Journal of Ovarian Research最新文献

{"title":"Development of a surface plasmon resonance sensor for assessing infertility based on Anti-Mullerian hormone levels.","authors":"Hamed Mirshekari, Fatemeh Sabzalizadeh, Ramezan Ali Taheri, Khosro Khajeh","doi":"10.1186/s13048-026-02096-9","DOIUrl":"https://doi.org/10.1186/s13048-026-02096-9","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147774279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial habitats radiomics from multiparametric magnetic resonance imaging predict platinum resistance in epithelial ovarian cancer.","authors":"Lingling Lin, Huawei Wu, Chao Wang, Jialu Xu, Enhui Xin, Yafen Li, Jun Zhu, Jianli Yu, Yu Wang, Jiejun Cheng","doi":"10.1186/s13048-026-02097-8","DOIUrl":"https://doi.org/10.1186/s13048-026-02097-8","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147774332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endometrial HOXA-10, HOXA-11, ß-1 integrin, ECM-1, FAK, and CD44 immunohistochemical expressions in endometriosis-related recurrent IVF failure: a retrospective case-control study.","authors":"Esin Şahin Toruk, Elif Acar Kaplan, Gülistan Sanem Sarıbaş, Özlem Erdem, Ahmet Erdem, Mehmet Erdem","doi":"10.1186/s13048-026-02072-3","DOIUrl":"https://doi.org/10.1186/s13048-026-02072-3","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147728537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"miR-378a-3p participates in the decline of ovarian reserve function by targeting ZFP36L2 to regulate granulosa cell mitochondrial function and apoptosis.","authors":"Jiaxue Wang, Jingyan Song, Wen Chen, Qihui Liang, Haicui Wu, Lu Guan, Fang Lian","doi":"10.1186/s13048-026-02095-w","DOIUrl":"https://doi.org/10.1186/s13048-026-02095-w","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147728648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk prediction model of survival in patients with low-grade serous ovarian cancer: a multicenter Cohort study.","authors":"Ying Xue, Zhongshao Chen, Xuecheng Fang, Ran Chu, Mingbao Li, Yuanming Shen, Qin Yao, Baochen Fu, Tianyu Qin, Li Li, Xu Qiao","doi":"10.1186/s13048-026-02108-8","DOIUrl":"https://doi.org/10.1186/s13048-026-02108-8","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate prognostic prediction remains a significant challenge in the management of low-grade serous ovarian cancer (LGSOC), a rare and molecularly distinct histologic subtype. This study aimed to develop a prediction model visualized by nomogram to predict recurrence and survival outcomes in LGSOC patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on patients with LGSOC, using Cox regression to identify factors associated with recurrence and survival for further development of prediction model. The model's accuracy and discriminative ability were assessed with area under the receiver operating characteristic curve (AUC) and calibration curves. The predictive performance of the model and International Federation of Gynecology and Obstetrics (FIGO) staging was compared using the concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Additionally, a deep-learning-based prediction model was developed through regression analysis, with performance evaluated via Kaplan-Meier analysis and C-index.</p><p><strong>Results: </strong>A cohort of 155 patients with LGSOC was analyzed and four independent prognostic factors were identified and incorporated into a Cox regression-based model. The model demonstrated good calibration, as shown by calibration curves. Through internal validation, the model showed superior discriminatory ability over the FIGO staging system, with higher C-indexes for both disease-free survival (0.781 vs. 0.689) and overall survival (0.802 vs. 0.679), which was further confirmed by significant improvements in IDI and NRI. Additionally, the deep learning-based model based on this model was developed to evaluate potential non-linear relationships. This model achieved even higher predictive performance, with C-indexes of 0.907 for disease-free survival and 0.922 for overall survival.</p><p><strong>Conclusion: </strong>We developed a risk prediction model, visualized by a clinically practical nomogram to predict recurrence and survival outcomes in LGSOC patients. Additionally, the deep learning-based prediction model based on neural networks was developed, providing improved prognostic evaluation for these patients.</p>","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BMP15 regulates proliferation of sheep granulosa cells with different genotypes via the TGF-β/SMAD signaling pathway.","authors":"Qing Liu, Guiling Cao, Wentao Li, Ziyi Liu, Peng Wang, Xiaoyun He, Yufang Liu, Mingxing Chu","doi":"10.1186/s13048-026-02106-w","DOIUrl":"https://doi.org/10.1186/s13048-026-02106-w","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vitrification versus slow freezing for prepubertal ovarian tissue cryopreservation: insights from a porcine xenotransplantation model.","authors":"Yingchun Guo, Xiaoping Liu, Jianhong Chen, Yun Xie, Wenlong Su, Peng Sun, Haitao Zeng, Cong Fang, Xiaoyan Liang","doi":"10.1186/s13048-026-02099-6","DOIUrl":"https://doi.org/10.1186/s13048-026-02099-6","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Yin-deficiency to therapy: molecular mechanisms and drug discovery in high-grade serous ovarian cancer.","authors":"Ling Wu, Peihong Lin, Xuedan Lai, Yufang Lin, Zhuoling Jiang","doi":"10.1186/s13048-026-02105-x","DOIUrl":"https://doi.org/10.1186/s13048-026-02105-x","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.","authors":"Weimei Li, Yuhua Xia, Yongzhao Li, Xing Wu, Lin Shi","doi":"10.1186/s13048-026-02090-1","DOIUrl":"https://doi.org/10.1186/s13048-026-02090-1","url":null,"abstract":"<p><strong>Background: </strong>Ovarian cysts are a common pelvic disorder in women, and accurate differentiation between benign and malignant types is essential for guiding treatment decisions and prognostic evaluations. However, traditional ultrasound examinations heavily depend on the operator's experience, introducing subjectivity and diagnostic inconsistencies. In recent years, deep learning technologies have demonstrated strong potential in intelligent medical imaging diagnostics, offering innovative solutions for automated and precise classification of ovarian cysts.</p><p><strong>Results: </strong>Compared to subjective evaluations by senior ultrasound physicians (accuracy: 76.5%) and the O-RADS classification system (accuracy: 87.8%), the DenseNet121 model demonstrated a superior Area Under the Curve (AUC: 0.913 vs. 0.858, P < 0.05), indicating stronger overall discriminative ability.</p><p><strong>Conclusions: </strong>Deep learning models based on ultrasound images can effectively address noise and feature complexity in such imaging, enabling high-precision classification of benign and malignant ovarian cysts. These models hold strong potential for clinical adoption, providing physicians with objective and reliable decision-making support.</p>","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative differentiation of borderline and malignant ovarian tumors using interpretable machine learning.","authors":"Saber SamadiAfshar, Hossein Azizi, Mahla Masoudi, Sahel SamadiAfshar, Ali Nikakhtar, Thomas Skutella","doi":"10.1186/s13048-026-02062-5","DOIUrl":"https://doi.org/10.1186/s13048-026-02062-5","url":null,"abstract":"","PeriodicalId":16610,"journal":{"name":"Journal of Ovarian Research","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}