Article Versions Notes

Liu J-C, Ruan X-H, Chun T-T, Yao C, Huang D, Wong H-L, Lai C-T, Tsang C-F, Ho S-H, Ng T-L, et al. MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study. Cancers. 2024; 16(17):2944. https://doi.org/10.3390/cancers16172944

Liu, Jia-Cheng, Xiao-Hao Ruan, Tsun-Tsun Chun, Chi Yao, Da Huang, Hoi-Lung Wong, Chun-Ting Lai, Chiu-Fung Tsang, Sze-Ho Ho, Tsui-Lin Ng, and et al. 2024. "MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study" Cancers 16, no. 17: 2944. https://doi.org/10.3390/cancers16172944

Liu, J.-C., Ruan, X.-H., Chun, T.-T., Yao, C., Huang, D., Wong, H.-L., Lai, C.-T., Tsang, C.-F., Ho, S.-H., Ng, T.-L., Xu, D.-F., & Na, R. (2024). MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study. Cancers, 16(17), 2944. https://doi.org/10.3390/cancers16172944