AIMC Topic: Ovarian Neoplasms
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Accuracy of Large Language Model-based Automatic Calculation of Ovarian-Adnexal Reporting and Data System MRI Scores from Pelvic MRI Reports.
Radiology
Apr 1, 2025
Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automatic assignment of O-RADS scores from reports could increase adoption and accuracy. Purpose To evaluate...
Exploring Ovarian Cancer Prediction Models and Potential Markers Using Machine Learning.
Annals of clinical and laboratory science
Mar 1, 2025
OBJECTIVE: To develop machine learning models, facilitate a more accurate diagnosis of ovarian cancer (OC), and explore potential markers.
Ovarian-adnexal reporting and data system MRI scoring: diagnostic accuracy, interobserver agreement, and applicability to machine learning.
The British journal of radiology
Feb 1, 2025
OBJECTIVES: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning.
A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
Jan 20, 2025
OBJECTIVES: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.
Multi-omics analysis and experiments uncover the link between cancer intrinsic drivers, stemness, and immunotherapy in ovarian cancer with validation in a pan-cancer census.
Frontiers in immunology
Jan 1, 2025
BACKGROUND: Although immune checkpoint inhibitors (ICIs) represent a substantial breakthrough in cancer treatment, it is crucial to acknowledge that their efficacy is limited to a subset of patients. The heterogeneity and stemness of cancer render it...
Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma.
Clinical and translational medicine
Sep 1, 2024
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.
Radiology. Artificial intelligence
Sep 1, 2024
Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study in...
[A lightweight recurrence prediction model for high grade serous ovarian cancer based on hierarchical transformer fusion metadata].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Aug 25, 2024
High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether ...
Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.
JCO clinical cancer informatics
Jul 1, 2024
PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep ...
An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.
Cancer medicine
Jun 1, 2024
BACKGROUND: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions...
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