Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Abstract

Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging (MRI), image features from T2-weighted images (T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and central gland (CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features (10/12) had significant difference (P<0.01) between PCa and non-PCa in the PZ, while only five features (sum average, minimum value, standard deviation, 10th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.prostate cancer, magnetic resonance imaging, T2WI, diagnosis, computer-assisted Citation:

Computer-aided Detection of Prostate Cancer with MRI

“…The combination of anatomic (T2W) images and functional techniques has been shown to increase the accuracy of MR imaging for diagnosis of PCa. Table 1 compares the performance of the major published prostate CADx systems [13, 14, 16–18, 22, 26, 27, 36, 37, 39, 51, 52, 54–57, 62, 122, 129, 140–151]. Chan et al.…”

A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

“…The parameters of SVM are chosen as follows: the penalty factor is set to 100 and the RBF kernel parameter is set to 0.01 [29]. The parameters of ANN are selected as follows: the number of the hidden neurons = 20, the maximum number of the iterations = 500, the learning rate = 0.1, and the training error = 0.001 [30]. The neighborhood number of KNNC is set to 7 [31].…”

Computer-aided diagnosis of prostate cancer with MRI

“…The CAD system achieved an area under the receiver operating curve values equal to 90.0%±7.6%. Zhao et al proposed a CAD system [38] that uses 12, quantitative image features extracted from prostate T2w MRI. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and the central gland (CG), respectively.…”