Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study

Bacchi, Stephen; Zerner, Toby; Dongas, John; Asahina, Adon Toru; Abou-Hamden, Amal; Otto, Sophia; Oakden-Rayner, Luke; Patel, Sandy

doi:10.1016/j.jocn.2019.10.003

Cited by 32 publications

(17 citation statements)

References 7 publications

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“…Abrol et al investigated differencing PsP from true PD in glioblastoma patients using radiomic analysis 39 . However, only a few studies proposed differentiating PsP from true PD based on the deep neural network framework 30 , 40 , 41 . The potential underlying reasoning for this is insufficient data, inability of deep learning to capture real variations in the data unless the data size is large enough.…”

Section: Discussionmentioning

confidence: 99%

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Lee

Wang

Türk

et al. 2020

Sci Rep

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Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post–T1pre and T2–FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

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Section: Discussionmentioning

confidence: 99%

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Lee

Wang

Türk

et al. 2020

Sci Rep

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“…Interestingly, models based solely on clinical or imaging features showed inferior performance, highlighting the importance of incorporation of clinical features. Bachhi and colleagues [ 88 ] used a CNN based on conventional MRI and DWI. In a cohort of 55 patients, the CNN model based on DWI and FLAIR sequences in combination achieved the highest diagnostic accuracy of 82% in the test dataset.…”

Section: Gliomamentioning

confidence: 99%

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

et al. 2020

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Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

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“…Nevertheless, in a few studies used advanced MRI imaging techniques (e.g. fMRI, DTI, DWI) and presented favourable results in comparison to standard imaging techniques [55,57].Furthermore, perfusion MRI imaging is another area that requires some elaborate postprocessing and the some initial investigations of the use of deep learning for perfusion MRI in neuro-oncology are ongoing [62][63][64].…”

Section: Discussionmentioning

confidence: 99%

“…Most studies use standard cT1-w, T1-w, T2-w or FLAIR MRI acquisitions, however the use of additional MRI sequences, like DWI, DTI or fMRI in the follow-up were described in three articles [55][56][57].…”

Section: Advanced Mrimentioning

confidence: 99%

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Current applications of deep-learning in neuro-oncological MRI

et al. 2021

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Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. Methods: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up.Results: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, autosegmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. Conclusion:Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.

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Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study

Cited by 32 publications

References 7 publications

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Current applications of deep-learning in neuro-oncological MRI

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