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This article discusses a case study of a rare type of ovarian tumor called Sertoli-Leydig cell tumor (SLCT). SLCTs are composed of Sertoli and Leydig cells and make up less than 0.5% of all ovarian tumors. The article describes the cytomorphologic features of an SLCT found in an uncommon metastatic site in a young female. The study highlights the importance of accurate cytologic diagnosis and clinical correlation in determining the prognosis and appropriate treatment for SLCTs. [Extracted from the article]

Abstract Background Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. Methods We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample’s predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. Results The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. Conclusions The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Abstract Background The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). Methods This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). Results The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. Conclusions The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Background: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. Methods: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. Results: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. Conclusions: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours. [ABSTRACT FROM AUTHOR]

Background: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). Methods: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). Results: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. Conclusions: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS. [ABSTRACT FROM AUTHOR]

Mounting evidence has highlighted the multifunctional characteristics of glutamine metabolism (GM) in cancer initiation, progression and therapeutic regimens. However, the overall role of GM in the tumour microenvironment (TME), clinical stratification and therapeutic efficacy in patients with ovarian cancer (OC) has not been fully elucidated. Here, three distinct GM clusters were identified and exhibited different prognostic values, biological functions and immune infiltration in TME. Subsequently, glutamine metabolism prognostic index (GMPI) was constructed as a new scoring model to quantify the GM subtypes and was verified as an independent predictor of OC. Patients with low‐GMPI exhibited favourable survival outcomes, lower enrichment of several oncogenic pathways, less immunosuppressive cell infiltration and better immunotherapy responses. Single‐cell sequencing analysis revealed a unique evolutionary trajectory of OC cells from high‐GMPI to low‐GMPI, and OC cells with different GMPI might communicate with distinct cell populations through ligand‐receptor interactions. Critically, the therapeutic efficacy of several drug candidates was validated based on patient‐derived organoids (PDOs). The proposed GMPI could serve as a reliable signature for predicting patient prognosis and contribute to optimising therapeutic strategies for OC. [ABSTRACT FROM AUTHOR]

Angiogenic inhibitors have been demonstrated to inhibit tumour cells in ovarian carcinoma, but the initial data are not accurate enough to indicate the influence of these drugs on the post‐therapy wound healing. In order to assess the effect of angiogenic inhibitors on the treatment of wound healing in ovarian carcinoma, we performed a meta‐analysis of related literature. For this meta‐analysis, we looked up the data from 4 databases: PubMed, EMBASE, Web of Science and the Cochrane Library. All literature searches were performed up to October 2023. The ROBINS‐I tool was applied to evaluate the risk of bias in the inclusion trials, and statistical analysis was performed with RevMan 5.3. In this research, 971 related research were chosen, and 9 of them were selected. These studies were published between 2013 and 2023. In all 9 trials, a total of 3902 patients were enrolled. There was a significant reduction in the risk of wound infection in the control group than in those who received angiogenesis inhibitors (OR, 0.66; 95% CI, 0.49–0.89 p = 0.007). The risk of developing an abscess was not significantly different from that of those who received angiogenesis inhibitors (OR, 0.80; 95% CI, 0.20–3.12 p = 0.74). The risk of perforation in the control group was smaller than that in those receiving angiogenic inhibitors (OR, 0.25; 95% CI, 0.11–0.56 p = 0.0006). There was a significant increase in the risk of injury and GI perforation in women who received angiogenic inhibitors than in the control group. But the incidence of abscess did not differ significantly among the two groups. [ABSTRACT FROM AUTHOR]

This article reports a case of an ovarian collision tumour consisting of an ovarian fibroma and a serous cystadenoma. A 60-year-old woman exhibited symptoms of post-menopausal bleeding and abdominal pain persisting for three months. Computerized tomography identified a solid mass with a cystic component in the right adnexa, and the patient underwent staging laparotomy. Gross examination of the right ovary revealed a cystic tumour with adjacent solid mass. The histopathological analysis identified a cystic mass that matched the characteristics of a serous cystadenoma, with an adjacent solid mass that matched the characteristics of a sex-cord stromal tumour, both located in the right ovary. Additionally, a small cyst that matched the characteristics of a serous cystadenoma was found in the left ovary. There have been only seven previously reported examples of this specific mix of ovarian tumours. Mostly affecting patients above 60 years of age, although tumour markers levels are normal, such cases may present with a complex clinical scenario, as in this case, and demand a comprehensive diagnostic and therapeutic approach.

A forty-six-year-old woman presented with complaints of pain and swelling in the left inguinal region. On examination, it was found to be an incarcerated irreducible left inguinal hernia. A cystic mass was also felt in the left iliac fossa. The patient had a congenital absence of the uterus and right kidney. She had primary amenorrhea, primary infertility and dyspareunia but her secondary sexual characters were developed normally. Ultrasonography (US) of the abdomen and pelvis revealed a loculated anechoic fluid collection and an ovary in the left inguinal canal (Fig. 1) with an empty bilateral renal fossa and an ectopic left pelvic kidney (Fig. 2). Contrast-enhanced computed tomography (CECT) and magnetic resonance imaging (MRI) of the abdomen and pelvis revealed a cystic lesion in the left inguinal canal with eccentrically placed ovary within the cyst, suggestive of the patent canal of Nuck with herniated ovary (Fig. 3). There was complete agenesis of the uterus, cervix and vaginal canal (Mullerian agenesis) with ectopic pelvic kidney in the left adnexa (Fig. 4). The diagnosis of an ovarian mass herniating through the left inguinal region as a part of Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome type II was made and the patient was managed successfully through surgery. [ABSTRACT FROM AUTHOR]

