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.

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.

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.

UNASSIGNED: Human telomerase reverse transcriptase (hTERT), a crucial enzyme for telomere maintenance, has been associated with the development of ovarian cancer (OC). The 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: An increased methylation of hTERT promoter is related to ctDNAs and tumour tissues of patients with ovarian magnificent tumour.

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.
CONCLUSIONS: 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.