Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Abstract

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Figure 1

The ROC curves for PCa and prostate BCs patients differentiation of non-deep learning with SIFT image feature and BoW model versus deep learning with deep convolutional neural network (DCNN). Note: ROC curve: receiver operating characteristic curve. AUC: area under ROC. PCa = prostate cancer prostate BCs = prostate benign conditions BPH = benign prostatic hyperplasia.

Figure 2

The structure of deep learning with deep convolutional neural network (DCNN) for the automatic classification of a PCa or BCs patient with morphologic images (axial 2D T2-weighted imaging). A 288 × 288 × 3 MR image was input. Five convolution layers and two inner product layers with sizes were shown in the figure. A max-pooling layer and non-linear ReLU layer following each convolution layer. A max-pooling layer downsize feature map gradually as demonstrated. Finally, an output layer specified PCa patient probability on input image. Note: PCa = prostate cancer. Prostate BCs = prostate benign conditions. BPH = benign prostatic hyperplasia.