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Ovarian mucinous cystic tumours with mural nodules are rare tumours of the ovary that are often missed out during diagnosis. They are classified under the ovarian mucinous surface epithelial-stromal tumours. These mural nodules can be sarcoma-like (benign), anaplastic carcinoma, sarcomas, or mixed malignant (carcinosarcoma). However, very few cases of anaplastic malignant mural nodules have been reported. Here, we present a case of a borderline ovarian mucinous cystadenoma with anaplastic mural nodule that has sarcomatoid differentiation, in a 39-year-old woman who presented with a 1-year history of progressive abdominal swelling and pain. There were intraoperative findings of huge right ovarian cystic tumour with omental and umbilical deposits. Differential diagnosis of possible germ cell tumours, vascular tumours, melanoma, sarcoma and sarcoma-like nodules were ruled out with routine histology (Haematoxylin & Eosin), histochemical (reticulin) and immunohistochemical stains (CK AE1/3+, CD30+, AFP-, HCG-, EMA-, S100 protein-, CD31-, and CD34-) and the final diagnosis of a mural nodule of anaplastic carcinoma with sarcomatoid differentiation in a borderline ovarian mucinous cystadenoma established. Unfortunately, due to the aggressive nature of the tumour and disease progression, the patient passed on a few months after the surgery. This rare tumour, especially the ones with anaplastic carcinoma or mixed tumours, usually has an aggressive clinical course with most patients presenting late when the disease is advanced with poor clinical outcomes as is seen with the index patient. A high index of suspicion of this tumour with early detection and a multidisciplinary approach to its management is advised. [ABSTRACT FROM AUTHOR]

Krukenberg tumours are unusual metastatic tumours of the ovary with primary tumours from the stomach, breast and gastrointestinal malignancies. Krukenberg tumour from pulmonary malignancy represents an extremely rare situation. This is an elaboration of a case of young women with Krukenberg tumour rising from lung adenocarcinoma. A 38‐year‐old woman presented with progressive abdominal distention for the past 2‐years. Computed tomography (CT) of thorax, abdomen and pelvis revealed a huge ovarian mass with left lung nodules and left‐sided pleural effusion. A detailed immunohistochemical staining on pleural fluid cytology confirmed the diagnosis of metastatic adenocarcinoma of lung origin. She underwent doublet platinum chemotherapy as molecular testing for oncogenic mutation was negative. The patient responded well to chemotherapy with a significant reduction in ovarian tumour size. Early identification of the primary source of Krukenberg tumour is paramount to avoid invasive diagnostic surgical intervention for ovarian metastasis. [ABSTRACT FROM AUTHOR]

Introduction: Variety of primary ovarian neoplasms reflects the complex function of the ovary. The study was conducted to know distribution of ovarian lesions, analyse their mode of presentation and distribution of various morphological pattern of ovarian neoplasms. Methods: A systematic search identified 205 ovarian lesion specimens submitted in the department over a time period of three years (January 2018-December 2020). The tumours were classified according to World Health Organization (WHO) classification 2016.Results: There was a wide age range from 11 years to 72 years. Among neoplastic ovarian lesions peak was seen in the fourth decade, mean 48.1 years. Sonographic appearance showed a prominent solid component (32/32,100%) in all malignant cases. Non-neoplastic cysts were more common (107, 52.2%) than neoplastic tumours (98, 47.8%). Among the neoplastic tumours (62) 63.3% were benign. Four borderline tumours (4.1%) were noted in the study. Surface epithelial tumours were the commonest histomorphological type 67 (68.4%). Granulosa cell tumour (4/32, 12.5%) was the commonest sex cord stromal tumour. Conclusion: Ovarian lesion possess wide gamut of histology. Non neoplastic ovarian lesions occur more frequently when compared to neoplastic lesions. Major proportion of malignant ovarian tumours was contributed by surface epithelial tumours. [ABSTRACT FROM AUTHOR]

Abstract Krukenberg tumours are unusual metastatic tumours of the ovary with primary tumours from the stomach, breast and gastrointestinal malignancies. Krukenberg tumour from pulmonary malignancy represents an extremely rare situation. This is an elaboration of a case of young women with Krukenberg tumour rising from lung adenocarcinoma. A 38‐year‐old woman presented with progressive abdominal distention for the past 2‐years. Computed tomography (CT) of thorax, abdomen and pelvis revealed a huge ovarian mass with left lung nodules and left‐sided pleural effusion. A detailed immunohistochemical staining on pleural fluid cytology confirmed the diagnosis of metastatic adenocarcinoma of lung origin. She underwent doublet platinum chemotherapy as molecular testing for oncogenic mutation was negative. The patient responded well to chemotherapy with a significant reduction in ovarian tumour size. Early identification of the primary source of Krukenberg tumour is paramount to avoid invasive diagnostic surgical intervention for ovarian metastasis.

