Classification Of Ovarian Tumors Research Articles

An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images.

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models-KNN, logistic regression, SVM, decision tree, and random forest-resulted in an improved accuracy of 92.8% compared to single classifiers.

Histopathological Spectrum of Ovarian Tumours in a Tertiary Care Centre in South Kerala, India: A Cross-sectional Study

Introduction: Ovarian tumours are among the most common tumours in females, exhibiting diverse histopathological patterns that remain crucial for early diagnosis. Proper classification of ovarian neoplasms, particularly malignant ones, is essential for accurate treatment. Accurately subclassifying ovarian tumours aids in treatments such as targeted therapy. Aim: To examine the histopathological spectrum of ovarian tumours and to assess the role of histopathology in accurate diagnosis and treatment. Materials and Methods: A cross-sectional study on the histopathological spectrum of ovarian tumours was conducted in Department of Pathology, Travancore Medical College, Kollam, Kerala, India, over a five-year period (January 2017 to December 2021). The study included 850 ovarian specimens, and the various histopathological patterns were studied according to the World Health Organisation (WHO) classification of ovarian tumours, 5th edition, 2020. These patterns and age distribution were expressed in frequency and percentage. Results: Out of the 850 ovarian specimens, 140 were neoplastic and 710 were non neoplastic. Abdominal pain was the most common clinical presentation (30%). Among the 140 neoplastic cases, 115 were benign, 20 were malignant, and five were borderline. The majority of cases, including benign, borderline, and malignant tumours, were seen in the age group of 31-40 years (25.71%). Benign tumours were more common than malignant ones in all age groups. Categorising based on histopathological patterns, epithelial tumours were the most common (88 cases, 62.86%). Serous cystadenoma was the most common benign tumour, constituting 36 out of 115 benign cases (25.71%). Borderline serous tumour was the most common borderline epithelial tumour (3 out of total 5 borderline ovarian neoplasms, 2.14%). Serous cystadenocarcinoma was the most common malignant tumour (5 cases, 03.57%). Conclusion: The wide spectrum of ovarian tumours presents diagnostic challenges. Effective therapeutic management of ovarian malignant tumours continues to be a challenge for clinicians. Histopathological examination remains crucial in diagnosis. Accurate histopathological diagnosis, combined with clinical staging, facilitates prompt and appropriate treatment and timely patient management.

IOTA simple rules: An efficient tool for evaluation of ovarian tumors by non-experienced but trained examiners - A prospective study

ObjectivesA simple and efficient tool for evaluating ovarian tumors in general hospitals where radiologists without experience in gynecological ultrasound is necessary. This study aims to evaluate the diagnostic performance of IOTA simple rules in initial classification of ovarian tumors by non-experienced examiners who have received simple training. Materials and methodA prospective single-center study was conducted at Hanoi Obstetrics and Gynecology Hospital. Three resident gynecologists trained themselves for two weeks and then received hands-on practice under the supervision of experts for another two weeks. The examiners performed ultrasound on 424 eligible women scheduled for surgery for ovarian tumors and classified the tumors based on IOTA simple rules. The postoperative pathology of ovarian tumors was used as the gold standard. Results90.8 % (385/424) of the tumors were benign. Simple rules were applicable in 399/424 (94.1 %) tumors, with a sensitivity of 84.8 % (95 % CI, 70.2–94.3), specificity of 98.9 % (95 % CI, 97.5–99.7), positive predictive value of 87.5 % (95 % CI, 73.3–95.9), and negative predictive value of 98.6 % (95 % CI, 97.1–99.5). The sensitivity of IOTA simple rules was higher in postmenopausal women (91.7 % vs. 81.0 %), while the specificity was higher in premenopausal women (99.4 % vs. 95.8 %). Accuracy was 100 % in all ten pregnant women were assessed using these rules. ConclusionIn conclusion, in the hands of non-expert examiners who were trained thoroughly, IOTA simple rules are a simple and efficient tool for clinical practice in centers where expert radiologists in gynecology are not always available. The training program is simple and could be applied widely in other clinical centers. Further studies are necessary to evaluate the effectiveness of the IOTA simple rules in assessing ovarian tumors among pregnant women.

