An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases

Tateishi, Machiko; Nakaura, Takeshi; Kitajima, Mika; Uetani, Hiroyuki; Nakagawa, Masataka; Inoue, T; Kuroda, Jun-ichiro; Mukasa, Akitake; Yamashita, Yasuyuki

doi:10.1016/j.jns.2019.116514

Cited by 24 publications

(30 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…data to improve the differentiation between GBM and brain metastases [74][75][76][77][78]. Novel diagnostic support systems based on radiomic features extracted from post-contrast 3 diffusion tensor imaging (DT1) MR images may help improving the distinction between solitary brain metastases and GBM with high diagnosis performance and generalizability [74].…”

Section: Single-voxel Proton Mr Spectroscopymentioning

confidence: 99%

“…Novel diagnostic support systems based on radiomic features extracted from post-contrast 3 diffusion tensor imaging (DT1) MR images may help improving the distinction between solitary brain metastases and GBM with high diagnosis performance and generalizability [74]. Machine learning and deep learning-based models applied to conventional MR images may support pre-operative discrimination between GBM and solitary brain metastasis [75][76][77], and deep learning network models that allow automated, on-site analysis of resected tumor specimens based on confocal laser endoscopic techniques image data sets have been developed [78]. Other parameters, such as the cerebral blood volume gradient in the peritumoral brain zone, may enable the differentiation of GBMs from metastases [79].…”

Section: Single-voxel Proton Mr Spectroscopymentioning

confidence: 99%

See 1 more Smart Citation

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Roesler

Dini²,

Isolan³

2021

Clinical and Experimental Immunology

View full text Add to dashboard Cite

Brain tumors and brain metastases induce changes in brain tissue remodeling that lead to immunosuppression and trigger an inflammatory response within the tumor microenvironment. These immune and inflammatory changes can influence invasion and metastasis. Other neuroinflammatory and necrotic lesions may occur in patients with brain cancer or brain metastases as sequelae from treatment with radiotherapy. Glioblastoma (GBM) is the most aggressive primary malignant brain cancer in adults. Imaging methods such as positron emission tomography (PET) and different magnetic resonance imaging (MRI) techniques are highly valuable for the diagnosis and therapeutic evaluation of GBM and other malignant brain tumors. However, differentiating between tumor tissue and inflamed brain tissue with imaging protocols remains a challenge. Here, we review recent advances in imaging methods that have helped to improve the specificity of primary tumor diagnosis versus evaluation of inflamed and necrotic brain lesions. We also comment on advances in differentiating metastasis from neuroinflammation processes. Recent advances include the radiosynthesis of 18 F-FIMP, an L-type amino acid transporter 1 (LAT1)specific PET probe that allows clearer differentiation between tumor tissue and inflammation compared to previous probes, and the combination of different advanced imaging protocols with the inclusion of radiomics and machine learning algorithms.

show abstract

Section: Single-voxel Proton Mr Spectroscopymentioning

confidence: 99%

Section: Single-voxel Proton Mr Spectroscopymentioning

confidence: 99%

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Roesler

Dini²,

Isolan³

2021

Clinical and Experimental Immunology

View full text Add to dashboard Cite

show abstract

“…Computational-aided quantitative analysis of MRI images may improve the accuracy in differentiating GBM from metastases, and texture features are more significant than fractal-based features for that purpose [76]. Increasingly, machine learning algorithms have been applied to imaging data to improve the differentiation between GBM and brain metastases [77][78][79][80][81]. Novel diagnostic support systems based on radiomic features extracted from post-contrast 3DT1 MR images may help improving the distinction between solitary brain metastases and GBM with high diagnosis performance and generalizability [77].…”

Section: Brain Metastases Inflammation and Imagingmentioning

confidence: 99%

“…Novel diagnostic support systems based on radiomic features extracted from post-contrast 3DT1 MR images may help improving the distinction between solitary brain metastases and GBM with high diagnosis performance and generalizability [77]. Machine learning and deep learning-based models applied to conventional MR images may support preoperative discrimination between GBM and solitary brain metastasis conventional MR images [78][79][80], and deep learning network models that allow automated, on-site analysis of resected tumor specimens based on confocal laser endoscopic techniques image datasets have been developed [81]. Other parameters such as the cerebral blood volume gradient in the peritumoral brain zone may enable the differentiation of GBMs from metastases [82].…”

Section: Brain Metastases Inflammation and Imagingmentioning

confidence: 99%

Brain tumours, brain metastases, and neuroinflammation: Insights from neuroimaging studies.

Roesler

Dini²,

Isolan³

2021

Preprint

View full text Add to dashboard Cite

Brain tumors and brain metastases induce changes in brain tissue remodelling that lead to immunosuppression and trigger an inflammatory response within the tumor microenvironment. These immune and inflammatory changes can influence invasion and metastasis. Other neuroinflammatory and necrotic lesions may occur in patients with brain cancer or brain metastases as sequelae from treatment with radiotherapy. Glioblastoma (GBM) is the most aggressive primary malignant brain cancer in adults. Imaging methods such as positron-emission tomography (PET) and different magnetic resonance imaging (MRI) techniques are highly valuable for the diagnosis and therapeutic evaluation of GBM and other malignant brain tumors. However, differentiating between tumor tissue and inflamed brain tissue with imaging protocols remains a challenge. Here, we review recent advances in imaging methods that have helped to improve the specificity of primary tumor diagnosis versus evaluation of inflamed and necrotic brain lesions. We also comment on advances in differentiating metastasis from neuroinflammation processes. Recent advances include the radiosynthesis of 18F-FIMP, an L-type amino acid transporter 1 (LAT1)-specific PET probe that allows better differentiation between tumor tissue and inflammation compared to previous probes; and the combination different advanced imaging protocols with the inclusion of radiomics and machine learning algorithms.

show abstract

“…The advent of radiomics in recent years has tremendously permeated clinical applications in terms of diagnosis, prognosis, as well as prediction of treatment response in tumors [ 13 , 14 ]. In particular, many studies have employed radiomics for the differentiation of GBM from SBM based on conventional MR sequences or/and more advanced MR technologies such as DWI, DTI, APT [ 15 , 16 , 17 , 18 , 19 , 20 ]. These investigations have shed light on mining more concealed image textures barely captured by the naked eyes in distinguishing GBM from SBM.…”

Section: Introductionmentioning

confidence: 99%

A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis

Liang

Wei

et al. 2021

Cancers

View full text Add to dashboard Cite

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).

show abstract

An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases

Cited by 24 publications

References 8 publications

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Brain tumours, brain metastases, and neuroinflammation: Insights from neuroimaging studies.

A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis

Contact Info

Product

Resources

About