The latest medical research on Interventional Radiology
The research magnet gathers the latest research from around the web, based on your specialty area. Below you will find a sample of some of the most recent articles from reputable medical journals about interventional radiology gathered by our medical AI research bot.
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Request AccessThe eagle-wing finding in FP-CIT SPECT, as a characteristic finding in patients with DESH- type iNPH.
NeuroradiologyAlthough dopamine transporter (DAT) imaging has been reported to be useful for differentiating idiopathic Normal Pressure Hydrocephalus (iNPH) from its mimics, the radiological findings of DAT imaging in iNPH have not been established. We investigated [123I] N-ω-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane (FP-CIT) single photon emission computed tomography (SPECT) images from patients with disproportionately enlarged subarachnoid-space hydrocephalus (DESH)-type iNPH to understand the characteristics of DAT images of iNPH.
We retrospectively collected 11 DESH-type iNPH patients without comorbidities who underwent FP-CIT SPECT imaging. The patients' FP-CIT SPECT were examined using both visual and quantitative evaluations. Visual assessment used Kahraman et al.'s five-step grading, and quantitative assessment used DaTView and MIM software to calculate specific binding ratios (SBRs) for four volumes of interest (VOIs): the entire striatum, caudate nucleus, anterior putamen, and posterior putamen. Intergroup comparisons were made between the DESH group and a normal control (NC) group adjusted for age and sex.
The visual assessment classified 91% of DESH patients as showing grade 4 'eagle-wing' on FP-CIT SPECT, with a Kappa coefficient of 0.601. The median SBR was lower in the DESH group than in the NC group for all four VOIs, and significantly lower in the anterior and posterior putamen (p < 0.05).
In DESH-type iNPH, FP-CIT SPECT imaging typically shows the 'eagle-wing' finding due to decreased DAT concentration in the putamen. Our results enhance the utility of FP-CIT SPECT in diagnosing iNPH and distinguishing it from mimics.
Fetal torcular pseudomass and development of the dural venous sinuses: insights from 2D TOF MR angiography.
NeuroradiologyThe described evolution in prenatal and postnatal periods appears to support the hypothesis that the torcular pseudomass (TP) is probably a physiological, highly frequent and transient developmental finding. Neverthless, it remains to be determined whether TP has any relation with the final anatomy of the adjacent venous sinuses or any anatomic variants. We aimed to explore the relation of the TP with the adjacent dural venous anatomy/anatomic variants in the prenatal period, using MR angiography (2D TOF MRA).
We conducted a single centre retrospective study (September 2018-April 2024), by selecting all the fetal brain MRIs with MRA acquisition (2D TOF). Two neuroradiologists independently reviewed all MRIs, assessing the TP anatomy and adjacent venous sinuses.
Forty-five brain MRIs were obtained, from pregnant women with median maternal age of 31 years, and a median gestational age of 31 weeks. At least one anatomic variant of the venous drainage system was present in 48.9% of cases (n = 22), mainly a venous drainage dominance (40%). The TP was present in 75.6% of cases; it was focal and bulky in 10 cases each, and crescentic in the remainder; 70.6% were median/symmetric and 29.4% were left paramedian. We found a significant association of TP position and TP category; all of those with lateralization were left-sided, with a large proportion (80%) being bulky. The TP position was significantly associated with a pattern of right venous drainage dominance.
We have provided an assessment of the relationship of the TP with the surrounding venous anatomy, particularly regarding its correlation with anatomic variants.
Visualizing the association between the location and prognosis of isocitrate dehydrogenase wild-type glioblastoma: a voxel-wise Cox regression analysis with open-source datasets.
NeuroradiologyThis 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.
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.
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.
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.
Interhypothalamic adhesions: prevalence, structure, and location-based classification map in pediatric patients undergoing MRI.
NeuroradiologyInterhypothalamic adhesions (IHAs) have been reported only in the pediatric population, with unknown prevalence and histological composition. We aim to demonstrate their prevalence, assess their persistence through longitudinal imaging, classify IHAs by anatomical distribution, explore their structure, and report associated pathologies.
A retrospective review was conducted on consecutive pediatric brain MRI studies obtained between January 2012, and December 2013. The presence of an IHA was only confirmed when observed on at least two planes. For each IHA, cross-sectional area was calculated, and signal intensities were measured at the center on sagittal T2WIs. Signal intensities were also measured in both cerebral white matter and gray matter for normalization and comparison. Patient demographics and clinical information were collected from electronic charts.
