Table4_Computer-aided diagnosis of prostate cancer based on deep neural networks from multi-parametric magnetic resonance imaging.DOCX

Objectives: To evaluate a new deep neural network (DNN)–based computer-aided diagnosis (CAD) method, namely, a prostate cancer localization network and an integrated multi-modal classification network, to automatically localize prostate cancer on multi-parametric magnetic resonance imaging (mp-MRI) and classify prostate cancer and non-cancerous tissues.
Materials and methods: The PROSTAREx database consists of a “training set” (330 suspected lesions from 204 cases) and a “test set” (208 suspected lesions from 104 cases). Sequences include T2-weighted, diffusion-weighted, Ktrans, and apparent diffusion coefficient (ADC) images. For the task of abnormal localization, inspired by V-net, we designed a prostate cancer localization network with mp-MRI data as input to achieve automatic localization of prostate cancer. Combining the concepts of multi-modal learning and ensemble learning, the integrated multi-modal classification network is based on the combination of mp-MRI data as input to distinguish prostate cancer from non-cancerous tissues through a series of operations such as convolution and pooling. The performance of each network in predicting prostate cancer was examined using the receiver operating curve (ROC), and the area under the ROC curve (AUC), sensitivity (TPR), specificity (TNR), accuracy, and Dice similarity coefficient (DSC) were calculated.
Results: The prostate cancer localization network exhibited excellent performance in localizing prostate cancer, with an average error of only 1.64 mm compared to the labeled results, an error of about 6%. On the test dataset, the network had a sensitivity of 0.92, specificity of 0.90, PPV of 0.91, NPV of 0.93, and DSC of 0.84. Compared with multi-modal classification networks, the performance of single-modal classification networks is slightly inadequate. The integrated multi-modal classification network performed best in classifying prostate cancer and non-cancerous tissues with a TPR of 0.95, TNR of 0.82, F1-Score of 0.8920, AUC of 0.912, and accuracy of 0.885, which fully confirmed the feasibility of the ensemble learning approach.
Conclusion: The proposed DNN-based prostate cancer localization network and integrated multi-modal classification network yielded high performance in experiments, demonstrating that the prostate cancer localization network and integrated multi-modal classification network can be used for computer-aided diagnosis (CAD) of prostate cancer localization and classification.

研究目的：评估一种基于深度神经网络（deep neural network, DNN）的计算机辅助诊断（computer-aided diagnosis, CAD）新方法，该方法包含前列腺癌定位网络与集成多模态分类网络，旨在实现在多参数磁共振成像（multi-parametric magnetic resonance imaging, mp-MRI）上自动定位前列腺癌，并区分前列腺癌与非癌组织。

材料与方法：本研究所用PROSTAREx数据库包含「训练集」（204例受试者的330个疑似病灶）与「测试集」（104例受试者的208个疑似病灶）。成像序列涵盖T2加权成像、弥散加权成像、Ktrans成像及表观弥散系数（apparent diffusion coefficient, ADC）图像。针对异常病灶定位任务，本研究受V-net架构启发，设计了以mp-MRI数据为输入的前列腺癌定位网络，以实现前列腺癌的自动定位。结合多模态学习与集成学习的理念，集成多模态分类网络以mp-MRI多模态数据组合作为输入，通过卷积、池化等一系列操作完成前列腺癌与非癌组织的区分。采用受试者工作特征曲线（receiver operating curve, ROC）评估各网络的前列腺癌预测性能，并计算ROC曲线下面积（area under the ROC curve, AUC）、灵敏度（真阳性率，TPR）、特异度（真阴性率，TNR）、准确率及戴斯相似性系数（Dice similarity coefficient, DSC）。

结果：前列腺癌定位网络在前列腺癌定位任务中表现优异，与人工标注结果相比，平均定位误差仅为1.64 mm，误差率约为6%。在测试数据集上，该网络的灵敏度为0.92、特异度为0.90、阳性预测值（positive predictive value, PPV）为0.91、阴性预测值（negative predictive value, NPV）为0.93，戴斯相似性系数为0.84。与多模态分类网络相比，单模态分类网络的性能稍显不足。集成多模态分类网络在前列腺癌与非癌组织分类任务中表现最优，其真阳性率（TPR）为0.95、真阴性率（TNR）为0.82、F1分数为0.8920、ROC曲线下面积（AUC）为0.912、准确率为0.885，充分验证了集成学习方法的可行性。

结论：本研究提出的基于DNN的前列腺癌定位网络与集成多模态分类网络在实验中展现出优异性能，证实该两类网络可应用于前列腺癌定位与分类的计算机辅助诊断（CAD）。