Introduction: Variety of primary ovarian neoplasms reflects the complex function of the ovary. The study was conducted to know distribution of ovarian lesions, analyse their mode of presentation and distribution of various morphological pattern of ovarian neoplasms. Methods: A systematic search identified 205 ovarian lesion specimens submitted in the department over a time period of three years (January 2018- December 2020). The tumours were classified according to World Health Organization (WHO) classification 2016. Results: There was a wide age range from 11 years to 72 years. Among neoplastic ovarian lesions peak was seen in the fourth decade, mean 48.1 years. Sonographic appearance showed a prominent solid component (32/32,100%) in all malignant cases. Non-neoplastic cysts were more common (107, 52.2%) than neoplastic tumours (98, 47.8%). Among the neoplastic tumours (62) 63.3% were benign. Four borderline tumours (4.1%) were noted in the study. Surface epithelial tumours were the commonest histomorphological type 67 (68.4%). Granulosa cell tumour (4/32, 12.5%) was the commonest sex cord stromal tumour. Conclusion: Ovarian lesion possess wide gamut of histology. Non neoplastic ovarian lesions occur more frequently when compared to neoplastic lesions. Major proportion of malignant ovarian tumours was contributed by surface epithelial tumours.

Abstract Background Cancer‐associated cachexia (CAC) is a complex syndrome of progressive muscle wasting and adipose loss with metabolic dysfunction, severely increasing the morbidity and mortality risk in cancer patients. However, there are limited studies focused on the underlying mechanisms of the progression of CAC due to the complexity of this syndrome and the lack of preclinical models that mimics its stagewise progression. Methods We characterized the initiation and progression of CAC in transgenic female mice with ovarian tumours. We measured proposed CAC biomarkers (activin A, GDF15, IL‐6, IL‐1β, and TNF‐α) in sera (n = 6) of this mouse model. The changes of activin A and GDF15 (n = 6) were correlated with the decline of bodyweight over time. Morphometry and signalling markers of muscle atrophy (n ≥ 6) and adipose tissue wasting (n ≥ 7) were assessed during CAC progression. Results Cancer‐associated cachexia symptoms of the transgenic mice model used in this study mimic the progression of CAC seen in humans, including drastic body weight loss, skeletal muscle atrophy, and adipose tissue wasting. Serum levels of two cachexia biomarkers, activin A and GDF15, increased significantly during cachexia progression (76‐folds and 10‐folds, respectively). Overactivation of proteolytic activity was detected in skeletal muscle through up‐regulating muscle‐specific E3 ligases Atrogin‐1 and Murf‐1 (16‐folds and 14‐folds, respectively) with decreasing cross‐sectional area of muscle fibres (P

Abstract Background This study aims to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) the Assessment of Different NEoplasias in the adneXa (ADNEX) model in the preoperative diagnosis of adnexal masses in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China. Methods This was a single oncology centre, retrospective diagnostic accuracy study of 620 patients. All patients underwent surgery, and the histopathological diagnosis was used as a reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumours, borderline ovarian tumours (BOTs), stage I ovarian cancer (OC), stage II-IV OC and ovarian metastasis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumours into different histological types with and without cancer antigen 125 (CA 125) results. Results Of the 620 women, 402 (64.8%) had a benign ovarian tumour and 218 (35.2%) had a malignant ovarian tumour, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate benign and malignant adnexal masses was 0.97 (95% CI, 0.96–0.98). Performance was excellent for the discrimination between benign and stage II-IV OC and between benign and ovarian metastasis, with AUCs of 0.99 (95% CI, 0.99–1.00) and 0.99 (95% CI, 0.98–1.00), respectively. The model was less effective at distinguishing between BOT and stage I OC and between BOT and ovarian metastasis, with AUCs of 0.54 (95% CI, 0.45–0.64) and 0.66 (95% CI, 0.56–0.77), respectively. When including CA125 in the model, the performance in discriminating between stage II–IV OC and stage I OC and between stage II–IV OC ovarian metastasis was improved (AUC increased from 0.88 to 0.94, P = 0.01, and from 0.86 to 0.97, p = 0.01). Conclusions The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of nonexpert ultrasonographers with limited experience in China. In classifying different subtypes of ovarian cancers, the model has difficulty differentiating BOTs from stage I OC and BOTs from ovarian metastases.

Medical 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. [ABSTRACT FROM AUTHOR]

Ovarian tumours occur at an incidence rate of 2.6 cases per 100,000 children, and their frequency escalates proportionally with age. These tumours typically have an organic origin and are seldom functional. The frequent presence of pain primarily characterizes the clinical manifestation of ovarian cysts. The complexity inherent in analyzing this pain can complicate diagnosis, necessitating a thorough evaluation that could potentially require resorting to a pelvic ultrasound to confirm the diagnosis and determine the optimal management. Additional imaging techniques and tumour marker assays aid in specifying the nature of this mass, where surgery remains the sole therapeutic option. The histopathological analysis further confirms the precise nature of the mass or cyst to establish a prognosis and guide the management strategies and progression follow-up. Our case is for a girl aged 15 years old without notable medical history, presenting at admission with a 6-month history of a gradually enlarging abdominal mass associated with abdominal pain and tumour markers (BHCG was negative). In addition, LDH, CA125, Inhibin, CEA and CA19.9 were negative. Radiologically, the ultrasound revealed a huge cystic abdominopelvic mass occupying the entirety of the abdomen and pelvis with posterior compression of the intestines without visualization of the left ovary; the right ovary appeared normal. MRI confirmed the presence of a voluminous intraperitoneal abdominopelvic cystic formation measuring 31 × 20 × 8 cm. The patient underwent laparoscopic surgery, revealing a large cystic mass with septations filling the abdomen and pelvis and displacing the gastrointestinal tract. After draining 6 L of clear fluid, the entire cyst was removed while preserving some left ovarian tissue, with the right ovary appearing normal. The follow-up is still ongoing, with the last consultation being 8 months post-operative., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s) 2024.)

