High-grade serous ovarian carcinoma (HGSOC), the most common and lethal subtype of epithelial ovarian cancer, is now recognized to originate predominantly from the distal fallopian tube. This paradigm shift has established opportunistic salpingectomy (OS) as a promising strategy for primary prevention during otherwise indicated abdominal or pelvic surgery. However, the frequency, timing, and surgical distribution of previous opportunities for OS among patients subsequently diagnosed with HGSOC remain insufficiently characterized. To quantify previous surgical opportunities for OS before HGSOC diagnosis using complementary patient-level and procedure-level analyses. This retrospective cohort study included patients with histologically confirmed HGSOC who underwent primary debulking surgery or interval debulking surgery after neoadjuvant chemotherapy between 2016 and 2025. Clinicopathological characteristics, family and personal cancer history, germline genetic testing results, and previous abdominopelvic procedures were reviewed. A procedure was considered eligible for OS if it was elective, had been performed at least 12 months before HGSOC diagnosis, and occurred after completion of childbearing. Among 426 patients, 165 (38.7
Borderline ovarian tumors (BOTs) are epithelial ovarian tumors with a favorable prognosis, particularly in younger patients. Fertility-sparing surgery (FSS) is often preferred for patients desiring to preserve fertility. This study aimed to develop a machine learning (ML)-based prediction model to identify patients at high risk of recurrence after FSS. A multi-center retrospective study was conducted, including 589 BOT patients who underwent FSS between 2010 and 2023. Clinical, pathological, and laboratory data were collected, and ten ML algorithms were evaluated for their predictive performance. The dataset was preprocessed, and feature selection was conducted using univariate analyses and feature importance scores derived from preliminary ML models. The dataset was randomly split into training and test sets, and hyperparameter tuning was performed using grid search and cross-validation. Model performance was assessed using metrics such as AUC, accuracy, sensitivity, and specificity. The Gradient Boosting model demonstrated the highest accuracy, with an AUC of 0.94, sensitivity of 91
Ovarian tissue cryopreservation and transplantation is an important fertility preservation strategy, but cryoinjury and post-transplantation ischemia-reperfusion injury lead to massive follicle loss and limited graft lifespan. This study was performed to compare the ovarian protective effects of gonadotropin-releasing hormone agonist (GnRHa) versus combined oral contraceptive (COC) pretreatment during vitrification and autotransplantation, and to evaluate selected molecular markers associated with follicular development, apoptosis, and graft revascularization. Seventy-two six-week-old female C57BL/6 mice were allocated to four pretreatment arms (n = 18 per arm: untreated, GnRHa, COC, or saline vehicle) and further divided into pre-transplantation (fresh control, GnRHa-pretreated vitrification [GV], COC-pretreated vitrification [CV], vitrification-only [V]; n = 10 per group) and post-transplantation cohorts (fresh-transplanted [FT], GnRHa-pretreated vitrified-transplanted [GVT], COC-pretreated vitrified-transplanted [CVT], and vitrified-transplanted [VT]; n = 8 per group). Ovaries underwent 14-day GnRHa/COC pretreatment followed by two-step vitrification and subrenal capsule autotransplantation. Histomorphology, ultrastructure, and molecular markers were examined after warming, and follicular density, Ki-67 immunostaining of primordial follicles, apoptosis (cleaved caspase-3), graft vascular density (CD31), and serum hormones were assessed 4 weeks after grafting. Both GnRHa and COC pretreatment significantly reduced serum FSH and E₂ after 14 days. After warming, significant primordial follicle loss occurred in the CV and V groups (severest in the V group) but not in the GV group, with no significant difference between the GV and CV groups; folliculogenesis- and apoptosis-related markers did not differ among groups, although ultrastructural damage was mildest in the GV group. Four weeks post-autotransplantation, the GVT group exhibited a primordial follicle density not significantly different from the FT group and significantly higher than the VT group, with intermediate levels in the CVT group. Its proportion of Ki-67-positive primordial follicles did not differ significantly from that of FT and was significantly lower than in the CVT and VT groups; GDF9 and BMP15 protein levels did not differ significantly from FT but were significantly reduced in VT; AMH protein was lower than in FT in all vitrified-transplanted groups but highest in the GVT group. Cleaved caspase-3 scores in growing follicles remained statistically comparable to fresh controls in the GVT group but were significantly elevated in the CVT and VT groups; graft vascular density (CD31) did not differ among the transplanted groups. Serum E₂ and FSH did not differ significantly between the GVT and FT groups but were significantly impaired in the CVT and VT groups (E₂ was also significantly lower in GVT than in fresh controls); serum AMH decreased progressively across the FT, GVT, CVT and VT groups, remaining significantly higher in the GVT group than in the CVT and VT groups. GnRHa pretreatment is associated with preserved primordial follicular density, reduced post-transplantation primordial follicle activation, and attenuated apoptosis in growing follicles, leading to improved structural and functional outcomes relative to COC-pretreated and vitrification-only controls in the setting of ovarian tissue vitrification and autotransplantation.