Current uptake and barriers to wider use of the International Ovarian Tumor Analysis (IOTA) models in Dutch gynaecological practice

ObjectiveCorrect referral of women with an ovarian tumor to an oncology department remains challenging. The International Ovarian Tumor Analysis (IOTA) consortium has developed models with higher diagnostic accuracy than the alternative Risk of Malignancy Index (RMI). This study explores the uptake of the IOTA models in Dutch hospitals and factors that impede or promote implementation. Optimal implementation is crucial to improve pre-operative classification of ovarian tumors, which may lead to better patient referral to the appropriate level of care. Study DesignIn February 2021, an electronic questionnaire consisting of 37 questions was sent to all 72 hospitals in the Netherlands. One pre-selected gynaecologist per hospital was asked to respond on behalf of the department. ResultsThe study had a response rate of 93% (67/72 hospitals). All respondents (100%) were familiar with the IOTA models with 94% using them in practice. The logistic regression 2 (LR2)-model, Simple ultrasound-based rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model were used in respectively 40%, 67% and 73% of these hospitals. Respondents rated the models overall with an 8.2 (SD 1.8), 8.3 (SD 1.6) and 8.9 (SD 1.3) respectively for LR2, SR and ADNEX on a scale from 1 to 10. Moreover, 89% indicated to have confidence in the results of the IOTA models. The most important factors to improve further implementation are more training (43%), research on sensitivity, specificity and cost-effectiveness in the Dutch health care system (27%), easier usability (24%) and more consultation time (19%). ConclusionThe IOTA ultrasound models are adopted in the majority of Dutch hospitals with the ADNEX model being used the most. While Dutch gynecologists have a strong familiarity and confidence in the models, the uptake varies in reality. Areas that warrant improvement in the Dutch context are more uniformity, education and more research. These findings can be helpful for other countries considering adopting the IOTA models.

A novel variant of deep convolutional neural network for classification of ovarian tumors using CT images

Deep Learning models have shown tremendously impressive performance on image classification tasks. In the medical imaging domain, progress has been made in obtaining high-quality data for analysis and using state-of-the- art artificial intelligence algorithms for solving complex problems and providing answers to key questions using data. One such problem that is of crucial importance and interest to medical researchers is to classify tumors into two categories benign and malignant. This research work focuses on proposing a novel variation of CNN architecture and a comparison of the performances of state-of-the-art ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winning architectures for the task of classifying ovarian tumors by training and evaluating images on a dataset of ovarian CT scan images with the help of cloud services such as Google Cloud Platform. The proposed architecture has attained an accuracy of 97.53% and outperformed the existing CNN variants.

Histomorphological Analysis of Ovarian Neoplasms According to the 2020 WHO Classification of Ovarian Tumors: A Distribution Pattern in a Tertiary Care Center.

In 2018, ovarian carcinoma ranked as the eighth most common cancer diagnosed and the leading cause of cancer death in women. High-grade serous carcinoma is the most common histological type seen among malignant cases. A diverse group of neoplasms is seen in the ovary with variable clinical, morphological, and histological features, so assessing the nature of ovarian neoplasms further assists in the treatment of the disease. This study was conducted to assess the different histopathological variants of ovarian neoplasms according to the latest 2020 World Health Organization (WHO) classification of ovarian tumors. Further analysis of the frequency, age, clinical features in patients, and distribution of various ovarian tumors is assessed. A retrospective study was conducted in the Department of Pathology at Patna Medical College and Hospital (PMCH), Patna. The data of the patients from the past three years, from February 2020 to February 2023, were retrieved and assessed. Gross and microscopic findings, including clinical details of patients with ovarian masses, were analyzed from the previous records. A total of 110 cases of ovarian neoplasms on histopathology were analyzed. The age range was 11-70 years. The types of specimens received were those of total abdominal hysterectomy, salphingoopherectomy, and unilateral or bilateral ovarian cystectomy. The most common presentation was an abdominal mass, followed by pain in the abdomen.The majority ofthe tumors were benign (69%), malignancy was observed in 24.5% of cases, and borderline tumors were seen in 5.4% of cases. Epithelial tumors were the commonest tumors, accounting for 70%, followed by germ cell tumors (21%). Serous cystadenoma was the commonest benign tumor, followed by mature teratoma and serous cystadenocarcinoma. High-grade serous cystadenocarcinoma was the commonest malignant ovarian tumor (9%), followed by low-grade serous cystadenocarcinoma (4.5%) and metastatic carcinoma of the ovary. Krukenberg tumor was seen in two cases, and a very rare case of sclerosing stromal tumor was seen in one of the cases. Ovarian neoplasms usually present with a variety of clinicomorphological and histological features. The most common neoplasm observed in the ovary is surface epithelial tumors, which are benign lesions that commonly affect reproductive age groups. Newer advancements like immunohistochemistry(IHC) and genetic studies have made the diagnosis easier and more precise. However, in institutes with limited resources, a histopathological study is still the gold standard in the diagnosis and prognostic evaluation of these tumors.

