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Proceedings Paper

Radiomics analysis potentially reduces over-diagnosis of prostate cancer with PSA levels of 4-10 ng/ml based on DWI data
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Paper Abstract

Prostate specific antigen (PSA) screening is routinely conducted for suspected prostate cancer (PCa) patients. As this technique might result in high probability of over-diagnosis and unnecessary prostate biopsies, controversies on it remains especially for patients with “gray-zone” PSA levels, i.e. 4-10ng/ml. To improve the risk stratification of suspected PCa patients, Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) was released in 2015. Although PI-RADSv2 showed good performance in the detection of PCa, its specificity was relatively low for patients with gray-zone PSA levels. This indicated that over-diagnosis issue could not be dealt well by PI-RADSv2 in the gray zone. Addressing this, we attempted to validate whether radiomics analysis of Diffusion weighted Imaging (DWI) data could reduce over-diagnosis of PCa with gray-zone PSA levels. Here, 140 suspected PCa patients in Peking Union Medical College Hospital were enrolled. 700 radiomic features were extracted from the DWI data. Least absolute shrinkage and selection operator (LASSO) were conducted, and 7 radiomic features were selected on the training set (n=93). Based on these features, random forest classifier was used to build the Radiomics model, which performed better than PI-RADSv2 (area under the curve [AUC]: 0.900 vs 0.773 and 0.844 vs 0.690 on the training and test sets). Furthermore, the specificity values of Radiomics model and PI-RADSv2 was 0.815 and 0.481 on the test set, respectively. In conclusion, radiomics analysis of DWI data might reduce the over-diagnosis of PCa with gray-zone PSA levels.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501Z (13 March 2019); doi: 10.1117/12.2511497
Show Author Affiliations
Shuaitong Zhang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Yafei Qi, Peking Union Medical College Hospital (China)
Jingwei Wei, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Jianxing Niu, Beijing Tiantan Hospital, Capital Medical Univ. (China)
The Third Medical Ctr. of Chinese PLA General Hospital (China)
Dongsheng Gu, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Yuqi Han, Xidian Univ. (China)
Xiaohan Hao, Univ. of Science and Technology of China (China)
Yali Zang, Institute of Automation (China)
Jie Tian, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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