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.
Highlights
Of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
As described in the result section, the AUCs of deep learning with DCNN and non-deep learning with SIFT image feature and BoW model were 0.84 and 0.70 respectively
There was a significant difference between the AUCs in deep learning with DCNN and non-deep learning with SIFT image feature and BoW model (P = 0.0007 < 0.001)
Summary
Of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All methods were performed in the principles of the Declaration of Helsinki. Written informed consent was obtained from the subject. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained from Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, with pathologically confirmed PCa and prostate BCs including BPH and prostatitis. The patient’s cohort included 93 prostate BCs and 79 PCa patients. Patient characteristics of the cohort are summarized in Table
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