ArticleBiomedical signal processing and control2018
A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer.
Article in Biomedical signal processing and control, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Emerging nanotechnologies and their role in early ovarian cancer detection, diagnosis and interventions.Journal of ovarian research · 2025Pooled it
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.Scientific reports · 2025Article
- Article
- Novel efficient reservoir computing methodologies for regular and irregular time series classification.Nonlinear dynamics · 2025Article
- Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation.Heliyon · 2024Article
- Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.Bioengineering (Basel, Switzerland) · 2024Article
- Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.Cancer medicine · 2024Article
- Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.Cancers · 2023Article
- A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.Scientific reports · 2023Article
- Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer.Journal of clinical medicine · 2023Article
- Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images.Oxidative medicine and cellular longevity · 2022Article
- An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals.Frontiers in systems neuroscience · 2021Article
- Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women.Cancers · 2020Article
- The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.Frontiers in aging neuroscience · 2020Article
- Statistical Approaches in the Studies Assessing Associations between Human Milk Immune Composition and Allergic Diseases: A Scoping Review.Nutrients · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.