The aim of this retrospective study was to determine the prevalence of ovarian masses and calculate the diagnostic performance of the pattern recognition approach in ovarian pathology. A total of 1001 patients diagnosed with ovarian mass were included, of which 92.6% were diagnosed with ovarian pathology and the presence of a pathological result, while 7.4% of cases were diagnosed with functional ovarian cyst. The prevalence of ovarian malignancy was 15%. A specific ultrasound diagnosis was suggested in 62.9% of all cases, while sonographers did not explicitly provide a diagnosis in remaining cases. A subjective assessment showed 80.3% sensitivity (95% confidence interval (CI) 68.7-89.1) and 97.6% specificity (95% CI 96-98.6) in differentiating between benign and malignant ovarian masses. The sensitivity and specificity for the diagnosis of endometriotic cyst were 77.03% and 90.63% and 63.19% and 94.3% for mature cystic teratoma, respectively. In conclusion, assessment showed good performance in differentiating between benign and malignant ovarian mass and it was possible to diagnose several specific ovarian tumours. Impact StatementWhat is already known on this subject? Pattern recognition is an acceptable method for classifying ovarian mass, which exhibits specific morphological features on grey-scale ultrasonography, and can be used to predict nature and histological type.Whatdothe results of this study add? Even in the hands of an expert examiner, there were a number of cases in which the diagnoses could not be specifically stated. Pattern recognition correctly classified 90.3% of ovarian masses as either benign or malignant and correctly provided specific histologic diagnoses after exclusion of unspecified diagnosis in 80.6% of all cases. The diagnostic performance of this approach was high in differentiating between benign and malignant ovarian mass and in diagnosing some specific ovarian pathologies.Whatarethe implicationsof these findings for clinical practice and/or further research? A subjective assessment is simple and easy to use in clinical practice and has shown promising results in classifying benign and malignant ovarian mass. Moreover, it can also be used to make some specific diagnoses. However, specialised and experienced gynaecological ultrasound examiners are required to provide the most accurate diagnosis. Therefore, criteria to describe ultrasound features and convincing operators to make a definite diagnosis as often as possible should be encouraged. A prospective study to verify diagnostic performance of pattern recognition or comparing with other ultrasonographic diagnostic tools should be considered. [ABSTRACT FROM AUTHOR]

The most common indication for frozen section at our Centre (NGOC, Gateshead, UK) is for the diagnosis of ovarian masses. The other indications are to assess the nature of uterine masses, assess adequacy of resection margins in trachelectomy specimens and assessment of lymph nodes for metastases prior to extensive surgical procedures such as pelvic exenterations and laterally extended endopelvic resections. This article will focus on practical issues relating to ovarian epithelial tumours, the most frequent group of tumours submitted for frozen section diagnosis. It will provide guidance on the approach to common entities, diagnostic pitfalls, and strategies for dealing with less frequently encountered lesions. Emphasis will be placed on macroscopic examination, appropriate sampling, and the use of intraoperative cytology as an adjunct. The aim is to provide clinically useful frozen section diagnosis in order to reduce the number of diagnoses deferred to paraffin section and facilitate optimal intraoperative management decisions. [ABSTRACT FROM AUTHOR]

Abstract Background Hilus cell tumours is considered an uncommon branch of androgen producing neoplasms that accounts for

Background: Epithelial ovarian carcinoma is the major subtype of ovarian cancer, one of the most lethal gynaecological malignancies. Due to some difficulties in early detection, patients are usually diagnosed at advanced stages, and overall survival is poor. P16 is a tumour suppressor gene that regulates the cell cycle by inhibiting S phase. Studying experssion of this immunohistochemical marker will help to diagnose and predict prognosis of ovarian epithelial tumours. The study's objective was to evaluate the expression of p16 in surface epithelial ovarian neoplasm and its association with histo-pathological grading. Methodology: This cross-sectional study was carried out at the Department of Pathology, Sir Salimullah Medical College, Dhaka. A total of 46 patients diagnosed histo-pathologically as surface epithelial ovarian neoplasm were included in the study. P16 immuno-staining, as well as some demographic and clinical data, were also evaluated. Result: The mean age of the patient was 48.04 ± 12.182SD years. The most common histologic subtype was serious, followed by the mucinous type. P16 was positive in 40 (86.96%) cases. A statistically significant difference in p16 expression was observed between tumour types and between tumour grades. Up-regulation of p16 expression was observed in malignant tumours more than in benign tumours. P16 expression was increased with increased grading of the malignant tumours. Conclusion: P16 expression is associated with histo-pathological grading in ovarian epithelial carcinoma. [ABSTRACT FROM AUTHOR]

