Inpyeong Hwang

Purpose: This study analyzed the malignancy rate of lymph nodes (LNs) according to the concordance of ultrasound (US) and computed tomography (CT) imaging features in patients with thyroid cancer.Methods: A retrospective analysis was performed of 277 cytopathologically confirmed cervical LNs (53.1% malignant) from 228 patients with thyroid cancer who underwent US-guided biopsy and preoperative US and CT. Malignancy rates were calculated according to the concordance of US and CT imaging features by matching cortical hyperechogenicity or abnormal vascularity on US (US-echo-vascularity) with strong or heterogeneous enhancement on CT (CT-enhancement), cystic changes on US (US-cystic) with cystic changes on CT (CT-cystic), and echogenic foci on US (US-EF) with calcifications on CT (CT-calcification). Results: Malignancy rates were significantly higher in positively concordant cases across all imaging features compared to negatively concordant cases (US-echo-vascularity/CT-enhancement: 93.5% vs. 8.2%, P<0.001; US-cystic/CT-cystic: 94.4% vs. 41.3%, P<0.001; and US-EF/CT-calcification: 84.4% vs. 34.5%, P<0.001). Discordance in US-cystic/CT-cystic (96.3% vs. 94.4%, P=0.735) or US-EF/CT-calcification (86.0% vs. 84.4%, P=0.873) findings showed malignancy rates comparable to those for positively concordant cases. However, discordance in US-echo-vascularity/CT-enhancement resulted in a significantly lower malignancy rate than in positively concordant cases (66.8% vs. 93.5%, P<0.001). In this discordant group, CT demonstrated a higher diagnostic odds ratio (3.896 vs. 0.257, P=0.002) and higher sensitivity (81.1% vs. 18.9%, P<0.001) than US.Conclusion: Positive US/CT concordance across all imaging features, as well as discordance in US-cystic/CT-cystic or US-EF/CT-calcification, strongly suggested LN metastasis. CT-enhancement was particularly reliable when discordant with US-echo-vascularity.

황인평, et al. J Korean Soc Radiol. 2026 Sep;87(5):911-921. https://doi.org/10.3348/jksr.2026.0015

To explore how molecular glioblastoma (mol-GBM) differs from histological glioblastoma (hist-GBM) in tumor heterogeneity using multi-parametric physiologic MRI-based tumor habitat analysis. In this multi-institutional retrospective study, imaging data were collected from two tertiary centers: 13 mol-GBMs and 39 hist-GBMs from institution 1 (2007-2024) for habitat definition, model development, and internal validation and nine mol-GBMs were obtained from institution 2 (2020-2024) for external validation. The apparent diffusion coefficient (ADC; cellularity), relative cerebral blood volume (rCBV; vascularity), and volume transfer constant (K^trans; permeability) were binarized into high/low categories, which yielded eight spatial habitat clusters to visualize tumor heterogeneity. Habitat proportions and intratumoral heterogeneity features were compared between groups. Multivariable logistic regression models incorporating habitat-derived features were developed to distinguish mol-GBM from hist-GBM, and their performance was evaluated. Fifty-two patients (hist-GBM, n = 39; mol-GBM, n = 13; mean age, 60.0 ± 11.1 years; 22 male) were evaluated. Habitat analysis revealed that mol-GBM had a significantly lower proportion of the most malignant habitat (cluster 3: low-ADC, high rCBV, high K^trans; 0.80% vs. 7.0%, P < 0.001). The total proportion of high K^trans habitats was markedly lower in the mol-GBM group (5.7% vs. 21.3%, P < 0.001). A habitat-based multivariable model incorporating tumor volume, cluster 3 proportion, high K^trans proportion, and Shannon entropy achieved an area under receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.73-0.97) at internal validation using a leave-one-out cross-validation, which was substantially greater, albeit nonsignificant, than the AUC for tumor size alone (0.70, 95% CI: 0.51-0.85, P = 0.057), with an external validation accuracy of 88.9% (8/9). Multiparametric physiologic MRI habitat analysis demonstrated differences in the tumor heterogeneity between mol-GBM and hist-GBM. Tumor permeability (K^trans) was the most discriminating parameter. Incorporating habitat-derived features alongside tumor volume may improve the discrimination of the two subtypes on MRI analysis. Further studies, including more rigorous external validation, are warranted.