Ovarain Cancer Prediction in Early Stage Using Machine Learning Approaches

Ovarian cancer is a disorder of ovarian cell growth that is triggered by series of acquired mutations affecting a single cell or its clonal progeny. It is purposeless prey on host and virtually autonomous. It is usually diagnosed at a late stage because of poor sensitivity of screening test. There are still no effective cures for this illness. Still early detection might lower the mortality rate. Our project's major goal is to conduct predictive analytics for early detection by using machine learning models and statistical techniques on clinical data collected from specific patients. Mutual information testing is crucial in statistical analysis for identifying indicative biomarkers. A collection of machine learning models, such as the Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM), and Light Gradient Boosting Machine (LGBM)are utilized in the classificationof ovarian tumors as benign or malignant. By using proposed system, it can significantly identify the class of benign and malignant patients. The data collected is analyzed and pre-processed before it is used for model training and testing.

An inception‐ResNet deep learning approach to classify tumours in the ovary as benign and malignant

AbstractThe classification of tumours into benign and malignant continues to date to be a very relevant and significant research topic in the cancer research domain. With the advent of Computer Vision and rapid developments in the fields of deep learning, as well as medical devices and instruments, researchers are therefore utilizing the state‐of‐the‐art deep learning architectures to discover patterns in the medical image data and thereby use this information to detect tumours and classify them as benign or malignant. In this paper, we propose a custom state‐of‐the‐art deep learning architecture, the Inception‐ResNet v2 for the classification of ovarian tumours into the two categories of benign and malignant based on a custom dataset with a validation accuracy of 97.5% and a test accuracy of 67%. Furthermore, a quantum convolutional neural network (QCNN) was also implemented with an accuracy of 92% on the validation dataset.

Segmentation and classification of ovarian cancer based on conditional adversarial image to image translation approach

AbstractMedical image analysis and disease diagnosis have significantly improved with the use of AI and Machine Learning algorithms. Automated systems for medical image analysis will help the doctors and radiologists understand the anomaly in a short span of time and with better visualization. Such automated systems will help to reduce the time taken for diagnosis by experts. Recently, Computer Vision is industrialized with the advancements in algorithms and hardware. The proposed study aims to develop a computer vision solution for automatic segmentation and classification of ovarian tumours in discriminating between benign and malignant tumours by image‐to‐image translation approach using Conditional Generative Adversarial Network (cGAN). Our method uses a novel algorithm which segments and classifies the images in a single pipeline which makes the algorithm unique and useful. This research also aims to compare its diagnostic accuracy with that of an expert radiologist. The dataset used by in the present study is formulated with images obtained from a hospital and annotated by doctors from the hospital. The obtained results show the proposed study is promising for ovarian cancer segmentation and classification with an average segmentation score of 0.825 for benign and 0.765 for malignant and classification accuracy of 83% for benign and 79% for malignant, precision score of 85% for benign and 80% for malignant and F1 score of 81% for benign and 80.1% for malignant images. The proposed methodology is evaluated on the existing MRI images to perform segmentation and classification. The results obtained shows that the proposed methodology can perform well on other MRI images. In this study, proposed methodology is convenient as separate segmentation need not be done and is giving good result. The same MRI images are segmented using UNet and classified using RESNET 101 and results are compared with the proposed methodology.

A Machine Learning approach for classification of ovarian tumours

Ovarian tumours are abnormal growths in the ovaries which can be either cancerous or non-cancerous. Ovarian cancer is reported to cause more deaths than any other female reproductive cancers. In order to reduce the death rate, early diagnosis and treatment is required. In the recent days, Artificial Intelligence (AI) based Computer Aided Diagnosis(CAD) is being used performed in order to reduce human errors. This work is one such CAD based classification system to classify of benign and malignant ovarian tumours. Six popular Machine Learning(ML) algorithms have been applied on clinical ovarian bio-markers with an aim to classify the tumours. Also, a comparative study between the applied ML techniques is given in the result and discussion section.

