Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low‐Grade Gliomas Using Multiparametric MR Radiomic Features

Ren, Yan; Zhang, Xi; Rui, Wenting; Pang, Haopeng; Qiu, Tianming; Wang, Jing; Xie, Qian; Jin, Teng; Zhang, Hua; Chen, Hong; Zhang, Yong; Lu, Hongbing; Yao, Zhenwei; Jun-hai, Zhao; Feng, Xiaoyuan

doi:10.1002/jmri.26240

Cited by 72 publications

(55 citation statements)

References 37 publications

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“…Our findings confirm the results of former investigations highlighting the impact of texture analysis on disease characterization . As a strength of our study, we assessed changes in texture features over a period of time .…”

Section: Discussionsupporting

confidence: 91%

Can texture analysis in ultrashort echo‐time MRI distinguish primary graft dysfunction from acute rejection in lung transplants? A multidimensional assessment in a mouse model

Euler

Blüthgen

Wurnig

et al. 2019

Magnetic Resonance Imaging

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Background: Differentiation of early postoperative complications affects treatment options after lung transplantation. Purpose: To assess if texture analysis in ultrashort echo-time (UTE) MRI allows distinction of primary graft dysfunction (PGD) from acute transplant rejection (ATR) in a mouse lung transplant model. Study Type: Longitudinal. Animal Model: Single left lung transplantation was performed in two cohorts of six mice (strain C57BL/6) receiving six syngeneic (strain C57BL/6) and six allogeneic lung transplants (strain BALB/c (H-2K d )). Field Strength/Sequence: 4.7T small-animal MRI/eight different UTE sequences (echo times: 50-5000 μs) at three different postoperative timepoints (1, 3, and 7 days after transplantation). Assessment: Nineteen different firstand higher-order texture features were computed on multiple axial slices for each combination of UTE and timepoint (24 setups) in each mouse. Texture features were compared for transplanted (graft) and contralateral native lungs between and within syngeneic and allogeneic cohorts. Histopathology served as a reference. Statistical Tests: Nonparametric tests and correlation matrix analysis were used. Results: Pathology revealed PGD in the syngeneic and ATR in the allogeneic cohort. Skewness and low-gray-level runlength features were significantly different between PGD and ATR for all investigated setups (P < 0.03). These features were significantly different between graft and native lung in ATR for most setups (minimum of 20/24 setups; all P < 0.05). The number of significantly different features between PGD and ATR increased with elapsing postoperative time. Differences in significant features were highest for an echo-time of 1500 μs. Data Conclusion: Our findings suggest that texture analysis in UTE-MRI might be a tool for the differentiation of PGD and ATR in the early postoperative phase after lung transplantation. Level of Evidence: 1 Technical Efficacy: Stage 3

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Section: Discussionsupporting

confidence: 91%

Can texture analysis in ultrashort echo‐time MRI distinguish primary graft dysfunction from acute rejection in lung transplants? A multidimensional assessment in a mouse model

Euler

Blüthgen

Wurnig

et al. 2019

Magnetic Resonance Imaging

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“…Furthermore, we were also able to show which image features were important in the classification, increasing the clinical understand-ing of the machine learning algorithm and potentially aiding better acceptance, as well as furthering fundamental research into understanding of glioma pathophysiology. Although other studies did already investigate the noninvasive prediction of the molecular subtype of LGG, these often focused on IDH mutations only and did not consider the 1p/19q codeletion status (11,28,29). In comparison with studies that did look at the 1p/19q codeletion, we used a larger cohort and an external validation dataset (10,13,14,30,31), which makes our results more robust and generalizable, respectively.…”

Section: Discussionmentioning

confidence: 99%

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm

Voort

Incekara

Wijnenga

et al. 2019

Clinical Cancer Research

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Purpose: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI. Experimental Design: Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images. Results: The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83). Conclusions: Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable.

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“…To break through the limitations of the conventional analysis methods, this study proposed a combined analysis of ROI‐based features at all cervical levels. Such ROI‐based features can provide more details of the inside of the spinal cord, and have been demonstrated to be more effective than whole‐cord‐based features for describing the status of the spinal cord . In this study, the ROI drawing was performed by experienced neuroimage researchers with a well‐established ROI drawing method; however, a question may still be raised concerning the accuracy of the ROI drawing in the seriously compressed cervical spinal cord.…”

Section: Discussionmentioning

confidence: 99%

“…Besides whole‐cord‐based features, DTI features can be extracted from the DTI metric maps via the drawing of a region of interest (ROI) inside the spinal cord. These ROI‐based features can provide detailed information on the tissues inside the spinal cord, and their feasibility and effectiveness for describing the spinal cord has previously been shown in several studies . In addition, it has been demonstrated that cervical levels other than the MCCL also contain information that is useful for the prognostic process, and thus DTI features at these cervical levels may also be of benefit for the prognosis.…”

Section: Introductionmentioning

confidence: 93%

“…The number of extractable ROI‐based features is quite large, and it would be complicated and time consuming to perform a manual analysis. Therefore, artificial intelligence (AI) methods, which have been demonstrated to be suitable for such kinds of analysis, should be a good choice for a combined analysis of the ROI‐based features at all cervical levels. Conventional AI models using logistic regression (LR), k ‐nearest neighbor (KNN), and radial basis function kernel support vector machine (RBF‐SVM) algorithms were evaluated for analyzing these DTI features.…”

Section: Introductionmentioning

confidence: 99%

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Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

Jin

Luk

2019

NMR in Biomedicine

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Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF-SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM. KEYWORDS artificial intelligence (AI), cervical myelopathy (CM), diffusion tensor imaging (DTI), prognosis 1 | INTRODUCTION Cervical myelopathy (CM) is a common spinal cord dysfunction that affects millions of people worldwide. 1 Currently, surgical intervention is considered to be the most immediate way to provide relief from the spinal cord compression and to promote neurological recovery when coupled with active post-operative neural rehabilitation. 2,3 An accurate prognosis for the surgery would provide useful assistance to surgeons, helping them to decide on the most appropriate treatment option, manage patient expectations, and plan postoperative rehabilitation. 4 Several prognosticators of surgical outcomes have already been proposed. 5,6 However, the value of these prognosticators remains controversial, and researchers continue to search for more effective prognostic methods.Diffusion tensor imaging (DTI) has been demonstrated to have prognostic value for CM. [7][8][9] In CM, demyelination is believed to be the main pathological change in the spinal cord, and this demyelination changes the organization of nerve fiber bundles, resulting in changes to DTI-related signals. Based on this principle, researchers have used DTI to study CM. Also, it has been reported that DTI has great potential for distinguishing CM patients from healthy subjects and for predicting the exact cervical levels causing symptoms. [10][11][12] The pathological status of the spinal cord,

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Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low‐Grade Gliomas Using Multiparametric MR Radiomic Features

Abstract: 3 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2018.

Cited by 72 publications

References 37 publications

Can texture analysis in ultrashort echo‐time MRI distinguish primary graft dysfunction from acute rejection in lung transplants? A multidimensional assessment in a mouse model

Can texture analysis in ultrashort echo‐time MRI distinguish primary graft dysfunction from acute rejection in lung transplants? A multidimensional assessment in a mouse model

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

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