Out of 1550 patients (0-17.9 years), 33 (19 males, 14 females) had an IHA, resulting in a 2.13% prevalence. Follow-up images were available for 19 IHA-positive patients, and IHAs were again seen in 92% of the follow-up scans (71/77). Normalized IHA signal highly correlated with normalized gray matter signal (r = 0.83, P < 0.001), but not with normalized white matter signal (r = -0.16, p = 0.494). Common co-occurring pathologies included hydrocephalus (n = 9), prematurity (n = 8), and corpus callosum abnormalities (n = 7). All type 3 IHAs (3/3) were accompanied by pituitary pathologies.
IHAs have a prevalence of 2.13% in our cohort, and the majority persist in longitudinal studies. They showed gray matter signal intensity and Type 3 IHAs exclusively accompanied pituitary abnormalities.
Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.
NeuroradiologyAssessment 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).
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.
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.
We found that AI-assisted reading of MRIs of patients with MS may be faster than evaluating these MRIs without AI-assistance.
Real-time measurement of radiation exposure in interventional radiologists during CT-guided intrathecal injections of nusinersen.
NeuroradiologySome 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.
Consecutive 40 CT-guided nusinersen injections were analyzed in terms of operator's radiation exposure measured in real time.
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.
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.
FET PET to differentiate between post-treatment changes and recurrence in high-grade gliomas: a single center multidisciplinary clinic controlled study.
NeuroradiologyThe 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.
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.
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%.
There is potential for FET PET to be used along with MRI in the post treatment algorithm of high-grade glial tumors.
Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.
NeuroradiologyHead 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.
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.
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.
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.
Medial temporal atrophy predicts the limbic comorbidities in lewy body disease.
NeuroradiologyAlthough neuropathological comorbidities, including Alzheimer's disease neuropathological change (AD-NC) and limbic-predominant age-related TAR DNA-binding protein 43encephalopathy neuropathological change (LATE-NC), are associated with medial temporal atrophy in patients with Lewy body disease (LBD), the diagnostic performance of magnetic resonance imaging (MRI)-derived indices remains unclear. This study aimed to investigate the diagnostic performance of MRI-derived indices representing medial temporal atrophy in differentiating between LBD with AD-NC and/or LATE-NC (mixed LBD [mLBD]) and without these comorbidities (pure LBD [pLBD]).
This study included 24 and 16 patients with pathologically confirmed mLBD and pLBD, respectively. In addition to the well-known medial temporal atrophy and entorhinal cortex atrophy (ERICA) scores, the cross-sectional areas of the bilateral entorhinal cortices/parahippocampal gyri (ABEP) were segmented manually.
Even incorporating various covariates such as age at MRI examination, sex, argyrophilic grain, the MRI-derived indices, especially ABEP, significantly correlated with the severity of AD-NC, and showed a trend of correlation with LATE-NC. For the differentiation between all mLBD and pLBD, the ERICA score and ABEP demonstrated higher diagnostic performance (area under the receiver-operating-characteristic curve [AUC] of 0.80 and 0.87, respectively). Additionally, the highest diagnostic performance for ABEP (AUC, 0.94; sensitivity, 100%; specificity, 88.9%; accuracy, 96%) was observed in differentiating between pLBD and mLBD with two comorbidities (AD-NC and LATE-NC).
In patients with pathologically confirmed LBD, medial temporal atrophy was significantly correlated with AD-NC, and showed a trend of correlation with LATE-NC. Moreover, MRI-derived indices indicative of medial temporal atrophy were useful in diagnosing these comorbidities.
Added-value of dynamic contrast-enhanced MRI to conventional MRI for the differentiation between inflammatory myofibroblastic tumor and squamous cell carcinoma in the sinonasal region.
NeuroradiologyThe purpose of this study was to evaluate the additional value of dynamic contrast-enhanced (DCE) MRI and diffusion weighted MRI (DWI) in differentiation between inflammatory myofibroblastic tumor (IMT) and squamous cell carcinoma (SCC) in the sinonasal cavity.
Patients with pathologically proven IMT and SCC in the sinonasal region were enrolled in this retrospective study. All participants underwent conventional MRI and dynamic contrast-enhanced MRI, while a subset of them performed DWI. All the MRI parameters were independently analyzed by two investigators.
