Neuroradiology最新文献

Background: Early neurological deterioration (END) affects 20-30% of patients with lacunar stroke within 48 h despite optimal treatment. Previously established markers included infection and infarct location on imaging. We studied the utility of measuring global cerebral blood flow (gCBF) measured by CT-Perfusion (CTP) as an early predictor of END in patients with lacunar strokes.

Methods: 162 patients with lacunar stroke were measured for gCBF including both cerebral hemispheres and cerebellum. We stratified patients by normal gCBF (> 40 ml/100 mg/min) vs. low gCBF (< 40 ml/100 mg/min). Stroke location, vascular risk factors, age and gender were assessed. The primary outcome was the change in the NIHSS score after 48 h from index stroke.

Results: Mean gCBF of the overall cohort was 37.72 ml/100 mg/min. Both groups had a baseline NIHSS score of 4.2 with similar standard deviations. The NIHSS score decreased by 1.3 points in normal gCBF group and increased by 1.1 points in the low gCBF group. All stroke sites deteriorated in the low gCBF group, particularly the capsula interna, corona radiata, and lateral pontine area. END occurred in 37.8% in low gCBF compared to 3.1% in the normal gCBF patients. In contrast, clinical improvement after 48 h occurred in 64.2% of patients with normal gCBF but only 6.1% with low gCBF.

Conclusion: Our study supports measurement of gCBF by CTP as a potential imaging biomarker for END. Additionally, it adds evidence to the body of supporting the vulnerability of capsula interna and pontine infarctions to END.

Purpose: Some patients with spinal muscular atrophy and scoliosis require CT guidance during injections of nusinersen. The radiation applied to the operator in such procedures becomes an important issue in terms of staff health and safety. The aim of the study was to assess the operator's radiation exposure during CT-guided nusinersen injections in patients with spinal muscular atrophy and scoliosis.

Methods: Consecutive 40 CT-guided nusinersen injections were analyzed in terms of operator's radiation exposure measured in real time.

Results: The median radiation dose measured under the physician's lead apron and patient dose in terms of DLP was 0.20 µSv and 31.90 mGy*cm respectively. The radiation doses were significantly higher (p = 0.047) in patients with spinal instrumentation.

Conclusion: The results show that CT-guided nusinersen injection is a relatively safe procedure in terms of operator's radiation exposure. This can allow for interventional radiologists to perform more procedures without exceeding their annual dose limit.

Introduction: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-efficiently by radiologists with assistance of artificial intelligence (AI).

Methods: Baseline and three follow-up (FU) MRIs of thirty-five consecutive patients diagnosed with MS were assessed by a radiologist manually, and with assistance of an AI-tool. Results were discussed with a consultant neuroradiologist and time metrics were evaluated.

Results: The mean reading time for the resident radiologist was 9.05 min (95CI: 6.85-11:25). With AI-assistance, the reading time was reduced by 2.83 min (95CI: 3.28-2.41, p < 0.001). The reading decreased steadily from baseline to FU3 for the resident radiologist (9.85 min baseline, 9.21 FU1, 8.64 FU2 and 8.44 FU3, p < 0.001). Assistance of AI further remarkably decreased reading times during follow-ups (3.29 min FU1, 3.92 FU2, 3.79 FU3, p < 0.001) but not at baseline (0.26 min, p = 0.96). The baseline reading time of the resident radiologist was 5.04 min (p < 0.001), with each lesion adding 0.14 min (p < 0.001). There was a substantial decrease in the baseline reading time from 5.04 min to 1.59 min (p = 0.23) with AI-assistance. Discussion of the reading results of the resident with the neuroradiology consultant (as usual in clinical routine) was exemplary done for FU-3 MRIs and added another 3 min (CI:2.27-3.76) to the reading time without AI-assistance.

Conclusion: We found that AI-assisted reading of MRIs of patients with MS may be faster than evaluating these MRIs without AI-assistance.

Purpose: This study aimed to investigate the efficacy of fine-tuned large language models (LLM) in classifying brain MRI reports into pretreatment, posttreatment, and nontumor cases.

