目的:该研究的目的是在外部验证两种AI模型,用于对前列腺mpMRI序列进行分类和对T2WI上的前列腺进行分割。
方法:回顾性收集了来自两家医院的719例患者的MpMRI数据,利用来自四个不同供应商的九台MR扫描仪,在2018年2月至2022年5月期间。Med3D深度学习预训练架构用于执行图像分类,使用UNet-3D分割前列腺。通过模式将图像分类为九种图像类型之一。使用T2WI图像验证分割模型。通过测量DSC来评估分割的准确性,VS,AHD。最后,对于不同的MR场强和序列,比较了模型的功效。
结果:从719个MR研究中获得了20,551个图像组。分类模型准确率为99%,与0.932的卡帕。精度,召回,九种图像类型的F1值具有统计学上的显着差异,(均P<0.001)。扫描仪的精度为1.436T,1.5T,3.0T为87%,86%,98%,分别(P<0.001)。对于分割模型,DSC中位数为0.942~0.955,VS中位数为0.974~0.982,AHD中位数为5.55~6.49mm,分别。对于三种不同的磁场强度,这些值也具有统计学上的显着差异(所有P<0.001)。
结论:用于mpMRI图像分类和前列腺分割的AI模型在外部验证过程中表现良好,这可以提高mpMRI测量前列腺体积和癌症检测的效率。
结论:这些模型可以大大提高癌症检测的工作效率,测量前列腺体积和引导活检。
The aim of the study was to externally validate two AI models for the classification of prostate mpMRI
sequences and segmentation of the prostate gland on T2WI.
MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and
sequences.
20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001).
The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI.
These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies.