Abstract

PURPOSE: Prostate cancer classification has significantly impact on the prognosis and treatment planning of patients. Currently, the classifying is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk-free. This study aims to learn discriminative features for prostate images and assist physicians to classify prostate cancer automatically.METHODS: We develop a novel multi-parametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pre-trained model to compute features from multi-parametric MRI images (mp-MRI) : T2w transaxial, T2w sagittal and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp-MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine-tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability.RESULTS: Two cohorts: 132 cases from our institutional review board approved patient database and 112 cases from the PROSTATEx-2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate cancer classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand-crafted feature based methods and existing deep learning models in prostate cancer classification with higher accuracy.CONCLUSION: The experiment results showed that the proposed method can learn discriminative features for prostate images and classify the cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine. This article is protected by copyright. All rights reserved.

View details for PubMedID 30597561