ArticleFrontiers in oncology2021
A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.Scientific reports · 2025Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features.Frontiers in oncology · 2024Article
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 2024Article
- Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.PloS one · 2024Article
- Article
- Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.Scientific reports · 2023Article
- Review
- Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.Scientific reports · 2023Article
- Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis.Scientific reports · 2023Article
- Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.Frontiers in medicine · 2023Article
- ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.Scientific reports · 2022Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Informative Power Evaluation of Clinical Parameters to Predict Initial Therapeutic Response in Patients with Advanced Pleural Mesothelioma: A Machine Learning Approach.Journal of clinical medicine · 2022Article
- A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.PloS one · 2022Article
- Cancer Detection and Prediction Using Genetic Algorithms.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.