Jeon YH, et al. Korean J Radiol. 2026 ;27:e33. https://doi.org/10.3348/kjr.2025.1073

양동진, et al. J Korean Soc Radiol. 2026 ;87:e8. https://doi.org/10.3348/jksr.2025.0034

An unsupervised learning framework detected blood-brain barrier leakage without pharmacokinetic models and arterial input function and distinguished between different tissue types within tumors, offering potential to improve diffuse glioma diagnosis.

Objectives: To investigate individual variations and outliers in cortical thickness among non‐alcoholic fatty liver disease (NAFLD) patients, ranging from hepatic steatosis to fibrosis, using neuroanatomical normative modeling.Materials and Methods: A cross‐sectional study with 2637 health check‐up subjects was conducted. Among NAFLD patients, hepatic steatosis (n = 556) and fibrosis (n = 57) were determined by hepatic steatosis index and fibrosis‐4 score, respectively. Cortical thickness in 148 different brain regions was assessed using T1‐weighted MRI scans. A publicly available neuroanatomical normative model analyzed cortical thickness distributions with data from around 58,000 participants. The hierarchical Bayesian regression was used to estimate cortical thickness deviation for each region, taking age, sex, and sites into account. On the basis of a normal adaptation set, Z‐scores below −1.96 or above +1.96 per region were classified as outliers. The total outlier count (tOC) was then calculated to quantify regional heterogeneity. Mass univariate analysis was conducted to compare steatosis and fibrosis groups, and the spatial patterns of regional heterogeneity were qualitatively analyzed.Results: Patients with hepatic fibrosis had a higher number of positive outlier regions (mean 6.3 ± 10.3) than hepatic steatosis (mean 4.2 ± 6.2, p = 0.02). Mass univariate group difference testing of 148 brain regions revealed patients with hepatic fibrosis had 6 cortical areas thicker than hepatic steatosis. Two groups showed shared regional heterogeneity in the temporal cortex.Conclusion: Distinct brain atrophy patterns were observed in NAFLD patients compared to the normal group, with more frequent temporal cortex outliers in both hepatic steatosis and fibrosis. Hepatic fibrosis showed slightly increased cortical thickness relative to steatosis.

Purpose: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learning-based reconstruction have shown the potential to improve image quality and reduce scan times. This study aimed to evaluate the effectiveness of accelerated intracranial TOF-MRA using deep learning-based image enhancement (TOF-DL) compared to conventional TOF-MRA (TOF-Con) at both 3-T and 1.5-T. Materials and methods: In this retrospective study, patients who underwent both conventional and 40% accelerated TOF-MRA protocols on 1.5-T or 3-T scanners from July 2022 to March 2023 were included. A commercially available DL-based image enhancement algorithm was applied to the accelerated MRA. Quantitative image quality assessments included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), and vessel sharpness (VS), while qualitative assessments were conducted using a five-point Likert scale. Cohen’s d was used to compare the quantitative image metrics, and a cumulative link mixed regression model analyzed the readers’ scores. Results: A total of 129 patients (mean age, 64 years ± 12 [SD], 99 at 3-T and 30 at 1.5-T) were included. TOF-DL showed significantly higher SNR, CNR, CR, and VS compared to TOF-Con (CNR = 183.89 vs. 45.58; CR = 0.63 vs. 0.59; VS = 0.73 vs. 0.61; all p < 0.001). The improvement in VS was more pronounced at 1.5-T (Cohen’s d = 2.39) compared to 3-T HR and routine (Cohen’s d = 0.83 and 0.75, respectively). TOF-DL also outperformed TOF-Con in qualitative image parameters, enhancing the visibility of small- and medium-sized vessels, regardless of the degree of resolution and field strength. TOF-DL showed comparable diagnostic accuracy (AUC: 0.77–0.85) to TOF-Con (AUC: 0.79–0.87) but had higher specificity for steno-occlusive lesions. CONCLUSIONS: Accelerated intracranial MRA with deep learning-based reconstruction reduces scan times by 40% and significantly enhances image quality over conventional TOF-MRA at both 3-T and 1.5-T.

Choi KS, et al. Korean J Radiol. 2025 Jan;26(1):54-64. https://doi.org/10.3348/kjr.2024.0653