Next Article in Journal
PTEN in Lung Cancer: Dealing with the Problem, Building on New Knowledge and Turning the Game Around
Previous Article in Journal
Type 1 Sodium Calcium Exchanger Forms a Complex with Carbonic Anhydrase IX and Via Reverse Mode Activity Contributes to pH Control in Hypoxic Tumors
Open AccessArticle

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

1,†, 2,3,4,†, 2,4,* and 1,*
1
Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa 999078, China
2
Department of Computer and Information Science, University of Macau, Taipa 999078, China
3
Department of Electromechanical Engineering, Chongqing Industry&Trade Polytechnic, Chongqing 408000, China
4
Center of Medical Instruments, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai 519000, China
*
Authors to whom correspondence should be addressed.
Shigao Huang and Jie Yang contributed equally to this work.
Cancers 2019, 11(8), 1140; https://doi.org/10.3390/cancers11081140
Received: 22 June 2019 / Revised: 23 July 2019 / Accepted: 29 July 2019 / Published: 9 August 2019
  |  
PDF [2205 KB, uploaded 9 August 2019]
  |  

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. View Full-Text
Keywords: brain metastases; radiosurgery; prognosis index; artificial intelligence brain metastases; radiosurgery; prognosis index; artificial intelligence
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Huang, S.; Yang, J.; Fong, S.; Zhao, Q. Mining Prognosis Index of Brain Metastases Using Artificial Intelligence. Cancers 2019, 11, 1140.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Cancers EISSN 2072-6694 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top