Meiyu Li1,2, Hailiang Tang3, Michael D Chan4, Xiaobo Zhou5, Xiaohua Qian2,4. 1. College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China. 2. Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China. 3. Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China. 4. Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA. 5. School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Abstract
PURPOSE: Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow-up magnetic resonance imaging (MRI). These similarities pose challenges to the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. METHODS: To address this challenge, we introduced a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, termed DC-AL GAN, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. We also constructed a multifeature selection module to concatenate features from different layers, contributing to more powerful features used for effectively discriminating between PsP and TTP. Finally, these discriminative features from the discriminator are used for classification by a support vector machine (SVM). Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were applied to evaluate the performance of this developed algorithm. RESULTS: The accuracy and AUC of DC-AL GAN for discriminating PsP and TTP after tenfold CV were 0.920 and 0.947. We also assessed the effects of different indicators (such as sensitivity and specificity) for features extracted from different layers to obtain a model with the best classification performance. CONCLUSIONS: The proposed model DC-AL GAN is capable of learning discriminative representations from GBM datasets, and it achieves desirable PsP and TTP classification performance superior to other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of PsP and TTP for GBM.
PURPOSE: Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow-up magnetic resonance imaging (MRI). These similarities pose challenges to the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. METHODS: To address this challenge, we introduced a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, termed DC-AL GAN, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. We also constructed a multifeature selection module to concatenate features from different layers, contributing to more powerful features used for effectively discriminating between PsP and TTP. Finally, these discriminative features from the discriminator are used for classification by a support vector machine (SVM). Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were applied to evaluate the performance of this developed algorithm. RESULTS: The accuracy and AUC of DC-AL GAN for discriminating PsP and TTP after tenfold CV were 0.920 and 0.947. We also assessed the effects of different indicators (such as sensitivity and specificity) for features extracted from different layers to obtain a model with the best classification performance. CONCLUSIONS: The proposed model DC-AL GAN is capable of learning discriminative representations from GBM datasets, and it achieves desirable PsP and TTP classification performance superior to other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of PsP and TTP for GBM.
Authors: Lan Lan; Lei You; Zeyang Zhang; Zhiwei Fan; Weiling Zhao; Nianyin Zeng; Yidong Chen; Xiaobo Zhou Journal: Front Public Health Date: 2020-05-12
Authors: Thomas C Booth; Mariusz Grzeda; Alysha Chelliah; Andrei Roman; Ayisha Al Busaidi; Carmen Dragos; Haris Shuaib; Aysha Luis; Ayesha Mirchandani; Burcu Alparslan; Nina Mansoor; Jose Lavrador; Francesco Vergani; Keyoumars Ashkan; Marc Modat; Sebastien Ourselin Journal: Front Oncol Date: 2022-01-31 Impact factor: 6.244