AIMC Topic: Ovarian Neoplasms
Clear Filters Showing 121 to 130 of 232 articles
Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 16, 2022
Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with recurrent cancer and decease. Surgical debulking of tumors following chemotherapy is the conventiona...
Application of artificial intelligence in the diagnosis and prognostic prediction of ovarian cancer.
Computers in biology and medicine
May 13, 2022
In recent years, the wide application of artificial intelligence (AI) has dramatically improved the work efficiency of clinicians and reduced their workload. This review provides a glance at the latest advances in AI-assisted diagnosis and prognostic...
A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.
Archives of gynecology and obstetrics
May 9, 2022
In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features t...
Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.
Radiology
Apr 12, 2022
Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and ...
Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.
Oxidative medicine and cellular longevity
Mar 4, 2022
BACKGROUND: Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signature...
Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.
Scientific reports
Feb 23, 2022
Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cas...
Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.
The FEBS journal
Oct 8, 2021
Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK)...
Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.
Scientific reports
Sep 27, 2021
High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist ...
A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.
Scientific reports
Aug 19, 2021
Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplat...
Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 6, 2021
Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31032
- Neural Networks, Computer 29717
- Algorithms 26831
- Deep Learning 26345
- Artificial Intelligence 24290
- Robotics 13311
- Image Processing, Computer-Assisted 9521
- Retrospective Studies 9110
- Magnetic Resonance Imaging 6181
- Reproducibility of Results 5592
- Tomography, X-Ray Computed 4770
- Support Vector Machine 4686
- Computational Biology 4236
- Brain 4050
- Computer Simulation 3881
Recent Journals
- Scientific reports 5940
- Sensors (Basel, Switzerland) 4794
- PloS one 4015
- Neural networks : the official journal of the International Neural Network Society 2929
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1854
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1130
- Journal of chemical information and modeling 998
- Nature communications 943
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 826