different combinations of the above possibilities resulted in 11 ways to apply augmentation. The literature contains no such comprehensive systematic comparison of these augmentation ways.ResultsNon-overlapping photographs of all tissues on 90 hematoxylin-and-eosin-stained urinary bladder slides were obtained. Then, they were manually classified as either inflammation (5948 images), urothelial cell carcinoma (5811 images), or invalid (3132 images; excluded). If done, augmentation was eight-fold by flipping and rotation. Four convolutional neural networks (Inception-v3, ResNet-101, GoogLeNet, and SqueezeNet), pre-trained on the ImageNet dataset, were fine-tuned to binary classify images of our dataset. This task was the benchmark for our experiments. Model testing performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Model validation accuracy was also estimated. The best testing performance was achieved when augmentation was done to the remaining data after test-set separation, but before division into training and validation sets. This leaked information between the training and the validation sets, as evidenced by the optimistic validation accuracy. However, this leakage did not cause the validation set to malfunction. Augmentation before test-set separation led to optimistic results. Test-set augmentation yielded more accurate evaluation metrics with less uncertainty. Inception-v3 had the best overall testing performance.ConclusionsIn digital histopathology, augmentation should include both the test set (after its allocation), and the remaining combined training/validation set (before being split into separate training and validation sets). Future research should try to generalize our results.

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models' accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models' effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.

Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning

Oral cancer is a prevalent malignancy that affects the oral cavity in the region of head and neck. The study of oral malignant lesions is an essential step for the clinicians to provide a better treatment plan at an early stage for oral cancer. Deep learning based computer-aided diagnostic system has achieved success in many applications and can provide an accurate and timely diagnosis of oral malignant lesions. In biomedical image classification, getting large training dataset is a challenge, which can be efficiently handled by transfer learning as it retrieves the general features from a dataset of natural images and adapted directly to new image dataset. In this work, to achieve an effective deep learning based computer-aided system, the classifications of Oral Squamous Cell Carcinoma (OSCC) histopathology images are performed using two proposed approaches. In the first approach, to identify the best appropriate model to differentiate between benign and malignant cancers, transfer learning assisted deep convolutional neural networks (DCNNs), are considered. To handle the challenge of small dataset and further increase the training efficiency of the proposed model, the pretrained VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, are fine-tuned by training half of the layers and leaving others frozen. In the second approach, a baseline DCNN architecture, trained from scratch with 10 convolution layers is proposed. In addition, a comparative analysis of these models is carried out in terms of classification accuracy and other performance measures. The experimental results demonstrate that ResNet50 obtains substantially superior performance than selected fine-tuned DCNN models as well as the proposed baseline model with an accuracy of 96.6%, precision and recall values are 97% and 96%, respectively.

Clinicopathological Profile of Non-small Cell Lung Cancer and the Changing Trends in Its Histopathology: Experience From a Tertiary Care Cancer Center in Kashmir, India.

Background The overall frequency and incidence of different cancers across the globe, including lung cancer, are marked by ethnic and geographical variations. Lung cancer is the most commonly diagnosed cancer worldwide that inflicts most of the cancer deaths. Non-small cell lung cancer (NSCLC) constitutes mostlung cancer cases. The aim of this study was to find the frequency and clinicopathological characteristics of NSCLC in high incidence zone of Kashmir, an ethnically and geographically distinct area in Northern India. Material and methods The study was conducted to evaluate the clinicopathological profile of NSCLC at a tertiary care cancer center,Sher-I-Kashmir Institute of Medical Sciences (SKIMS). The patients and case records were analyzed for clinical presentation and demographic features, smoking status, radiological features, histopathological type, and stage of their disease at presentation. Results The study included 1557 NSCLC patientsregistered over a period of seven years, i.e., 2008-2014. Most of the patients belonged to rural areas (70%).The median age of the whole cohort was 58.0 years (ranges 22-95 years), and the male-to-female ratio was 3.7:1 (male = 1231 and female = 326). Smokers comprised 77.39% of cases, and Hookah was the most common form of smoking (65.06%). The ratio of squamous cell carcinoma and adenocarcinoma was 3.7:1 (67.5% vs. 24.9%). Stage III and IV disease accounted for 93% of cases (30.6% and 62.7%, respectively). Squamous cell carcinoma histopathology was dominant in smokers (74.3%) compared to adenocarcinoma (19%), while squamous cell carcinoma and adenocarcinoma histopathology ran parallel in nonsmokers (45.1% and 44%, respectively). Most of the patients had an Eastern Cooperative Oncology Group (ECOG) performance status between 1 and 2 (79%). Conclusion We conclude Kashmir region is a very high-risk area for lung cancer, with NSCLC showing a high incidence. Most of our patients present in advanced stages, and the frequency of adenocarcinoma is showing an increasing trend over the years from 2008-2014.

