ArticleCancers2019
Mining Prognosis Index of Brain Metastases Using Artificial Intelligence.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- An Interpretable AdaBoost Model for 1-Year Readmission Risk Prediction in AECOPD Patients with Hypertension.International journal of chronic obstructive pulmonary disease · 2026Article
- Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts.Diagnostics (Basel, Switzerland) · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.Cancers · 2024Review
- Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer.Cureus · 2022Review
- Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction.Entropy (Basel, Switzerland) · 2021Article
- Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives.International journal of biological sciences · 2021Review
- Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.European journal of hybrid imaging · 2020Review
- Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model.Medicine · 2020Observational
- Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment.IEEE access : practical innovations, open solutions · 2020Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient's prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%, and 1.83% higher than that of the traditional statistical method, sequential feature selection (SFS), mutual information with particle swarm optimization(MIPSO), and mutual information with sequential feature selection (MISFS), respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.
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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.