Early therapy evaluation of intra-arterial trastuzumab injection in a human breast cancer xenograft model using multiparametric MR imaging.

PURPOSE: To investigate the treatment efficacy of intra-arterial (IA) trastuzumab treatment using multiparametric magnetic resonance imaging (MRI) in a human breast cancer xenograft model. MATERIALS AND METHODS: Human breast cancer cells (BT474) were stereotaxically injected into the brains of nude mice to obtain a xenograft model. The mice were divided into four groups and subjected to different treatments (IA treatment [IA-T], intravenous treatment [IV-T], IA saline injection [IA-S], and the sham control group). MRI was performed before and at 7 and 14 d after treatment to assess the efficacy of the treatment. The tumor volume, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) MRI parameters (Ktrans, Kep, Ve, and Vp) were measured. RESULTS: Tumor volumes in the IA-T group at 14 d after treatment were significantly lower than those in the IV-T group (13.1 mm3 [interquartile range 8.48-16.05] vs. 25.69 mm3 [IQR 20.39-30.29], p = 0.005), control group (IA-S, 33.83 mm3 [IQR 32.00-36.30], p<0.01), and sham control (39.71 mm3 [IQR 26.60-48.26], p <0.001). The ADC value in the IA-T group was higher than that in the control groups (IA-T, 7.62 [IQR 7.23-8.20] vs. IA-S, 6.77 [IQR 6.48-6.87], p = 0.044 and vs. sham control, 6.89 [IQR 4.93-7.48], p = 0.004). Ktrans was significantly decreased following the treatment compared to that in the control groups (p = 0.002 and p<0.001 for vs. IA-S and sham control, respectively). Tumor growth was decreased in the IV-T group compared to that in the sham control group (25.69 mm3 [IQR 20.39-30.29] vs. 39.71 mm3 [IQR 26.60-48.26], p = 0.27); there was no significant change in the MRI parameters. CONCLUSION: IA treatment with trastuzumab potentially affects the early response to treatment, including decreased tumor growth and decrease of Ktrans, in a preclinical brain tumor model.

Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study.

OBJECTIVES: Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours. METHODS: This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed. RESULTS: The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values. CONCLUSION: Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Bright diffusion sign: A sensitive and specific radiologic biomarker for multinodular and vacuolating neuronal tumor.

BACKGROUND AND PURPOSE: Accurate differentiation between multinodular and vacuolating neuronal tumor (MVNT) and dysembryoplastic neuroepithelial tumor (DNET) is important for treatment decision-making. We aimed to develop an accurate radiologic diagnostic model for differentiating MVNT from DNET using T2WI and diffusion-weighted imaging (DWI). MATERIALS AND METHODS: A total of 56 patients (mean age, 47.48±17.78 years; 31 women) diagnosed with MVNT (n = 37) or DNET (n = 19) who underwent brain MRI, including T2WI and DWI, were included. Two board-certified neuroradiologists performed qualitative (bubble appearance, cortical involvement, bright diffusion sign, and bright apparent diffusion coefficient [ADC] sign) and quantitative (nDWI and nADC) assessments. A diagnostic tree model was developed with significant and reliable imaging findings using an exhaustive chi-squared Automatic Interaction Detector (CHAID) algorithm. RESULTS: In visual assessment, the imaging features that showed high diagnostic accuracy and interobserver reliability were the bright diffusion sign and absence of cortical involvement (bright diffusion sign: accuracy, 94.64 %; sensitivity, 91.89 %; specificity, 100.00 %; interobserver agreement, 1.00; absence of cortical involvement: accuracy, 92.86 %; sensitivity, 89.19 %; specificity, 100.00 %; interobserver agreement, 1.00). In quantitative analysis, nDWI was significantly higher in MVNT than in DENT (1.52 ± 0.34 vs. 0.91 ± 0.27, p < 0.001), but the interobserver agreement was fair (intraclass correlation coefficient = 0.321). The overall diagnostic accuracy of the tree model with visual assessment parameters was 98.21 % (55/56). CONCLUSION: The bright diffusion sign and absence of cortical involvement are accurate and reliable imaging findings for differentiating MVNT from DNET. By using simple, intuitive, and reliable imaging findings, such as the bright diffusion sign, MVNT can be accurately differentiated from DNET.