Polycystic ovary syndrome (PCOS) is often associated with aberrant autophagy activation in granulosa cells, though the underlying mechanisms remain unclear. This study identified Autophagy-related protein 9B (ATG9B) as a key differentially expressed gene in PCOS granulosa cells through transcriptomic analysis of a public dataset. We demonstrated that ATG9B is significantly upregulated in PCOS and that its overexpression disrupts autophagy. Molecular docking and in vitro experiments revealed that the natural compound liensinine exerts a therapeutic effect by binding to ATG9B and subsequently modulating the AKT/mTOR signaling pathway. This action attenuates excessive autophagy and restores mitochondrial function in a DHT-induced PCOS cell model. Crucially, the therapeutic effects of liensinine were diminished upon ATG9B overexpression, confirming its central role. Our findings elucidate a novel mechanism by which liensinine targets ATG9B to regulate the AKT/mTOR axis and ameliorate aberrant autophagy, presenting a promising therapeutic strategy for PCOS. Summary This study elucidates the novel mechanism by which liensinine alleviates aberrant autophagy in PCOS by directly targeting ATG9B and modulating the AKT/mTOR signaling pathway, thereby advancing our understanding of autophagy dysregulation in PCOS and identifying a potential therapeutic target.
Polyendocrine metabolic ovarian syndrome (PMOS), formerly termed polycystic ovary syndrome (PCOS), is a heterogeneous endocrine and metabolic disorder with reproductive and metabolic manifestations. Ultrasound is the primary imaging modality for assessing polycystic ovarian morphology (PCOM) within the historical PCOS diagnostic framework, but its interpretation is affected by operator dependence, interobserver variability, image quality, and subjective assessment. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has increasingly been applied to ultrasound-based PCOS diagnosis, including automated image analysis, follicle segmentation, feature extraction, and diagnostic classification. This review synthesises evidence from 65 studies published between 2017 and 2026, identified through a structured search of major biomedical and engineering databases. Methodological quality and risk of bias were assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) in 53 eligible prediction-model studies, with substantial risk-of-bias concerns identified particularly in the analysis domain; results were visualised using ROBVIS. This review focuses specifically on AI-assisted ultrasound imaging, including multimodal studies only when ultrasound formed an integral component of the diagnostic framework. Reported diagnostic performance was generally high across individual studies, but direct comparison was limited by heterogeneity in datasets, imaging protocols, outcome definitions, preprocessing, model development, and validation strategies. Frequent reliance on retrospective, single-centre datasets, limited external validation, and inconsistent reporting constrain confidence in the generalisability and clinical applicability of existing AI models. Additional barriers to clinical translation include interpretability, data governance, regulatory requirements, and workflow integration. Future research should prioritise standardised reporting, multicentre datasets representing diverse populations, prospective external validation, transparent model development, and regulatory-compliant evaluation to establish the reliability and clinical applicability of AI-assisted ultrasound systems for PCOS diagnosis.