Ultrasound imaging (US) is one of the most common diagnostic imaging tools for producing images of the human body in clinical practice. This work is devoted to studying ultrasound images collected from gynaecological tests for medical purposes regarding ovarian and breast defects. The study revolves around (i) Enhancing the texture of the image by applying a new effective framework that can help in reducing the speckle noise from the image while preserving the most important information; (ii) Extracting the most prominent features using the histogram of oriented gradients (HOG) and; (iii) Fusing the features that are produced by the edge operators and using them as an input to the ANN classifier to generate three trained classifiers. The fusion technique has been used to get an effective decision by using the whole features. The experimental results of the proposed method for the breast cancer and ovarian tumour using the second experiment achieved 97.96% accuracy, 96.05% sensitivity, and 99.17% specificity by utilizing the breast cancer information set. Overall, 95.87% precision, 97.01% sensitivity, and 93.33% specificity have been achieved for the ovarian tumour data collection. Consequently, the proposed method has been improved to validate the output of modern computerized and automated technologies. This method analyzes the gynaecological ultrasound images to identify suspicious objects or cases with health consequences for women. [ABSTRACT FROM AUTHOR]

The uterine leiomyomas are monoclonal tumours of myometrial smooth muscle cells that are oestrogen dependent. A 43-year-old patient was referred by her gynaecologist under a suspected diagnosis of ovarian tumour. She complained of prolonged and profuse, regular menstrual bleeding that leaded to anaemia. The ultrasound examination showed a complex tumour mass with dimensions 68 x 85 mm, with several cystic formations fulfilled with clear fluid, which protrudes from the posterior wall of the uterus. Therefore, the diagnosis of leiomyoma with a cystic degeneration was considered preoperatively. In the case of cystic degeneration of the myoma, the ultrasound shows a combination of cystic and solid components with irregular shape and variable echogenicity. The ovarian malignancy should be ruled out in the presence of a large cystic mass with irregular septa and solid nodular parts filling the small pelvis. Ultrasound characteristics of degenerative myomas should always be considered, so as not to replace it with an ovarian mass, especially if it is subserosal and on the loop.

Background: This study aims to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) the Assessment of Different NEoplasias in the adneXa (ADNEX) model in the preoperative diagnosis of adnexal masses in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China. Methods: This was a single oncology centre, retrospective diagnostic accuracy study of 620 patients. All patients underwent surgery, and the histopathological diagnosis was used as a reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumours, borderline ovarian tumours (BOTs), stage I ovarian cancer (OC), stage II-IV OC and ovarian metastasis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumours into different histological types with and without cancer antigen 125 (CA 125) results. Results: Of the 620 women, 402 (64.8%) had a benign ovarian tumour and 218 (35.2%) had a malignant ovarian tumour, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate benign and malignant adnexal masses was 0.97 (95% CI, 0.96–0.98). Performance was excellent for the discrimination between benign and stage II-IV OC and between benign and ovarian metastasis, with AUCs of 0.99 (95% CI, 0.99–1.00) and 0.99 (95% CI, 0.98–1.00), respectively. The model was less effective at distinguishing between BOT and stage I OC and between BOT and ovarian metastasis, with AUCs of 0.54 (95% CI, 0.45–0.64) and 0.66 (95% CI, 0.56–0.77), respectively. When including CA125 in the model, the performance in discriminating between stage II–IV OC and stage I OC and between stage II–IV OC ovarian metastasis was improved (AUC increased from 0.88 to 0.94, P = 0.01, and from 0.86 to 0.97, p = 0.01). Conclusions: The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of nonexpert ultrasonographers with limited experience in China. In classifying different subtypes of ovarian cancers, the model has difficulty differentiating BOTs from stage I OC and BOTs from ovarian metastases. [ABSTRACT FROM AUTHOR]

Epithelial ovarian neoplasms constitute the majority of ovarian tumours and are the most common malignant ovarian neoplasm. They are the eighth leading cause of cancer deaths worldwide. This study evaluated the epidemiology and histological subtypes of epithelial ovarian neoplasms at a single institution. This retrospective, quantitative analysis evaluated 123 epithelial ovarian neoplasms between 2013 and 2017 and classified them according to age, biological behaviour and histological subtype. Of 123 cases, 64 (52.0%), tumours were benign, 30 (24.4%) were borderline and 29 (23.6%) tumours were malignant. Serous cystadenoma was the most common benign tumour (42.2%). Mucinous borderline tumours were the most common borderline neoplasm (50.0%). High-grade serous carcinoma was the most common carcinoma (58.6%). Patients presented at an older age with borderline tumours and malignant tumours, compared with patients with benign tumours. The current study demonstrated that serous tumours were the most common type of benign and malignant tumours whilst mucinous neoplasms were the commonest subtype of borderline tumours. These findings are congruent with multiple similar studies. A higher number of borderline tumours were seen in this cohort in comparison with previous studies. This suggests a need for additional sampling of borderline tumours, over and above the international standard, in our population, to definitively exclude invasive malignancy. This study also demonstrated the histological progression of benign serous and mucinous tumours to borderline tumours and low-grade serous carcinoma and mucinous carcinoma, respectively, which supports the stepwise tumour progression model of these tumours. [ABSTRACT FROM AUTHOR]

Abstract Background Existing data from several reports on the association between lipid profile and ovarian tumour (OT) suggests divergent conclusions. Our aim was to examine whether circulating lipid profile: total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) differed between cases and non-cases of OT. Methods Electronic repositories; PUBMED, EMBASE and Cochrane library were explored through December 2019 to retrieve published articles for inclusion in the meta-analysis after quality assessment. Heterogeneity was assessed using I 2 statistics, the effect of individual studies on the overall effect size was tested using sensitivity analysis and funnel plot was used to evaluate publication bias. Results Twelve studies, involving 1767 OT cases and 229,167 non-cases of OT were included in this meta-analysis and I 2 statistics ranged between 97 and 99%. Mean circulating TC (− 16.60 [− 32.43, − 0.77]mg/dL; P = 0.04) and HDL (− 0.25[− 0.43, − 0.08]mmol/L; P = 0.005) were significantly lower among OT cases compared to non-OT cases. Conclusion Decreased TC and HDL profiles were observed among subjects with OT in this collection of reports. The implications of TC and HDL in tumour manifestations and growth need to be validated in a large multi-ethnic longitudinal cohort adjusting for relevant confounders.