Development of an adenosquamous carcinoma histopathology - selective lung metastasis model.

Preclinical tumor models with native tissue microenvironments provide essential tools to understand how heterogeneous tumor phenotypes relate to drug response. Here we present syngeneic graft models of aggressive, metastasis-prone histopathology-specific NSCLC tumor types driven by KRAS mutation and loss of LKB1 (KL): adenosquamous carcinoma (ASC) and adenocarcinoma (AC). We show that subcutaneous injection of primary KL; ASC cells results in squamous cell carcinoma (SCC) tumors with high levels of stromal infiltrates, lacking the source heterogeneous histotype. Despite forming subcutaneous tumors, intravenously injected KL;AC cells were unable to form lung tumors. In contrast, intravenous injection of KL;ASC cells leads to their lung re-colonization and lesions recapitulating the mixed AC and SCC histopathology, tumor immune suppressive microenvironment and oncogenic signaling profile of source tumors, demonstrating histopathology-selective phenotypic dominance over genetic drivers. Pan-ERBB inhibition increased survival, while selective ERBB1/EGFR inhibition did not, suggesting a role of the ERBB network crosstalk in resistance to ERBB1/EGFR. This immunocompetent NSCLC lung colonization model hence phenocopies key properties of the metastasis-prone ASC histopathology, and serves as a preclinical model to dissect therapy responses and metastasis-associated processes.

CLINICOPATHOLOGICAL CHARACTERISTICS OF THYROID NEOPLASTIC LESION AT SANGLAH GENERAL HOSPITAL DENPASAR BALI 2016-2020 PERIOD

Thyroid neoplastic lesion can be either benign or malignant/cancerous. Thyroid cancer is a type of cancer that is increasing worldwide and is the most common endocrine gland malignant in Indonesia. The purpose of this study is to determine the clinicopathological characteristics of thyroid neoplastic lesion at Sanglah General Hospital Denpasar Bali for the 2016-2020 period based on age, sex, histopathological type, and location of lesion. This research is a descriptive study with a cross sectional approach. The data used are secondary data of patients with thyroid neoplastic lesion that registered for histopathological examination at the Anatomical Pathology Laboratory of Sanglah Hospital Denpasar in 2016-2020. The data collection technique used was total sampling which result in of 407 data meeting the inclusion criteria. The results show that the most common thyroid neoplastic lesion is found in the age group of 41-50 years (25.3%), female sex (77.4%), histopathological type of papillary thyroid carcinoma (79.9%), and location of the lesion is multiple (combination of two or more locations) as much as 36.9%. The conclusions that can be drawn from this study are thyroid neoplastic lesion patients at Sanglah General Hospital Denpasar for the period 2016-2020 have the most clinicopathological characteristics aged 41-50 years, female sex, papillary thyroid carcinoma histopathology, and multiple lesion locations. The results of this study can be developed and become the basis for other research with more varied variables or other research methods such as analytical research.
 Keywords: Thyroid Neoplastic Lesion, Clinicopathological Characteristics, Histopathological Type

Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing.

Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data. We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care. We identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85. Using an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.

The Relationship of iTILs (Intratumoral Infiltrating Lymphocytes) and sTILs (Stromal Infiltrating Lymphocytes) with SOX2 (Sex-Determining Region Y (SRY)-Box2) Expression in Nasopharyngeal Carcinoma

