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Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology
ZHANG Yunfeng  WANG Chao  QIAO Xiaoni  WANG Mengyu  ZHOU Fenghai 

Cite this article as: ZHANG Y F, WANG C, QIAO X N, et al. Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology[J]. Chin J Magn Reson Imaging, 2023, 14(2): 197-202. DOI:10.12015/issn.1674-8034.2023.02.035.


[Abstract] In recent years, the incidence of tumors of the urinary system has increased year by year, kidney cancer, bladder cancer (BCa), prostate cancer (PCa) have become important factors threatening the health of middle-aged and elderly people. The early detection and prognosis monitoring of urinary malignant tumors have increasingly become the hot spot of current research. Radiomics is an emerging diagnostic method in recent years, it enables non-invasive and quantitative evaluation of tissues by extracting and analyzing the characteristics of tissue heterogeneity, compared with traditional imaging, it can diagnose and differentiate lesions more accurately. From urology clinician's perspective, this paper reviews the current research progress of radiomics in preoperative diagnosis, efficacy evaluation, prognosis evaluation, and gene expression of urologic tumors.
[Keywords] adrenal gland neoplasms；kidney neoplasms；urinary bladder neoplasms；prostatic neoplasms；radiomics；magnetic resonance imaging

ZHANG Yunfeng1, 2   WANG Chao2   QIAO Xiaoni3   WANG Mengyu4   ZHOU Fenghai2*  

1 The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China

2 Department of Urology, Gansu Provincial People's Hospital, Lanzhou 730000, China

3 Department of Information Management, Gansu Provincial People's Hospital, Lanzhou 730000, China

4 School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

*Correspondence to: Zhou FH, E-mail: zhoufengh@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Gansu Province Key R&D Program Funding Project (No. 21YF5FA016).
Received  2022-10-08
Accepted  2023-01-17
DOI: 10.12015/issn.1674-8034.2023.02.035
Cite this article as: ZHANG Y F, WANG C, QIAO X N, et al. Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology[J]. Chin J Magn Reson Imaging, 2023, 14(2): 197-202. DOI:10.12015/issn.1674-8034.2023.02.035.

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