Methods: This retrospective study included 759, 284, and 164 brain MRI reports for training, validation, and test dataset. Radiologists stratified the reports into three groups: nontumor (group 1), posttreatment tumor (group 2), and pretreatment tumor (group 3) cases. A pretrained Bidirectional Encoder Representations from Transformers Japanese model was fine-tuned using the training dataset and evaluated on the validation dataset. The model which demonstrated the highest accuracy on the validation dataset was selected as the final model. Two additional radiologists were involved in classifying reports in the test datasets for the three groups. The model's performance on test dataset was compared to that of two radiologists.

Results: The fine-tuned LLM attained an overall accuracy of 0.970 (95% CI: 0.930-0.990). The model's sensitivity for group 1/2/3 was 1.000/0.864/0.978. The model's specificity for group1/2/3 was 0.991/0.993/0.958. No statistically significant differences were found in terms of accuracy, sensitivity, and specificity between the LLM and human readers (p ≥ 0.371). The LLM completed the classification task approximately 20-26-fold faster than the radiologists. The area under the receiver operating characteristic curve for discriminating groups 2 and 3 from group 1 was 0.994 (95% CI: 0.982-1.000) and for discriminating group 3 from groups 1 and 2 was 0.992 (95% CI: 0.982-1.000).

Conclusion: Fine-tuned LLM demonstrated a comparable performance with radiologists in classifying brain MRI reports, while requiring substantially less time.

Purpose: This study aimed to clarify whether there is a relationship between vessel deviation during stent retrieval and successful recanalization in stent-based mechanical thrombectomy (MT) for M2 occlusion.

Methods: The video of the MT was reviewed for each of the 25 included patients with M2 occlusion. The vertical distance of vessel deviation at the time of stent retrieval was defined as D, and the diameter of the balloon guide catheter shown on the same screen was defined as B. The D/B ratio was calculated as an index of the vessel deviation. The presence or absence of successful recanalization (thrombolysis in cerebral infarction (TICI) score of 2b/3) was compared based on the D/B ratio and clinical factors.

Results: Of the 25 patients, successful recanalization was achieved in 18 (72%). The median D/B ratio with successful recanalization was 0.9, which was significantly lower than that without successful recanalization (2.5, p < 0.001, Mann-Whitney U test). Combined aspiration catheters were used in 24 cases. In nine (36%) cases, the tip of the aspiration catheter was in M2 during stent retrieval. The median D/B ratio with the position of the aspiration catheter tip in M1 or the internal carotid artery was 1.5, which was significantly higher than that with the position in M2 (0, p = 0.003, Mann-Whitney U test).

Conclusion: In stent-based MT for M2 occlusion, cases in which successful recanalization was achieved showed less vessel deviation during stent retrieval. To reduce vessel deviation, advancing the combined aspiration catheter up to M2 is useful.

Purpose: Although previous studies have reported that hemoglobin levels can affect the brain, very few have focused on the association between hemoglobin levels and brain volume. We aimed to identify the influence of hemoglobin levels on brain volume measured using magnetic resonance imaging (MRI) in a large elderly population.

Methods: This cross-sectional study included 2153 participants (median age, 69 years; 60.2% female) who underwent 3T brain MRI. Multiple regression analyses were performed after adjusting for potential confounders. In the subgroup analyses, participants were divided into four groups according to sex and age threshold (lower age group [65-74] and higher age group [≥ 75]).

Results: After adjusting for potential confounders, total white matter volume reduction was found to be associated with decreased hemoglobin levels (females: standardized β = 0.059, [95% confidence interval (CI): 0.0032, 0.11], P = 0.038, males: standardized β = 0.069 [95% CI: -0.00023, 0.14], P = 0.051). This relationship was notable in younger age groups of both sexes. After adjustment, the total gray matter and hippocampal volumes were not significantly associated with hemoglobin levels.

Conclusion: Low hemoglobin levels may have deleterious effects on white matter volume, which diminishes with age.

Introduction: Central tegmental tract hyperintensity (CTTH) on T2-weighted imaging is an uncommon neuroimaging finding in pediatric patients with unclear clinical significance. CTTH may represent either a physiological or pathological process. This study evaluates the relationship between CTTH and MRI sequences (FLAIR, DWI) to explore its diagnostic value.