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DLfeature] or decision fusion [hereafter, DLdecision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DLfeature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DLdecision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DLdecision, DLfeature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.

Diagnostic Performance of International Ovarian Tumor Analysis Logistic Regression Model LR2 for Adnexal Masses Classification at a Tertiary Gynecology Center in Singapore

Background: The International Ovarian Tumor Analysis (IOTA) LR2 model has been shown to provide a reasonably accurate preoperative classification of ovarian tumors. The purpose of this study was to evaluate the diagnostic performance of the IOTA LR2 model in distinguishing benign and malignant adnexal masses in the Singapore population. Methods: This was a retrospective study in a tertiary referral center. Women who attended the Gynecology Unit at Singapore General Hospital with evidence of adnexal tumor on ultrasound examination were evaluated using the IOTA LR2 protocol. The LR2 model was then used to calculate the probability of malignancy. Likelihood ratio of malignancy greater than 10% classifies the mass as malignant. The preoperative diagnosis of women who underwent surgery within 120 days of ultrasound examination was correlated with the final histopathological result. Results: Of the 353 women included in the final study population, 223 had benign disease, 29 had borderline malignant, and 101 had invasive cancer. The IOTA LR2 model had a sensitivity of 79.2% (95% confidence interval (CI), 71.2-85.8%) and a specificity of 79.4% (95% CI, 73.5-84.5%). The area under the receiver-operating characteristics curve was 0.84 (95% CI, 0.80 - 0.89). Conclusions: The IOTA LR2 model maintained its overall diagnostic accuracy when used in our local population. Although it is useful as a first-step test for triaging women with ovarian masses for surgery, a second-stage test would be required to minimize the number of women with benign disease being offered surgery for suspected ovarian malignancy. J Clin Gynecol Obstet. 2021;10(3):67-72 doi: https://doi.org/10.14740/jcgo758

Ovarian Cancer and Cancer Stem Cells-Cellular and Molecular Characteristics, Signaling Pathways, and Usefulness as a Diagnostic Tool in Medicine and Oncology.

Simple SummaryOvarian cancer is still a high-risk, metastatic disease, often diagnosed at a late stage. Difficulties in its treatment are associated with high resistance to chemotherapy and recurrence. Responsible for the malignant features of cancer are considered to be cancer stem cells (CSCs), which generate new cells by modifying various signaling pathways. Signaling pathways are crucial for the regulation of epithelial-mesenchymal transition, metastasis, and self-renewal of CSCs. New therapies based on the use of inhibitors that block CSC growth and proliferation signals are being investigated. The current histological classification of ovarian tumors, their epidemiology, and the recent knowledge of ovarian CSCs, with particular emphasis on their molecular basis, are important considerations.Despite the increasing development of medicine, ovarian cancer is still a high-risk, metastatic disease that is often diagnosed at a late stage. In addition, difficulties in its treatment are associated with high resistance to chemotherapy and frequent relapse. Cancer stem cells (CSCs), recently attracting significant scientific interest, are considered to be responsible for the malignant features of tumors. CSCs, as the driving force behind tumor development, generate new cells by modifying different signaling pathways. Moreover, investigations on different types of tumors have shown that signaling pathways are key to epithelial-mesenchymal transition (EMT) regulation, metastasis, and self-renewal of CSCs. Based on these established issues, new therapies are being investigated based on the use of inhibitors to block CSC growth and proliferation signals. Many reports indicate that CSC markers play a key role in cancer metastasis, with hopes placed in their targeting to block this process and eliminate relapses. Current histological classification of ovarian tumors, their epidemiology, and the most recent knowledge of ovarian CSCs, with particular emphasis on their molecular background, are important aspects for consideration. Furthermore, the importance of signaling pathways involved in tumor growth, development, and metastasis, is also presented.

Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image

Texture analysis has always been active areas of ultrasound image processing research. Using texture features to classify the ultrasound images is the focus of researchers' attention. How to extract representative texture features is an important part of successful texture description. The research goal of this paper is to apply the deep neural network into the ultrasound classification of ovarian tumors, and design a novel type of ovarian cancer diagnosis system. The improved HOG feature extraction method and the gray-level concurrence matrix of LBP image are firstly adopted to extract low-level features; Then, these features are cascaded into a new feature vector, and are input into the auto-encoder neural network to learn the high-level feature. Finally, the SVM classifier is used to achieve the classification of ovarian lesion. A large number of qualitative and quantitative experiments show that the improved method has more performance than the comparisons algorithms for ovarian ultrasound lesion, and it can significantly improve the classification performance while ensuring the accuracy rate and recall rate.

