Purpose To investigate and implement the utilization of deep learning for calculating the prostate volume in T2-weighted MR images,and to compare it with the prostate volume calculated using the prolate ellipsoid formula.Materials and Methods T2-weighted MR images and diagnostic reports of 180 patients pathologically confirmed benign prostatic hyperplasia and 251 patients with pathologically confirmed prostate cancer in Renmin Hospital of Wuhan University were collected from October 2019 to February 2022.The prostate volume was calculated for each patient based on the diagnostic report using the prolate ellipsoid formula.The prostate was segmented using a U-Net-based deep learning model.The formula,prostate volume=sum(number of pixels in the prostate×size of each pixel×thickness),was used to obtain the prostate volume calculated using deep learning.The difference and consistency between the prostate volume calculated using deep learning and the prolate ellipsoid formula were compared.Results Bland-Alteman analysis revealed that the prostate volume calculated using the two methods in benign prostatic hyperplasia and prostate cancer showed high consistency,with only 5%and 6.37%of the data,respectively,falling outside the 95%confidence interval.Prostate volume consistency was higher in benign prostatic hyperplasia than in prostate cancer(ICC=0.803,0.686).There was a significant difference in prostate volume calculated by the two methods in both groups(Z=-10.742,-12.706,P<0.05),with a larger prostate volume calculated using deep learning.Conclusion Deep learning remains consistent with the prolate ellipsoid formula in calculating prostate volume.Therefore,utilizing deep learning for calculating MR prostate volume holds vast prospects,but further improvement is needed.
Prostatic hyperplasiaProstatic neoplasmsDeep learningMagnetic resonance imagingProstate segmentationProstate volumeProlate ellipsoid formula
NETL
NSTL
万方数据