Granulosa Cell Tumour (GCT) is a rare malignant ovarian tumour. Adult GCT variety is more common than juvenile variety and occurs usually in postmenopausal women. It showed a spectrum of the imaging findings due to various histological appearances. It may present with solid masses, multilocular cystic lesion or completely cystic lesion. Here, authors present an interesting case of a 27-year-old young female, who presented with a large lump in the abdomen. On Magnetic Resonance Imaging (MRI) there was a large well-defined, multilocular cystic lesion at superior aspect of the lesion and complex cystic, solid mass at inferior aspect. Functional MRI like diffusion weighted imaging which provides good image contrast helped in determining the malignancy despite a benign diagnosis on ultrasound guided biopsy and furthering the patient for histopathological examination to come to a final diagnosis.

After performing laparoscopic unilateral adnexectomy in a 53-year-old woman for a rapidly grown unilateral adnexal mass, pathologists reported a primary ovarian leiomyoma with no genuine ovarian tissue. This rare diagnosis is found in less than 100 reports after systematic literature review, a greater number of asymptomatic ovarian leiomyomas can be expected. Thorough preoperative diagnostic measures are essential as rare cases of malignancy have been described. [ABSTRACT FROM AUTHOR]

purpose of this study was to investigate the difference of methylation rates of hTERT promoter in tumour tissues and plasma samples of patients with ovarian magnificent tumour and those with ovarian benign tumour, as well as in plasma samples of healthy women. This study further aimed to establish a possible association between increased methylation rate of hTERT promoter and circulating tumour DNAs (ctDNA) amongst patients with ovarian magnificent tumour. Methods: Tumour tissue samples and plasma samples were separately obtained from 17 patients with ovarian magnificent tumour (experiment group, group A) and from 15 patients with ovarian benign tumour (control group, group B). Another 15 plasma samples were acquired from healthy women (control group, group C). Promoter methylation was assessed by methylation-specific PCR (MSP). Statistical analysis was conducted using SPSS 22.0. Results: Methylation of hTERT was observed in 76.5% of tumour tissue samples and in 70.6% of plasma samples from patients with ovarian magnificent tumour. It was also observed in 26.7% of tumour tissue samples and 20% of plasma samples from patients with ovarian benign tumour, and in 13.3% of plasma samples from healthy women. Comparing between plasmas and tissues, the respective rates of consistency, sensitivity and specificity were 70.59%, 76.9% and 50% in group A, and 80%, 50% and 90.9% in group B. Hence, the associations of hTERT methylation with ctDNAs (p=0.001) and tumour tissue samples (p=0.012) amongst patients with ovarian magnificent tumour were established. Conclusion: An increased methylation of hTERT promoter is related to ctDNAs and tumour tissues of patients with ovarian magnificent tumour. [ABSTRACT FROM AUTHOR]

Fibrothecomas are rare sex cord stromal tumours. They usually occur in postmenopausal women and are mostly unilateral. They are usually associated with good prognosis; however, difficulties are encountered in establishing diagnosis. The treatment includes surgical resection. Here, the authors describe a rare case of a bilateral ovarian fibrothecoma in a 20-year-old female who presented with a large pelvic mass. On examination, there was a regular, immobile solid mass extending 30 weeks pregnancy size per abdomen. Pelvic examination revealed a firm nodular mass with minimal mobility and uterus could not be appreciated separately. On ultrasonography, a large solid mass arising from the pelvis superior and to the right of the uterus extending up to right hypochondrium was seen along with few cystic spaces and peripheral vascularity. Computerised tomography demonstrated two large solid cystic abdominopelvic mass lesion with internal solid enhancing areas and calcification of approximately 18 cm on the right side and 14 cm on the left side. The patient underwent exploratory laparotomy. Intra-operatively, the right ovarian mass was found, which was histopathologically confirmed as bilateral ovarian tumour (Bilateral Fibrothecoma).

Struma ovarii is a variant of mature cystic teratoma, with predominant thyroid element. Confirmatory diagnosis is by histopathology. It may mimic as ovarian malignancy in some. It may be associated with ascites in minority; even CA-125 has been found to be raised in some cases. We here report three cases – two operated for suspected ovarian malignancy in a postmenopausal female and one case operated for persistent benign ovarian cyst with raised CA-125 in a young female. In all histopathological diagnosis was a benign struma ovarii. It is difficult to diagnose these cases preoperatively as there are no specific clinical, radiological or serum markers for these rare tumors in the absence of thyroid biology abnormality. Most cases are diagnosed on histopathology. These benign tumors many times require extensive staging laparotomy for suspected ovarian malignancy in the absence of preoperative diagnosis. Prognosis is good for these tumors. Subtle radiological signs and a clinical suspicion can avoid extensive staging laprotomies in these patients.