A Retrospective Clinicopathological Study of Malignant Ovarian Tumors: A 3 Year Experience in a Tertiary Health Care Center of Dhaka, Bangladesh

Background: Ovarian tumors are a heterogeneous group of neoplasm that have become increasingly important now-a-days because of their large variety of neoplastic entities and gradually increased rate of mortality due to female genital cancers.
 Objectives: The aim of this study was to analyze the histopathological pattern and clinical presentation of malignant ovarian tumors according to the WHO classification of ovarian tumors [2003] in a tertiary care center of Dhaka, Bangladesh.
 Materials and Methods: This retrospective study included and studied a total of 54 cases of malignant ovarian tumors at the Department of Obstetrics and Gynecology, Popular Medical College Hospital, Dhaka, Bangladesh over a period of 3 years from Jan 2015 to Dec 2017.
 Results: The mean age was 47.44±14.24 years old (age ranged from 20-70 years). Of the 54 malignant ovarian tumors studied, the commonest histological pattern observed in the study was serous cyst adenocarcinoma constituting 26 cases (48.15%) followed by adenocarcinoma of ovary (25.9%), mucinous cyst adenocarcinoma (14.8%), endometriod adenocarcinoma (3.7%), dysgerminoma (3.7%) and ovarian choriocarcinoma (3.7%). High level of serum CA125 was found in most of the cases (n=48; 88.89%). The chief complaints were abdominal pain (n=38; 70.37%) and abdominal distension (n=29; 53.70%). Majority were admitted with less than one month duration of symptoms. The size of the tumor varied from 2.2 to 20 cm. All the patients were admitted in III and IV stage.
 Conclusions: Serous cyst adenocarcinoma was the common finding of this study. The prognosis and varying therapeutic strategies of malignant ovarian tumors necessitate an accurate histopathological evaluation.
 J Dhaka Medical College, Vol. 29, No.2, October, 2020, Page 100-105

A two year retrospective study of ovarian tumours in a rural tertiary care hospital

Introduction: The classification of ovarian tumours, both benign and malignant, into numerous categories is a proof for the ever expanding number of new lesions being added every year. This heterogeneity of ovarian lesions is the initiating factor to conduct this retrospective study to evaluate the frequency and age distribution of ovarian lesions in our tertiary centre. Materials and Methods: This is a retrospective study conducted in the Department of pathology, Government Thiruvarur medical college, Thiruvarur, Tamil Nadu between the period Jan ‘2016 to Dec’2017.The clinical details along with the gross and histopathological diagnosis cases with ovarian lesions were compiled and analysed to arrive at the percentage of distribution of various lesions. Results: Out of 144 cases studied, benign, borderline and malignant ovarian tumours were 79.85%, 3.47% and 16.65% respectively. Benign tumours peaked between third and fourth decades, while malignant tumours were seen commonly after fifth decade. Surface epithelial tumours were common (84%) followed by germ cell tumours (7.64%) and sex cord stromal tumours (4.16%). Serous cystadenoma was more common than mucinous cystadenoma followed by mature cystic teratoma. Serous cystadenocarcinoma was the most common malignancy in our study. Keywords: Surface epithelial tumour, Germ cell tumours, Sex cord stromal tumours.

A five year retrospective study of the histomorphological spectrum of ovarian tumours in an urban population of West Bengal, in the light of WHO classification of Ovarian Tumours (2014)

A five year retrospective study of the histomorphological spectrum of ovarian tumours in an urban population of West Bengal, in the light of WHO classification of Ovarian Tumours (2014)

