■经直肠超声引导下的前列腺活检是前列腺癌诊断测试的黄金标准,但它是一种非靶向穿刺的侵入性检查,具有很高的假阴性率。
■在这项研究中,我们旨在开发一种基于多参数MRI(mpMRI)图像的计算机辅助前列腺癌诊断方法.
■我们回顾性收集了106例经前列腺活检诊断后接受根治性前列腺切除术的患者。MPMRI图像,包括T2加权成像(T2WI),弥散加权成像(DWI),和动态对比度增强(DCE),并进行了相应的分析。我们在相同水平的三个连续MRI轴向图像上提取了关于肿瘤和良性区域的感兴趣区域(ROI)。获得433张MPMRI图像的ROI数据,其中良性202例,恶性231例。其中,使用50张良性和50张恶性图像进行训练,333张图像用于验证。五个主要特征组,包括直方图,GLCM,GLGCM,基于小波的多分数布朗运动特征和Minkowski函数特征,从MPMRI图像中提取。用MATLAB软件对选定的特征参数进行了分析,选取了3种准确度较高的分析方法。
■通过基于mpMRI图像的前列腺癌识别,我们发现该系统使用58个纹理特征和3种分类算法,包括支持向量机(SVM),K-近邻(KNN),和合奏学习(EL),表现良好。在基于T2WI的分类结果中,SVM的最优准确率和AUC值分别为64.3%和0.67。在基于DCE的分类结果中,支持向量机的最优准确率和AUC值分别为72.2%和0.77。在基于DWI的分类结果中,集成学习达到最佳的准确性以及AUC值为75.1%和0.82。在基于所有数据组合的分类结果中,支持向量机的最优准确率和AUC值分别为66.4%和0.73。
■提出的计算机辅助诊断系统对前列腺癌的诊断提供了良好的评估,这可以减轻放射科医生的负担,提高前列腺癌的早期诊断。
UNASSIGNED: Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate.
UNASSIGNED: In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images.
UNASSIGNED: We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected.
UNASSIGNED: Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73.
UNASSIGNED: The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.