Study Progress of Radiomics with Machine-learning for Precision Medicine in Bladder Cancer Management
- 1Sichuan University, China
- 2West China Hospital, Sichuan University, China
Bladder cancer is fatal cancer that happens in the genitourinary tract with quite high morbidity and mortality annually. The high level of recurrence rate ranging from 50% to 80% makes bladder cancer one of the most challenging and costly diseases to manage. Faced with various problems in existing methods, a recently emerging concept for the measurement of imaging biomarkers and extraction of quantitative features called “radiomics” shows great potential in the application of detection, grading and follow-up management of bladder cancer. Furthermore, Machine-learning (ML) algorithms on the basis of “big data” are fueling the powers of radiomics for bladder cancer monitoring in the era of precision medicine. Currently, the usefulness of the novel combination of radiomics and ML has been demonstrated by a large number of successful cases. It possesses outstanding strengths including non-invasive, low-cost and high-efficiency，which may serve as a revolution to tumor assessment and emancipate workforce. However, for the extensive clinical application in the future，more efforts should be made to break down the limitations caused by technology deficiencies，inherent problems during the process of radiomic analysis, as well as the quality of present studies.
Keywords: Radiomics, machine-learning, Bladder cancer, Full-cycle management, precision medicine
Received: 19 Aug 2019;
Accepted: 08 Nov 2019.
Copyright: © 2019 Ge, Chen, Yan, Zhao, Zhang, A and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Jiaming Liu, Sichuan University, Chengdu, China, JM3099@163.com