Background: Nasopharyngeal carcinoma (NPC) is a type of malignant head and neck tumor originating from the epithelial lining of the nasopharynx which is commonly found in Asian countries, including Indonesia. SOX2 is part of SOX. The SOX gene family first emerged with the discovery of the mammalian testicular determinant, Sry. SOX2 expression in NPC is promoted by transcription factors such as activation protein 2 (AP-2), homeobox protein 1 (Prox1) and Pax6. TILs can be used systematically to evaluate a patient's anti-cancer immunological function that can be a valuable prognostic factor. Research Objectives: To determine the relationship between the degree of iTILs (intratumoral infiltrating lymphocytes) and sTILs (stromal infiltrating lymphocytes) with the immunohistochemical expression of SOX2 (sex-determining region Y (SRY)-box 2) in the histopathology of nasopharyngeal carcinoma. Materials and Methods: 43 samples in the form of a histopathological slide from biopsy/operative tissue diagnosed with nasopharyngeal carcinoma were collected. Stromal and intratumoral TILs were evaluated by the researcher and two anatomic pathologists, then immunohistochemical staining of SOX2 was performed and the expression was assessed. Spearman correlation test was performed to analyse between variables. Results: Based on the Spearman?s correlation test, there was no significant relationship between the degree of iTILs and SOX2 expression (p= 0.892) and there was a significant relationship between the degree of sTILs and SOX2 expression (p= 0.003). Conclusion: This study shows no significant relationship between the degree of iTILs and SOX2 expression, but there was a significant relationship between the degree of sTILs and SOX2 expression.

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images

Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

Re-Irradiation of Recurrent Nasopharyngeal Cancer: A 4-year Follow-up Study of Treatment using 3-Dimensional Conformal Radiotherapy

Introduction: Nasopharyngeal cancer (NPC) is a prevalent diagnosis of head and neck cancers (HNC) in Asia. In Indonesia, it is estimated that there are 12,000 new cases per year. Case Presentation: The author reports a 25-year-old male patient with recurrent NPC. This patient was first diagnosed with NPC stage IVB in 2013 and was treated by chemoradiation. In 2016, he found another mass in his neck. The biopsy result showed the recurrence of the NPC with histopathology of Undifferentiated carcinoma (WHO type III). Re-irradiation was planned, and the patient subsequently received 3D-Conformal Radiotherapy (3D-CRT) at our department. Four years after the completion of re-irradiation, the patient was alive and well with no signs of recurrence but still complained about late toxicities like trismus and fibrotic neck. Conclusions: In this study, we discuss the use of 3D-CRT in the re-irradiation of NPC with its limitation on obtaining optimum dose sculpture compared to more sophisticated and widely spread modalities like intensity-modulated radiotherapy (IMRT). However, with careful planning, we can still obtain optimum tumor dose, minimize organs at-risk (OAR) dose, and subsequently late toxicities that come after. We hope that this study can bring hope to centers with limited facilities, and we suggest further studies on re-irradiation, especially in OAR dose tolerance guidelines.

The Association Between Clinical Characteristics and Histopathology of Nasopharyngeal Carcinoma At Dr. Mohammad Hoesin General Hospital, In 2019-2020

Introduction. Nasopharyngeal carcinoma is a cancer originating from the nasopharyngeal epithelium. Clinical characteristics such as age, gender, and risk factors are believed to influence the incidence of this cancer. Patients suffering from nasopharyngeal cancer may have certain clinical symptoms and provide certain histopathological features. Methods. The method used in this research were analytic observational with a cross sectional design using secondary data in the form of medical records from the Department of Anatomical Pathology section and the Central Medical Record section of RSUP Dr. Mohammad Hoesin Palembang as the research subject. Samples obtained 64 samples, then managed using the SPSS application. Results. The highest age group was 35-55 years old, which amounted to 44 people (68.8%). According to gender, the most common were males with 53 people (82,8%). According to clinical symptom, the most common were non-epistaxis with 33 people (51.6%). According to risk factors, the most were smokers with 36 people (56.3%). According to histopathology, the most common features were differentiated non-keratinized squamous cell carcinoma with 40 people (62,5%). From the statistical test, it resulted that p > 0,05 for all variables (age, gender, clinical symptoms, and risk factors) that associated with histopathology of nasopharyngeal carcinoma. Conclusion. There was no significant association found between clinical characteristics and histopathology of nasopharyngeal carcinoma.