Histopathological study of ovarian tumors in a tertiary care center

Introduction: Ovary is a complex structure and its neoplasms show a wide spectrum of histological types and clinical behavior. The present study was done with the aim of studying the histopathological pattern of ovarian tumors in women of various age groups. Ovarian tumors represent about 30% of all cancers of the female genital system. Ovarian tumor is the seventh leading cause of cancer death. Ovarian tumors are often difficult to detect until they are advanced in stage or size, as symptoms are vague and insidious. An accurate and early diagnosis of malignant lesions will go a long way in the optimal management of these cases. Aims and Objectives: To determine the frequency of different histological types of ovarian tumors in a tertiary care center. Materials and Methods: This was a retrospective study included 150 cases of histopathologically proven ovarian tumors, reported in the Sree Mookambika Institute of medical sciences, over a one and half year period (January 2018 to June 2019). These were classified according to the WHO classification of ovarian tumors. Clinical presentation of the patients was analyzed from archived case records. Results: Of 150 cases of ovarian tumors, 138 masses were unilateral (92%) and 12 were bilateral (8%). Benign neoplastic lesions were 58.7%, and malignant was 2%.In benign neoplastic, the most common neoplasm was serous cystadenoma followed by mucinous cystadenoma In malignant, the current study had 2 cases of mucinous cystadenoma with borderline malignancy and 1 case was serous papillary cystadenocarcinoma Conclusion: A more detailed prospective study include genetic profiling, to establish the reason for this low incidence of malignant lesions

Histopathological spectrum of ovarian tumors: A study of 53 cases

Background: Ovarian malignancies are one of the most common malignancies affecting women. Worldwide, it is the sixth most common cancer overall and second most common among gynecological cancers. It consists of 4% of all female cancers and 23% of all gynecological cancers. In India, it comprises 8.7% of cancers in different parts of the country. About 80% are benign and occur in younger age group and malignant tumors occur in older age group. The purpose of the study was to study the frequency of ovarian tumors in relation of age distribution, laterality, histological subtypes and histomorphological spectrum. Methods: This one year retrospective study included fifty-three (53) cases of ovarian tumors. All the resected specimens received either as solitary specimens or as a part of pan hysterectomy in the laboratory were included in the study. After detailed study of sections under the light microscope the final diagnosis was arrived and were classified according to the WHO classification of ovarian tumors. Results: Of the 53 cases majority were malignant tumors 71.70% (38 cases) with serous carcinomas being more common. Majority of the malignant tumors were seen bilaterally and more common in older age groups. Benign tumors were more common in younger age groups. Conclusion: The ovarian tumors being one of the major gynecological challenge to medical fraternity. However, benign tumors are more common in the younger age group and malignant tumors in older age group. Hence early detection of ovarian tumors is very crucial in reducing the morbidity and mortality of the patients.

Ovarian tumors: a survey of selected advances of note during the life of this journal

The author reviews highlights of advances in knowledge concerning ovarian tumor pathology since the time of an essay in the first issue of this Journal written by Dr Robert E. Scully, who, both before and for several decades after the Journal was instituted, made many original contributions to the field and was the major architect of the 1973 World Health Organization classification of ovarian tumors which was much more clear and logical than prior ones. The current review considers the neoplasms in essentially the same order as was done in the first issue of this journal and presents a personal look at the highlights of new information concerning various well-known categories, surface epithelial, germ cell, sex cord-stromal, metastatic neoplasms and briefly, benign so-called tumor-like lesions. Some of the most notable developments are as follows: (1) an orderly approach to the classification of implants of serous borderline tumors into noninvasive and invasive categories; (2) recognition of distinctive micropapillary patterns seen in some borderline tumors and low-grade carcinomas; (3) a remarkable propensity for some endometrioid carcinomas to mimic sex cord- stromal tumors; (4) appreciation of the differences between primary mucinous tumors of intestinal and müllerian types; (5) the importance of distinguishing within primary mucinous carcinomas between expansile and destructive stromal invasion; (6) emphasis on the diagnosis of immature teratoma being based on the presence of primitive-embryonic-appearing tissues; (7) appreciation of variant morphology of cases of struma ovarii which may lead to significant diagnostic problems; (8) subdivision of granulosa cell tumors into adult and juvenile types because of the differing features of the two groups including in the second category the propensity for more malignant neoplasms to be mimicked; (9) recognition of a distinctive form of Sertoli-Leydig cell tumor, the retiform variant, with a propensity to occur in the young; (10) appreciation of a unique highly malignant neoplasm that typically afflicts the young and may be associated with hypercalcemia, so-called small cell carcinoma of hypercalcemic type; (11) greater awareness than was hitherto the case of the propensity for metastatic intestinal adenocarcinoma to mimic primary endometrioid carcinoma and similarly for metastatic mucinous carcinomas to simulate primary mucinous cystic tumors; (12) recognition of the distinctive features of low-grade appendiceal mucinous neoplasms that spread to the ovary and are typically associated with pseudomyxoma peritonei; and (13) appreciation that the histologic spectrum seen in cases of Krukenberg tumor is broader than often previously thought.