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Cerebral Microbleeds May Be Less Detectable by Susceptibility Weighted Imaging MRI From 24 to 72 Hours After Traumatic Brain Injury

ABSTRACT: Purpose: A former rodent study showed that cerebral traumatic microbleeds (TMBs) may temporarily become invisible shortly after injury when detected by susceptibility weighted imaging (SWI). The present study aims to validate this phenomenon in human SWI. Methods: In this retrospective study, 46 traumatic brain injury (TBI) patients in various forms of severity were included and willingly complied with our strict selection criteria. Clinical parameters potentially affecting TMB count, Rotterdam and Marshall CT score, Mayo Clinic Classification, contusion number, and total volume were registered. The precise time between trauma and MRI [5 h 19 min to 141 h 54 min, including SWI and fluid-attenuated inversion recovery (FLAIR)] was individually recorded; TMB and FLAIR lesion counts were assessed. Four groups were created based on elapsed time between the trauma and MRI: 0–24, 24–48, 48–72, and >72 h. Kruskal–Wallis, ANOVA, Chi-square, and Fisher’s exact tests were used to reveal differences among the groups within clinical and imaging parameters; statistical power was calculated retrospectively for each comparison. Results: The Kruskal–Wallis ANOVA with Conover post hoc analysis showed significant (p = 0.01; 1−β > 0.9) median TMB number differences in the subacute period: 0–24 h = 4.00 (n = 11); 24–48 h = 1 (n = 14); 48–72 h = 1 (n = 11); and 72 h ≤ 7.5 (n = 10). Neither clinical parameters nor FLAIR lesions depicted significant differences among the groups. Conclusion: Our results demonstrate that TMBs on SWI MRI may temporarily become less detectable at 24–72 h following TBI.

SUBMITTER: Kornyei B

PROVIDER: S-EPMC8514822 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score.ResultsA total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895-0.921), accuracy of 0.819 (95% CI 0.768-0.870), weighted-average precision of 0.864 (95% CI 0.831-0.897), and weighted-average F1-score of 0.829 (95% CI 0.782-0.876) in the external set, outperforming the performance of the neurologist group.ConclusionThe DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.

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Cerebral Microbleeds May Be Less Detectable by Susceptibility Weighted Imaging MRI From 24 to 72 Hours After Traumatic Brain Injury

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