HISTOPOTHOLOGICAL STUDY OF ENDOMETRIALCARCINOMA IN WOMEN WITH POSTMENOPAUSAL BLEEDING

Postmenopausal bleeding represents one of the most common reasons for referral to gynecological services, largely due to suspicion of an underlying endometrial malignancy. The objective of this study is to find the prevalence of endometrial carcinoma in women with post-menopausal bleeding and to study the histopathology of endometrial carcinoma. Materials And Methods:A three and half years retrospective (May 2010 to Oct 2013) and one and half year prospective study (Nov 2013 to April 2015) of all D&C and hysterectomy specimens of patients presenting with postmenopausal bleeding were undertaken in the department of Pathology, KIMS Hubballi. Results:In our study out of 122 cases of postmenopausal bleeding there were 22 cases of endometrial carcinoma. In 22 cases 21 were endometrial adenocarcinoma 1 case was carcinosarcoma. Majority were in 56 to 65 years. There were various histopathological patterns showing endometriatype, endometroid type with papillary differentiation, squamousdifferentiation, mucinous differentiation and 1 case showingcarcinosarcoma. Majority ,18 cases (85.7%) were in Grade I, 2 cases (9.5%) were in Grade II and 1 case was in Grade III. Out of 22 cases 15 cases were hysterectomy specimen,7 cases were curettage samples. In 15 cases ,2 cases were confined to uterus (Stage IA),9 cases (60.6%) were stage IB,1 cases were stage IC.There was 1 case in in stage IIA with involvement of endocervicalmucosa, there were 2 cases in stage III with each showing involvement of bilateral ovaries(Stage IIIA) and bilateral pelvic lymph nodes (Stage IIIC).In our study there were 1cases of carcinosarcoma in stage IB in the age group 55 years. Hence postmenopausal bleeding is a dreaded and alarming symptoms and requires complete evaluation in order to ensure the absence of malignancy and to identify and treat high risk patients.

Histopathology of hepatocellular carcinoma - when and what

When do you need to take biopsies of the liver, and what information will you get is the topic of this review on hepatocellular carcinoma (HCC). If, clinically, the differential diagnosis of HCC after imaging is suggested, a biopsy has become obligatory as a diagnostic confirmation of HCC in the non-cirrhotic liver prior to definitive therapeutic interventions, as well as in a palliative therapy concept. In the case of hepatic lesions with an uncharacteristic contrast uptake, a biopsy should be performed immediately to confirm the diagnosis of HCC. After diagnosing HCC, a treatment strategy is evaluated. Further, the biopsy, or in case of surgical treatment, the resected tissue, shows us the different subtypes of HCC, with the steatohepatitic subtype being the most common and the lymphocyte-rich subtype being the least common. Further, the histological grade of HCC is determined according to the grading system of the WHO or the Edmonson and Steiner System. Through biopsies, HCC can be differentiated from intrahepatic cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma or metastases of other malignant tumors, especially metastases of the gastrointestinal tract. In summary, biopsies are fundamental in the diagnosis of HCC.

Relationship between Transvaginal Ultrasound Endometrial Volume, Body Mass Index and Endometrial Pathology in Women with Postmenopausal Bleeding

Abstract Background Menopause is recognized to have occurred after one year of amenorrhea, for which there is no other obvious pathological or physiological cause. Perimenopause should include the period immediately prior to the menopause (when the endocrinological, biological and clinical features of approaching menopause commence) and the first one year after menopause. Objectives The aim of the study is classification of patients into those with benign endometrial pathology and those with endometrial hyperplasia or carcinoma using endometrial volume and BMI. Patients and Methods This observational cross sectional study was conducted at the Department of Obstetrics and Gynecology in Ain Shams University Hospital from March 2019 till January 2020. The population of this study was 100 menopausal women with postmenopausal bleeding ≥12 months and endometrial thickness by TVUS ≥5 mm. Results According to histopathology of endometrial carcinoma, statistical analysis of our data revealed that age, menopausal duration and BMI were significantly highest. Parity was significantly lowest. Endometrial volume was significantly highest in carcinoma (7.9±2.9 cc). Age, menopausal duration, parity and endometrial volume had significant moderate diagnostic performance in predicting endometrial carcinoma but BMI had significantly low. Age ≥ 62.0, menopausal duration ≥ 11.0 and parity ≤3.0 had low diagnostic characteristics in diagnosis of endometrial carcinoma but endometrial volume ≥ 6.0 had high sensitivity but low other diagnostic characteristics in the diagnosis of endometrial carcinoma. According to histopathology of endometrial hyperplasia, statistical analysis of our data revealed that age, menopausal duration, BMI, parity and endometrial volume had no significant diagnostic performance in the diagnosis of endometrial hyperplasia. Conclusion In our study, analysis of data revealed that the using of transvaginal measurement of endometrial volume is the best predictor of endometrial cancer with a positive correlation with BMI.