The effect of inflammation markers on cortical thinning in major depressive disorder: A possible mediator of depression and cortical changes.

BACKGROUND: Major depressive disorder (MDD) is a prevalent mental health condition with significant societal impact. Owing to the intricate biological diversity of MDD, treatment efficacy remains limited. Immune biomarkers have emerged as potential predictors of treatment response, underscoring the interaction between the immune system and the brain. This study investigated the relationship between cytokine levels and cortical thickness in patients with MDD, focusing on the corticolimbic circuit, to elucidate the influence of neuroinflammation on structural brain changes and contribute to a deeper understanding of the pathophysiology of MDD. METHOD: A total of 114 patients with MDD and 101 healthy controls (HC) matched for age, sex, and body mass index (BMI) were recruited. All participants were assessed for depression severity using the Hamilton Depression Rating Scale (HDRS), and 3.0 T T1 weighted brain MRI data were acquired. Additionally, cytokine levels were measured using a highly sensitive bead-based multiplex immunosorbent assay. RESULTS: Patients diagnosed with MDD exhibited notably elevated levels of interleukin-6 (p = 0.005) and interleukin-8 (p = 0.005), alongside significant cortical thinning in the left anterior cingulate gyrus and left superior frontal gyrus, with these findings maintaining significance even after applying Bonferroni correction. Furthermore, increased interleukin-6 and interleukin-8 levels in patients with MDD are associated with alterations in the left frontomarginal gyrus and right anterior cingulate cortex (ACC). CONCLUSIONS: This suggests a potential influence of neuroinflammation on right ACC function in MDD patients, warranting longitudinal research to explore interleukin-6 and interleukin-8 mediated neurotoxicity in MDD vulnerability and brain morphology changes.

Deep Learning-Based Synthetic TOF-MRA Generation Using Time-Resolved MRA in Fast Stroke Imaging.

BACKGROUND AND PURPOSE: Time-resolved MRA enables collateral evaluation in acute ischemic stroke with large-vessel occlusion; however, a low SNR and spatial resolution impede the diagnosis of vascular occlusion. We developed a CycleGAN-based deep learning model to generate high-resolution synthetic TOF-MRA images using time-resolved MRA and evaluated its image quality and clinical efficacy. MATERIALS AND METHODS: This retrospective, single-center study included 397 patients who underwent both TOF- and time-resolved MRA between April 2021 and January 2022. Patients were divided into 2 groups for model development and image-quality validation. Image quality was evaluated qualitatively and quantitatively with 3 sequences. A multireader diagnostic optimality evaluation was performed by 16 radiologists. For clinical validation, we evaluated 123 patients who underwent fast stroke MR imaging to assess acute ischemic stroke. The diagnostic confidence level and decision time for large-vessel occlusion were also evaluated. RESULTS: Median values of overall image quality, noise, sharpness, venous contamination, and SNR for M1, M2, the basilar artery, and posterior cerebral artery are better with synthetic TOF than with time-resolved MRA. However, with respect to real TOF, synthetic TOF presents worse median values of overall image quality, sharpness, vascular conspicuity, and SNR for M3, the basilar artery, and the posterior cerebral artery. During the multireader evaluation, radiologists could not discriminate synthetic TOF images from TOF images. During clinical validation, both readers demonstrated increases in diagnostic confidence levels and decreases in decision time. CONCLUSIONS: A CycleGAN-based deep learning model was developed to generate synthetic TOF from time-resolved MRA. Synthetic TOF can potentially assist in the detection of large-vessel occlusion in stroke centers using time-resolved MRA.

Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.

