The current study was conducted to see the frequency of epithelial malignancies of endometrium with focus on the common diagnostic pitfalls and identify morphological and immunohistochemical markers helpful in the differential diagnosis between different subtypes. It is a retrospective descriptive study carried out on 52 specimens of endometrial tumors received in Fatima Memorial Hospital, Lahore, Pakistan, during three years (2010–2012). Patients were divided into 5 age groups: <40, 41–50, 51–60, 61–70, and >70 yrs. Tissues were fixed in 10% formalin and processed and stained with haematoxylin-eosin. Stained slides were examined to determine the histological types by WHO classification, and immunohistochemistry for WT1, p53, ER/PR, and MIB1 was done in cases where morphology alone was not helpful in making a confirmed diagnosis. 80% of specimens were of endometrioid adenocarcinomas, 11% of serous tumors, 4% of clear cell carcinoma, and 4% of squamous cell carcinomas involving both cervix and endometrium. Most of the patients (28.84%) with endometrial carcinomas fall in the age range of 51–60 yrs. Endometrioid adenocarcinoma is the most common type of epithelial endometrial malignancies. Morphology is the keystone in the evaluation of these tumors, but immunohistochemistry can also be helpful in establishing the correct diagnosis.

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, with both a high malignant potential and poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance to leiomyosarcoma, often being accompanied by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three biomarkers (i.e., mitosis count, necrosis, and nuclear atypia). Among these biomarkers, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.

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Objective: To study the frequency of uterine leiomyoma with its risk factors. Material and Methods: A cross-sectional study was carried out between January 2020 to June 2020 including women who were diagnosed with uterine leiomyomas in the Department of Gynecology, Jinnah Postgraduate Medical Center (JPMC), Karachi. All the major risk factors including demographic details, family history, marital status, parity, use of oral contraceptives and co-morbids were studied in 300 females using a questionnaire. The data was analyzed with the statistical program Statistical Package for the Social Sciences (SPSS) version 21.0. Results: A total number of 300 patients having uterine leiomyomas were included in the study. The mean and standard deviation of the patients’ age was 42.52±7.98 and patients’ weight was 59.8±9.12. The mean and standard deviation of the patients’ BMI was 23.4±3.85 and patients’ height was 63.01±2.23. The demographic and clinical characteristics of the patients are presented in Table-I and II. Leiomyomas were reported mostly in women between the ages of 36-40 years (28%), followed by 46-50 years (20%) and 51-55 years (14.3%). 10% of the women reported hypertension as a co-morbid. 3.8% were both hypertensive and diabetic. 76.9% females presented with abnormal uterine bleeding and 69.8% had anemia. Most of the women with leiomyomas were para 3+ (36.9%) and para 3 (20.9%). 22.2% women were nulliparous. 72.2% women had a single fibroid and 21.6% had two or more fibroids. 5.7% women had a family history of fibroids. 5.4% women were using some form of hormonal contraception. Conclusion: Risk factors for uterine leiomyomas were identified and prevalence of these risk factors in patients with leiomyomas was observed. Keywords: Leiomyoma, Risk factors, Uterine fibroid.

Histopathology is the gold standard for diagnosis of cancers as well as many non- neoplastic diseases. Pakistan is a country of more than 220 million people and the fifth most populated country of the world. Unfortunately, it has a weak healthcare system in general and poor pathology services in particular. Till date, only 338 histopathologists have passed their fellowship examination in Pakistan; this has led to a very alarming situation considering the marked increase in the prevalence of cancer cases and other diseases which need histopathological interpretation. There are only 18 big histopathological labs in the country, the majority of which are located in major cities which further delays the diagnosis of patients who live in rural areas. Immediate steps are required for better histopathology services in the country. Adoption of digital tools may bridge the gaps of histopathology-practice and ensure consistency across the country.