Aims: Anaplastic carcinoma arising in a mucinous tumour of the ovary and rarely in the retroperitoneum is an uncommon neoplasm with three morphological patterns; rhabdoid, sarcomatoid and pleomorphic. We investigated expression of switch/sucrose non‐fermentable (SWI/SNF) chromatin remodelling complex components and claudin‐4 expression. Methods and results: Twenty‐two ovarian and three retroperitoneal mucinous tumours were investigated using antibodies against SMARCB1, SMARCA4, SMARCA2, ARID1A and claudin‐4. Loss of nuclear staining for any SWI/SNF protein was observed in the anaplastic component of nine of 25 (36%), with retained expression within the mucinous component of all tumours. Five (56%) showed loss of more than one protein, with dual loss of SMARCA4 and SMARCA2 in two, loss of SMARCA2 and ARID1A in two and loss of SMARCB1 and SMARCA2 in one. Retained expression of claudin‐4 was seen in 39% of the anaplastic carcinomas and within the mucinous component of all tumours. Rhabdoid morphology was associated with poor prognosis [stages III or IV disease (six of six, 100% versus four of 14, 29%; P = 0.0108] and death from disease (three of four, 75% versus one of 13, 8%; P = 0.0223). Although loss of a SWI/SNF protein was not significantly associated with death from disease (three of five, 60% versus one of 12, 8%; P = 0.0525), it showed a trend in correlation with poor prognosis and was often noted in tumours with rhabdoid morphology within this small cohort. Conclusions: Our report adds to the growing list of female genital tract malignancies with loss of SWI/SNF proteins, underlining their broad differential diagnosis and the importance of careful, context‐dependent interpretation of SWI/SNF protein loss. [ABSTRACT FROM AUTHOR]

Background: Existing data from several reports on the association between lipid profile and ovarian tumour (OT) suggests divergent conclusions. Our aim was to examine whether circulating lipid profile: total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) differed between cases and non-cases of OT.Methods: Electronic repositories; PUBMED, EMBASE and Cochrane library were explored through December 2019 to retrieve published articles for inclusion in the meta-analysis after quality assessment. Heterogeneity was assessed using I2 statistics, the effect of individual studies on the overall effect size was tested using sensitivity analysis and funnel plot was used to evaluate publication bias.Results: Twelve studies, involving 1767 OT cases and 229,167 non-cases of OT were included in this meta-analysis and I2 statistics ranged between 97 and 99%. Mean circulating TC (- 16.60 [- 32.43, - 0.77]mg/dL; P = 0.04) and HDL (- 0.25[- 0.43, - 0.08]mmol/L; P = 0.005) were significantly lower among OT cases compared to non-OT cases.Conclusion: Decreased TC and HDL profiles were observed among subjects with OT in this collection of reports. The implications of TC and HDL in tumour manifestations and growth need to be validated in a large multi-ethnic longitudinal cohort adjusting for relevant confounders. [ABSTRACT FROM AUTHOR]

A 12 year old dog was submitted for a routine pregnancy examination. The information received from the owner was that the animal had been mated 45 days previously. During abdominal palpation, a hard, round mobile structure was detected. Extension of the tumour from the right ovary into the abdomen was determined by ultrasonography. Hemogram, blood biochemistry, estradiol and serum progesterone analyzes were performed before surgery. An ovariohysterectomy was performed and 1.6 L of ascites fluid aspirated. A tumour in the right ovary weighing 1.3 kg was detected. Histopathological examination revealed ovarian papillary cystadenoma. One month after the operation, the animal showed good general condition, however, five months after the operation, the state of health deteriorated. Euthanasia followed this, because the metastases were detected in the repeated laparotomy operation. As a result, Increased serum E2 and E2/P4 ratio (3.15), and sonographically detectable abdominal mass and ascites could be useful for the dectection of the ovarian tumour in bitch. In case of rapidly growing papillary adenomas, frequent post-operative controls should be recommendable.

Aim of the study: To retrospectively evaluate how the International Ovarian Tumor Analysis (IOTA) simple rules used in ultrasound examinations estimate the probability of malignant and benign tumour occurrence in the studied population. Material and methods: The study was performed on a group of 425 patients with ovarian tumours operated in the Clinic of Surgical and Oncological Gynecology at the Medical University of Lodz in the years 2014-2015. Adnexal tumours were rated according to IOTA simple rules, classifying them as probably malignant, probably benign, or unclassified. The results of the study were compared with final histopathological results. The statistical analysis was performed using STATISTICA 13 PL with Medical Pack. Results: We analysed data on n = 43 (11%) patients with malignant, n = 346 (86%) patients with benign, and n = 12 (3%) patients with borderline tumours, respectively. Malignant tumour patients were significantly older (mean age 61.0 ±11.6 vs. 43.6 ±16.2 years, p < 0.001), had higher BMI (mean 27.3 ±7.0 vs. 25.2 ±5.2, p < 0.05), more pregnancies (median 2 vs. 1, p = 0.001), and higher cancer antigen 125 (CA 125) concentrations (median 251.5 vs. 18.5, p < 0.001) than patients with a benign tumour. Also, they more often suffered from diabetes mellitus (19% vs. 8%, p = 0.02) and arterial hypertension (60% vs. 42%, p < 0.01) than benign tumour patients. Conclusions: In our study, IOTA performance in predicting or ruling out a malignant tumour was highly satisfactory and similar to that of CA 125. Both the methods may be complementary and used to assess the risk of malignant vs. benign ovarian neoplasm, although the context of other clinical variables may also be important. [ABSTRACT FROM AUTHOR]