This retrospective study included 21 patients with IMT and 55 patients with SCC. Significant differences were found in the conventional MR imaging features including mass margin, T2 signal intensity and track sign of maxillary (p < 0.05). For DCE-MRI features, significant differences were found in progressive centripetal continual enhancement and CImax (p < 0.001 and p = 0.026, respectively). A marginal significant difference was found in ADC values between IMT (0.86 ± 0.59) and SCC (1.14 ± 0.25) (p = 0.061). The conventional MRI analysis revealed that the combination of mass margin and track sign of maxillary yielded an accuracy of 81.6%. Using a combination of progressive centripetal continual enhancement on DCE-MRI and track sign of maxillary in multivariate logistic regression analysis, the accuracy was elevated to 92.1%.
The incorporation of DCE-MRI features into conventional MRI showed improved diagnostic performance in differentiating IMT from SCC in the sinonasal region. The novel progressive centripetal continual enhancement on DCE-MRI is the most effective feature of IMT.
Peak width of skeletonized mean diffusivity: a novel biomarker for white matter damage in spinocerebellar ataxia type 2.
NeuroradiologyPeak width of skeletonized mean diffusivity (PSMD) is a robust and fully automated imaging marker employed to detect microstructural damage in white matter. This study aimed to evaluate whether PSMD reflects the severity of white matter damage and tracks disease progression in patients with spinocerebellar ataxia type 2 (SCA2).
Nine patients with SCA2 and sixteen age- and gender-matched healthy controls were enrolled. Clinical and imaging data were collected at baseline and after 3.5 years. Each participant underwent MRI scans twice to obtain diffusion tensor imaging data, from which PSMD were automatically calculated. Differences in PSMD between SCA2 patients and healthy controls were analyzed using a linear mixed model. Additionally, Spearman's rank correlations were employed to assess associations between PSMD values and clinical variables.
Patients with SCA2 exhibited higher PSMD values at baseline and follow-up compared to HCs, indicating more severe white matter damage. Longitudinal data revealed a continual increase in PSMD values in SCA2 patients over time. The mixed-effects model confirmed significant differences in PSMD values between the two groups, as well as an interaction effect suggesting different progression rates. These findings suggest that SCA2 associates with progressive deterioration of white matter. No significant correlations were observed between PSMD values and clinical variables in this study.
This study underscores the potential of PSMD as a neuroimaging biomarker for detecting microstructural white matter damage and monitoring disease progression in patients with SCA2.
Reliability of distal radius fracture classification systems: a CT based study.
Emergency RadiologyTo assess the reliability and reproducibility of AO/OTA, Frykman and Fernandez classification systems for distal radius fractures on CT.
Four radiologists, including one radiology resident, two musculoskeletal radiology fellows and one radiology consultant independently evaluated CT scans of 115 patients with distal radius fractures and classified the fractures according to AO/OTA, Frykman and Fernandez classification system. To assess reproducibility, a second set of reading was done by two observers after an interval of six weeks. Interobserver reliability was calculated for each classification system using intraclass correlation coefficient (ICC) and using Light's modification of kappa. Intraobserver agreement was calculated using Cohen's kappa.
Interobserver reliability using ICC showed fair agreement for AO/OTA (0.447) and Frykman (0.432) classification system and poor agreement for Fernandez (0.196) classification system. Interobserver agreement using kappa was moderate for AO/OTA fracture (0.447) classification into either of three types, while it was only slight for complete classification into type, group and subgroup (0.177). Interobserver agreement using kappa was slight for Fernandez (0.196) classification systems and moderate for Frykman classification system (0.406). Intraobserver agreement for AO/OTA classification system was moderate for observer 1 (0.449) and slight for observer 2 (0.162). Intraobserver agreement for Frykman classification system was substantial for observer 1(0.754) and moderate for observer 2 (0.496). Intraobserver agreement for Fernandez classification system was moderate for both the observers (0.333, 0.320).
Currently there is no classification system that is fully reproducible. AO/OTA and Frykman classification systems performed better than Fernandez classification system in terms of interobserver reliability. However, Frykman classification system performed better than both AO/OTA and Fernandez classification system in terms of intraobserver reproducibility. Fernandez classification system had worst inter and intraobserver reliability in present study. Reliability and reproducibility of AO/OTA classification system decreased when fractures were divided into subgroups.
