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

By Xinggang Wang, W. Yang, J. Weinreb, Juan Han, Qiubai Li, Xiangchuang Kong, Yongluan Yan, Zan Ke, Bo Luo, Tao Liu and 1 more. 2017

186 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Metastasis [290] A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks comprising”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, methodology): “…used MRI images from 172 patients with prostate cancer to train and test a DNN (developed using Caffe deep learning framework by Berkeley AI Research) that could distinguish prostate cancer from benign prostate conditions (such as prostate gland enlargement) with a reported AUC of 0.84 (17).”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, background): “…to a variety of tasks such as classiﬁcation of prostate MR images to distinguish patients with prostate cancer from patients with benign prostate conditions [26], identiﬁcation of CT images with pulmonary tuberculosis [27], and classiﬁcation of radiographs to identify hip osteoarthritis [28].”
Deep learning with convolutional neural network in radiology (2018, unknown): “For some tasks, CNNs achieved higher performance compared to conven‐ tional machine learning methods [43].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[60] DCNN MRI In this study the predictive test was conducted both with deep learning and non-deep-learning.”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “It has been shown with other related applications, including bioimage analysis (Janowczyk and Madabhushi 2016; Wang et al.2017 ) and compound-target interaction prediction (Ma et al.2015; Xu et al. 2017) that when the feature spaces are large enough, deep-learning machine learning models can learn…”