Karagoz Ahmet, Alis Deniz, Seker Mustafa Ege, Zeybel Gokberk, Yergin Mert, Oksuz Ilkay, Karaarslan Ercan
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
Artificial Intelligence and Information Technologies, Hevi AI Health, Istanbul, Turkey.
Insights Imaging. 2023 Jun 19;14(1):110. doi: 10.1186/s13244-023-01439-0.
To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning.
We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa.
The PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning.
The state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets.
A self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice.
评估在大规模双参数MRI数据上训练的自适应深度网络在检测来自不同人口统计学男性的外部多中心数据中具有临床意义的前列腺癌（csPCa）的有效性；研究迁移学习的优势。
我们使用了两个样本：（i）公开可用的多中心和多供应商前列腺影像：癌症人工智能（PI-CAI）训练数据，由1500次双参数MRI扫描组成，以及其未见过的验证和测试样本；（ii）内部多中心测试和迁移学习数据，包括1036次和200次双参数MRI扫描。我们在PI-CAI数据上使用概率性前列腺掩膜训练了一个自适应3D nnU-Net模型，并在有和没有迁移学习的情况下，在隐藏的验证和测试样本以及内部数据上评估其性能。我们使用受试者工作特征（AUROC）曲线下面积来评估检测csPCa时的患者水平性能。
PI-CAI训练数据中有425次扫描存在csPCa，而内部测试和微调数据中分别有288次和50次扫描存在csPCa。nnU-Net模型在隐藏的验证和测试数据上的AUROC分别为0.888和0.889。该模型在内部测试数据上的AUROC为0.886，使用迁移学习时性能略有下降至0.870。
使用在大规模双参数MRI数据上训练的前列腺掩膜的先进深度学习方法在检测具有不同特征的内部和外部测试数据中的csPCa方面具有高性能，证明了深度学习在数据集内部和跨数据集的稳健性和通用性。
一个利用前列腺掩膜并在大规模双参数MRI数据上训练的自适应深度网络，在准确检测不同数据集中具有临床意义的前列腺癌方面是有效的，突出了深度学习方法在临床实践中改善前列腺癌检测的潜力。
