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

被引：0
作者：
Xinggang Wang
Wei Yang
Jeffrey Weinreb
Juan Han
Qiubai Li
Xiangchuang Kong
Yongluan Yan
Zan Ke
Bo Luo
Tao Liu
Liang Wang
机构：
[1] Department of Radiology,
[2] Tongji Hospital,undefined
[3] Huazhong University of Science and Technology,undefined
[4] Jiefang Road 1095,undefined
[5] School of Electronics Information and Communications,undefined
[6] Huazhong University of Science and Technology,undefined
[7] Luoyu Road 1037,undefined
[8] Department of Nutrition and Food Hygiene,undefined
[9] MOE Key Lab of Environment,undefined
[10] Hubei Key Laboratory of Food Nutrition and Safety,undefined
[11] Health,undefined
[12] School of Public Health,undefined
[13] Tongji Medical College,undefined
[14] Huazhong University of Science and Technology,undefined
[15] Hangkong Road 13,undefined
[16] Department of Radiology and Biomedical Imaging,undefined
[17] Yale University School of Medicine,undefined
[18] Department of Maternal and Child and Adolescent & Department of Epidemiology and Biostatistics,undefined
[19] School of Public Health,undefined
[20] Tongji Medical College,undefined
[21] Huazhong University of Science and Technology,undefined
[22] Hangkong Road 13,undefined
[23] Program in Cellular and Molecular Medicine,undefined
[24] Boston Children’s Hospital,undefined
[25] Department of Radiology,undefined
[26] Union Hospital,undefined
[27] Huazhong University of Science and Technology,undefined
[28] Jiefang Road 1277,undefined
[29] School of mechanical science and engineering,undefined
[30] Huazhong University of Science and Technology,undefined
[31] Luoyu Road 1037,undefined
[32] Department of Radiology,undefined
[33] Tongji Hospital,undefined
[34] Tongji Medical College,undefined
[35] Huazhong University of Science &Technology,undefined
[36] Jie-Fang-Da-Dao 1095,undefined
来源：
Scientific Reports | / 7卷
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摘要：
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.
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