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References from Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021–2026). Local targets link to admitted publications; unresolved targets remain external evidence.
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
10.3322/caac.21834 · 2024 · External reference
Outcomes of women with high-grade and low-grade advanced-stage serous epithelial ovarian cancer
10.1097/aog.0000000000001867 · 2017 · External reference
Ovarian cancer—epidemiology, classification, pathogenesis, treatment, and estrogen receptors’ molecular backgrounds
10.3390/ijms26104611 · 2025 · External reference
Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer
10.1038/nrc4019 · 2015 · External reference
Platinum resistant ovarian cancer: what is it, who to treat and how to measure benefit
10.1016/j.ygyno.2014.02.038 · 2014 · External reference
Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention
10.1186/s13048-026-01979-1 · 2026 · External reference
Analytical validation of a deep neural network algorithm for the detection of ovarian cancer
10.1200/cci.21.00192 · 2022 · External reference
Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study
10.1016/s2589-7500(23)00245-5 · 2024 · External reference
Genome-wide cell-free DNA fragmentation in patients with cancer
10.1038/s41586-019-1272-6 · 2019 · External reference
Precision gynecologic oncology: circulating cell-free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer
10.1038/s41598-022-23149-1 · 2022 · External reference
Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
10.1038/s41467-024-48667-6 · 2024 · External reference
Deep learning artificial intelligence predicts homologous recombination deficiency and platinum response from histologic slides
10.1200/jco.23.02641 · 2024 · External reference
Survival prediction and risk stratification in R0-resected ovarian cancer: a multi-modal deep learning approach
10.1038/s41698-025-01263-3 · 2026 · External reference
Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study
10.1016/j.ejca.2024.113532 · 2024 · External reference
Artificial intelligence applications in ovarian cancer detection: a systematic literature review of deep learning approaches and clinical translation challenges
10.1016/j.critrevonc.2026.105126 · 2026 · External reference
Artificial intelligence for single-omics in ovarian cancer: a methodological review
10.1016/j.ijgc.2025.104452 · 2026 · External reference
An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators
10.1186/s13048-024-01365-9 · 2024 · External reference
AI-derived blood biomarkers for ovarian cancer diagnosis: systematic review and meta-analysis
10.2196/67922 · 2025 · External reference
Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study
10.1016/j.eclinm.2024.102923 · 2024 · External reference
Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and Malignant ovarian tumours: a multi-class classification exploratory study
10.1186/s12880-024-01251-2 · 2024 · External reference
Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer
10.1016/j.isci.2024.109403 · 2024 · External reference
Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and Malignant adnexal masses
10.1038/s41698-026-01320-5 · 2026 · External reference
Detection of ovarian cancer using plasma cell-free DNA methylomes
10.1186/s13148-022-01285-9 · 2022 · External reference
Plasma cell-free DNA methylation analysis for ovarian cancer detection: analysis of samples from a case-control study and an ovarian cancer screening trial
10.1002/ijc.34757 · 2024 · External reference
Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers
10.1158/2159-8290.cd-24-0393 · 2025 · External reference
Predictive value of machine learning for platinum chemotherapy responses in ovarian cancer: systematic review and meta-analysis
10.2196/48527 · 2024 · External reference
Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer
10.1038/s41598-025-99930-9 · 2025 · External reference
Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer
10.1186/s12885-024-11989-1 · 2024 · External reference
A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer
10.1186/s12885-025-14265-y · 2025 · External reference
MMHC-OCPR: prediction of platinum response and recurrence risk in ovarian cancer with multimodal deep learning
10.3390/biomedicines14020348 · 2026 · External reference
A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy
10.1038/s41598-025-98434-w · 2025 · External reference
Global oncology: tackling disparities and promoting innovations in low- and middle-income countries
10.1200/edbk-25-473930 · 2025 · External reference
Leveraging digital technologies to reduce cancer disparities in low-income and middle-income countries
10.1016/j.landig.2025.100937 · 2025 · External reference
Artificial intelligence in ultrasound diagnoses of ovarian cancer: a systematic review and meta-analysis
10.3390/cancers16020422 · 2024 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
10.1038/s41591-022-01772-9 · 2022 · External reference
Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines
10.1186/s13063-020-04951-6 · 2021 · External reference
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
10.1136/bmj-2024-081554 · 2025 · External reference
Nationwide real-world implementation of AI for cancer detection in population-based mammography screening
10.1038/s41591-024-03408-6 · 2025 · External reference
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study
10.1016/s1470-2045(23)00298-x · 2023 · External reference