Circadian rhythms at a central and peripheral level are operated by transcriptional/translational feedback loops involving a set of genes called 'clock genes' that have been implicated in the development of several diseases, including malignancies. Dysregulation of the Clock system can influence cancer susceptibility by regulating DNA damage and repair mechanisms, as well as apoptosis. A number of oncogenic pathways can be dysregulated via clock genes' epigenetic alterations, including hypermethylation of clock genes' promoters or variants of clock genes. Clock gene disruption has been studied in breast, lung and prostate cancer, and haematological malignancies. However, it is still not entirely clear whether clock gene disruption is the cause or the consequence of tumourigenesis and data in endocrine neoplasms are scarce. Recent findings suggest that clock genes are implicated in benign and malignant adrenocortical neoplasias. They have been also associated with follicular and papillary thyroid carcinomas and parathyroid adenomas, as well as pituitary adenomas and craniopharyngiomas. Dysregulation of clock genes is also encountered in ovarian and testicular tumours and may also be related with their susceptibility to chemotherapeutic agents. The most common clock genes that are implicated in endocrine neoplasms are PER1, CRY1; in most cases their expression is downregulated in tumoural compared to normal tissues. Although there is still a lot to be done for the better understanding of the role of clock genes in endocrine tumourigenenesis, existing evidence could guide research and help identify novel therapeutic targets aiming mainly at the peripheral components of the clock gene system. [ABSTRACT FROM AUTHOR]

Granulosa Cell Tumour (GCT) is a rare malignant ovarian tumour. Adult GCT variety is more common than juvenile variety and occurs usually in postmenopausal women. It showed a spectrum of the imaging findings due to various histological appearances. It may present with solid masses, multilocular cystic lesion or completely cystic lesion. We present an interesting case which was present in a young female (27-year-old), who presented with a large lump in the abdomen. On Magnetic Resonance Imaging (MRI) there was a large well-defined, multilocular cystic lesion at superior aspect of the lesion and complex cystic, solid mass at inferior aspect. Functional MRI like diffusion weighted imaging which provides good image contrast helped in determining the malignancy despite a benign diagnosis on ultrasound guided biopsy and furthering the patient for histopathological examination to come to a final diagnosis. [ABSTRACT FROM AUTHOR]

The present case is of a rare malignant steroid cell tumour which accounts for less than 0.1% of ovarian tumours. Symptoms of virilisation on presentation were noted and patient had an ovarian tumour which turned out to be malignant steroid cell tumour. The present case is unique because presentation in post menopausal age group is rare and the case being malignant is unusual.

Sertoli–Leydig Cell Tumours (SLCT) accounts for less than 0.5% of all ovarian neoplasms. Presence of mesenchymal heterologous elements in a poorly differentiated SLCT is extremely uncommon. It not only causes diagnostic difficulty but also renders an aggressive behaviour to the tumour. We report a rare case of poorly differentiated SLCT with cartilage and rhabdomyoblastic differentiation along with review of literature.

The overall incidence of ovarian tumours in pregnancy is 2.4-5.7% and because of the asymptomatic nature of ovarian mass it is usually diagnosed in late pregnancy when there may be symptoms produced by it. Due to the similarity of symptoms, there has always been perplexity when a patient presents with mass per abdomen in pregnancy. During pregnancy, mild hydronephrosis is considered a normal phenomenon and may be present in up to 90% of pregnancies. Dilation is usually more pronounced on the right kidney and after mid-pregnancy. Here, we present a case which presented as a huge abdominal mass in pregnancy which was mistaken to be an ovarian mass in pregnancy and then later diagnosed as pyonephrosis.

Ovarian Cancer (OC) is the most fatal condition among all gynaecologic malignancies. The survival rate of early and advance stage of OC is 80-90% and 15-20% respectively. This data constitutes the need of a novel biomarker for early diagnosis, which may distinguish malignant tumours from benign ovarian cysts. Presently in practice there are three screening techniques: bimanual pelvic examination, serum Cancer Antigen (CA) 125 and transvaginal ultrasound. Bimanual pelvic examination can detect only 1 in 10,000 ovarian cancers in women presenting to the clinic. CA 125 is raised in 80% of cases of ovarian carcinoma; however, if the carcinoma is limited to ovary, the raised value is seen only in 50% of women. It is mainly useful in postmenopausal women. Ultrasonography also lacks specificity and sensitivity, apart from being costly as a screening test. Studies are going on for search of a biomarker, which may complement CA 125. Most common among these are Human Epididymis Protein 4 (HE4), Thrombopoietin (TPO), CA19-9, human kallikrein 10, human kallikrein 6, osteopontin, claudin 3, DF3 (murine monoclonal antibody), vascular endothelial growth factor, MUC1, mesothelin etc. In 2008, FDA approved the use of HE4 assay for monitoring of progressive disease in patients with epithelial ovarian cancer. Similarly TPO also holds the promise of being a biomarker to complement CA125 and many studies are indeed available to support the association of TPO and OC. In this review article we have summarised and analysed such studies.