Methods: We retrospectively analyzed 3462 pediatric brain MRI scans conducted between July 2011 and January 2022, identifying 104 patients with bilateral CTTH. DWI, FLAIR sequences, and follow-up scans were visually assessed for T2/FLAIR mismatch and diffusion restriction. Clinical data were obtained from electronic patient records. Statistical analysis was performed using SPSS, with significance set at p < .05.

Results: A total of 104 pediatric patients with CTTH were included, ranging from 1 month to 16 years old (mean age: 31.34 months). Epilepsy, metabolic diseases, and cerebral palsy were the most common clinical diagnoses. Diffusion restriction was observed in 40.8% of patients, while 39.6% had FLAIR hyperintensity. T2/FLAIR mismatch, defined for the first time in CTTH, was found in 60.4% of patients. A significant correlation was found between T2/FLAIR mismatch and clinical diagnoses (p = .020), as well as between diffusion restriction and T2/FLAIR mismatch (p = .017).

Conclusion: CTTH in pediatric patients may arise from two distinct processes: a transient, developmental phenomenon or a pathological process marked by irreversible myelin degeneration. T2/FLAIR mismatch and diffusion restriction provide valuable diagnostic markers, offering insights into the severity and chronicity of CTTH. Further studies are needed to validate these findings and their clinical implications.

Purpose: This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis.

Methods: Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates.

Results: The study included 769 participants of 464 men and 305 women (mean age, 63 years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses.

Conclusion: This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.

Introduction: Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosing LNM. This meta-analysis aims to estimate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting LNM in head and neck cancers.

Methods: A systematic search was performed on four databases, looking for studies reporting the diagnostic accuracy of AI models in detecting LNM in head and neck cancers. Methodological quality was assessed using the METRICS tool and meta-analysis was performed using bivariate model in R environment.

Results: 23 articles met the inclusion criteria. Due to the absence of external validation in most studies, all analyses were confined to internal validation sets. The meta-analysis revealed a pooled AUC of 91% for CT-based radiomics, 84% for MRI-based radiomics, and 92% for PET/CT-based radiomics. Sensitivity and specificity were highest for PET/CT-based models. The pooled AUC was 92% for deep learning models and 91% for hand-crafted radiomics models. Models based on lymph node features had a pooled AUC of 92%, while those based on primary tumor features had an AUC of 89%. No significant differences were found between deep learning and hand-crafted radiomics models or between lymph node and primary tumor feature-based models.

Conclusion: Radiomics and deep learning models exhibit promising accuracy in diagnosing LNM in head and neck cancers, particularly with PET/CT. Future research should prioritize multicenter studies with external validation to confirm these results and enhance clinical applicability.

Purpose: The clinico-radiological dilemma in post-treatment high-grade gliomas, between disease recurrence (TR) and treatment-related changes (TRC), still persists. FET (Fluoro-ethyl-tyrosine) PET has been extensively used as problem-solving modality for cases where MR imaging is inconclusive. We incorporated a systematic imaging and clinical follow-up algorithm in a multi-disciplinary clinic (MDC) setting to analyse our cohort of FET PET in post-treatment gliomas.

Methods: We retrospectively analyzed 171 patients of post-treatment grade III and IV glioma with equivocal findings on MRI. 185-222 MBq of 18 F-FET was injected and dedicated static imaging of brain was performed at 20 min. TBR (Tumor to background ratio) was used as semi-quantitative parameter. Cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed with histopathological diagnosis, wherever available or in a multidisciplinary joint clinic based on serial imaging.

Results: 121 of 171 patients showed recurrent disease on FET PET, on follow up, 109 were confirmed with recurrence; 7 patients showed TRC, whereas 5 were treated with bevacizumab, with no further clinico-radiological deterioration, thus confirming TRC. 50 patients showed TRC on FET PET, on follow up on follow up, 40 were confirmed as true-negative. 10 patients who showed TBR less than 2.5 had confirmed TR on subsequent MR imaging. The overall sensitivity and specificity was 91.6 and 76.9% respectively, with a diagnostic accuracy of 87.13%.

Conclusion: There is potential for FET PET to be used along with MRI in the post treatment algorithm of high-grade glial tumors.