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.

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.

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.

Sertoli-Leydig cell tumours (SLCTs) are rare, mixed sex-cord stromal tumours composed of varying proportions of both Sertoli and Leydig cells, which account for <0.5% of all ovarian tumours. The cytomorphologic features of SLCTs are not well described in literature. Herein, we describe the cytomorphologic features of an SLCT at an uncommon metastatic site in a young female. Sertoli-Leydig cell tumours (SLCTs) are rare, mixed sex-cord stromal tumours composed of varying proportions of both Sertoli and Leydig cells, which account for <0.5% of all ovarian tumours. The cytomorphologic features of SLCTs are not well described in literature. Herein, we describe the cytomorphologic features of an SLCT at an uncommon metastatic site in a young female.

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.

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.

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.What do the 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.What are the implications of 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.

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.
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.
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 < 0.001). Muscle wasting mechanisms related with p-p38 MAPK, FOXO3, and p-AMPKα were highly activated in concurrence with an elevation in serum activin A. Dramatic fat loss was also observed in this mouse model with decreased fat mass (n ≥ 6) and white adipocytes sizes (n = 6) (P < 0.0001). The adipose tissue wasting was based on thermogenesis, supported by the up-regulation of uncoupling protein 1 (UCP1). Fibrosis in adipose tissue was also observed in concurrence with adipose tissue loss (n ≥ 13) (p < 0.0001).
Our novel preclinical CAC mouse model mimics human CAC phenotypes and serum biomarkers. The mouse model in this study showed proteolysis in muscle atrophy, browning in adipose tissue wasting, elevation of serum activin A and GDF15, and atrophy of pancreas and liver. This mouse line would be the best preclinical model to aid in clarifying molecular mediators of CAC and dissecting metabolic dysfunction and tissue atrophy during the progression of CAC.

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.

BACKGROUND: Hilus cell tumours is considered an uncommon branch of androgen producing neoplasms that accounts for < 5% of all ovarian tumours. They are mostly benign and have characteristic gross and microscopic features. Here we present the first case of a hilus cell tumour in association with bilateral serous cystadenomas.
METHODS: A 65-year-old lady with no symptoms of virilization, presented with postmenopausal dysfunctional uterine bleeding and radiological investigations revealing bilateral ovarian cysts that required a total abdominal hysterectomy with bilateral salpingo-oophorectomy. Gross and microscopic evaluation confirmed the diagnosis of hilus cell tumour associated with bilateral serous cystadenomas.
CONCLUSIONS: This was the first case of hilus cell tumour in association with bilateral serous cystadenomas of the ovaries. Although, majority of hilus cell tumours that have been reported in the literature were benign, further studies are required to determine the behavior of the disease.