Relative cerebral blood volume is a potential biomarker in late delayed radiation‐induced brain injury

Xie, Ying; Huang, Hongbo; Guo, Junjie; Zhou, Dongxiao

doi:10.1002/jmri.25837

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…The blood supply of the WM is relatively scarce. After exposure, the blood vessels are damaged, and ischaemic necrosis easily occurs [10]. Therefore, most of the studies related to RBI are focused on WM, for which decreased fractional anisotropy (FA) values indicate loss of WM integrity.…”

Section: Introductionmentioning

confidence: 99%

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

et al. 2019

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BackgroundThe purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic.MethodsA DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject’s DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier.ResultsFor FA, the accuracy of classification between the 0–6, 6–12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0–6, 6–12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced.ConclusionsRBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.

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Section: Introductionmentioning

confidence: 99%

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

et al. 2019

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“…BBB disruption and hypoperfusion (fig. S1, A and B) are considered to be the major risk factors that contribute to the initiation and development of RIBI ( 29 , 36 , 37 ). Hence, we analyzed eight patients who agreed to undergo dynamic contrast-enhanced (DCE) and nine patients who agreed to undergo perfusion-weighted imaging (PWI)–MRI both before and after thalidomide treatment (table S1).…”

Section: Resultsmentioning

confidence: 99%

A phase 2 study of thalidomide for the treatment of radiation-induced blood-brain barrier injury

Cheng

Jiang

et al. 2023

Sci. Transl. Med.

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Radiation-induced brain injury (RIBI) is a debilitating sequela after radiotherapy to treat head and neck cancer, and 20 to 30% of patients with RIBI fail to respond to or have contraindications to the first-line treatments of bevacizumab and corticosteroids. Here, we reported a Simon’s minmax two-stage, single-arm, phase 2 clinical trial (NCT03208413) to assess the efficacy of thalidomide in patients with RIBI who were unresponsive to or had contraindications to bevacizumab and corticosteroid therapies. The trial met its primary endpoint, with 27 of 58 patients enrolled showing ≥25% reduction in the volume of cerebral edema on fluid-attenuated inversion recovery–magnetic resonance imaging (FLAIR-MRI) after treatment (overall response rate, 46.6%; 95% CI, 33.3 to 60.1%). Twenty-five (43.1%) patients demonstrated a clinical improvement based on the Late Effects Normal Tissues–Subjective, Objective, Management, Analytic (LENT/SOMA) scale, and 36 (62.1%) experienced cognitive improvement based on the Montreal Cognitive Assessment (MoCA) scores. In a mouse model of RIBI, thalidomide restored the blood-brain barrier and cerebral perfusion, which were attributed to the functional rescue of pericytes secondary to elevation of platelet-derived growth factor receptor β (PDGFRβ) expression by thalidomide. Our data thus demonstrate the therapeutic potential of thalidomide for the treatment of radiation-induced cerebral vasculature impairment.

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“…Although there was no visible abnormality in the MR images, some cells may have already been damaged by radiotherapy. The higher the cell density of the TL is, the more abundant the blood supply and higher the oxygen demand of the corresponding temporal lobe are, 32,33 the more likely the radiationdamaged TL tissue is to develop RTLI. In total, our findings reflected the selected radiomics features based on MR images may reflect cellular and histological characteristics that are difficult to be perceived visually.…”

Section: Discussionmentioning

confidence: 99%

Magnetic resonance imaging‐based radiomics model for predicting radiation‐induced temporal lobe injury in nasopharyngeal carcinoma after intensity‐modulated radiotherapy

et al. 2022

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Background To develop a model based on magnetic resonance imaging (MRI) radiomics and clinical features for predicting radiation‐induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity‐modulated radiotherapy (IMRT). Methods Two hundred and sixteen patients with NPC were retrospectively included. Radiomics features were extracted and selected. The logistic regression analysis was performed for prediction models construction. The area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. Results Three radiomics features were selected to construct the radiomics signature (AUC of 0.94 and 0.92). The clinical‐radiomics model, integrating radiomics signature with T classification, achieved higher predictive performance in the training and validation cohorts (AUC of 0.95 and 0.93), as well as improved accuracy of the classification of RTLI outcomes (net reclassification improvement: 0.711; 95% CI: 0.57–0.86; p < 0.001). Conclusions The clinical‐radiomics model and radiomics signature both showed great performance in predicting RTLI in patients with NPC.

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Relative cerebral blood volume is a potential biomarker in late delayed radiation‐induced brain injury

Abstract: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1112-1118.

Cited by 10 publications

References 32 publications

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

A phase 2 study of thalidomide for the treatment of radiation-induced blood-brain barrier injury

Magnetic resonance imaging‐based radiomics model for predicting radiation‐induced temporal lobe injury in nasopharyngeal carcinoma after intensity‐modulated radiotherapy

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