As many human organs exist in pairs or have symmetric appearance and loss of symmetry may indicate pathology, symmetry evaluation on medical images is very important and has been routinely performed in diagnosis of diseases and pretreatment evaluation. Therefore, applying symmetry evaluation function to deep learning algorithms in interpreting medical images is essential, especially for the organs that have significant inter-individual variation but bilateral symmetry in a person, such as mastoid air cells. In this study, we developed a deep learning algorithm to detect bilateral mastoid abnormalities simultaneously on mastoid anterior-posterior (AP) views with symmetry evaluation. The developed algorithm showed better diagnostic performance in diagnosing mastoiditis on mastoid AP views than the algorithm trained by single-side mastoid radiographs without symmetry evaluation and similar to superior diagnostic performance to head and neck radiologists. The results of this study show the possibility of evaluating symmetry in medical images with deep learning algorithms.

Orientation of the ultrasound probe to identify the taller-than-wide sign of thyroid malignancy: a registry-based study with the Thyroid Imaging Network of Korea.

PURPOSE: Although the taller-than-wide (TTW) sign has been regarded as one of the most specific ultrasound (US) features of thyroid malignancy, uncertainty still exists regarding the US probe's orientation when evaluating it. This study investigated which US plane would be optimal to identify the TTW sign based on malignancy risk stratification using a registry-based imaging dataset. METHODS: A previous study by 17 academic radiologists retrospectively analyzed the US images of 5,601 thyroid nodules (≥1 cm, 1,089 malignant and 4,512 benign) collected in the webbased registry of Thyroid Imaging Network of Korea through the collaboration of 26 centers. The present study assessed the diagnostic performance of the TTW sign itself and fine needle aspiration (FNA) indications via a comparison of four international guidelines, depending on the orientation of the US probe (criterion 1, transverse plane; criterion 2, either transverse or longitudinal plane). RESULTS: Overall, the TTW sign was more frequent in malignant than in benign thyroid nodules (25.3% vs. 4.6%). However, the statistical differences between criteria 1 and 2 were negligible for sensitivity, specificity, and area under the curve (AUC) based on the size effect (all P<0.05, Cohen's d=0.19, 0.10, and 0.07, respectively). Moreover, the sensitivity, specificity, and AUC of the four FNA guidelines were similar between criteria 1 and 2 (all P>0.05, respectively). CONCLUSION: A longitudinal US probe orientation provided little additional diagnostic value over the transverse orientation in detecting the TTW sign of thyroid nodules.

Malignancy risk stratification of thyroid nodules according to echotexture and degree of hypoechogenicity: a retrospective multicenter validation study.

Various risk stratification systems show discrepancies in the ultrasound lexicon of nodule echotexture and hypoechogenicity. This study aimed to determine the malignancy risk of thyroid nodules according to their echotexture and degree of hypoechogenicity. From June to September 2015, we retrospectively evaluated 5601 thyroid nodules with final diagnoses from 26 institutions. Nodules were stratified according to the echotexture (homogeneous vs. heterogeneous) and degree of hypoechogenicity (mild, moderate, or marked). We calculated the malignancy risk according to composition and suspicious features. Heterogeneous hypoechoic nodules showed a significantly higher malignancy risk than heterogeneous isoechoic nodules (P ≤ 0.017), except in partially cystic nodules. Malignancy risks were not significantly different between homogeneous versus heterogeneous nodules in both hypoechoic (P ≥ 0.086) and iso- hyperechoic nodules (P ≥ 0.05). Heterogeneous iso-hyperechoic nodules without suspicious features showed a low malignancy risk. The malignancy risks of markedly and moderately hypoechoic nodules were not significantly different in all subgroups (P ≥ 0.48). Marked or moderately hypoechoic nodules showed a significantly higher risk than mild hypoechoic (P ≤ 0.016) nodules. The predominant echogenicity effectively stratifies the malignancy risk of nodules with heterogeneous echotexture. The degree of hypoechogenicity could be stratified as mild versus moderate to marked hypoechogenicity.

Synthetic Time of Flight Magnetic Resonance Angiography Generation Model Based on Cycle-Consistent Generative Adversarial Network Using PETRA-MRA in the Patients With Treated Intracranial Aneurysm.

BACKGROUND: Pointwise encoding time reduction with radial acquisition (PETRA) magnetic resonance angiography (MRA) is useful for evaluating intracranial aneurysm recurrence, but the problem of severe background noise and low peripheral signal-to-noise ratio (SNR) remain. Deep learning could reduce noise using high- and low-quality images. PURPOSE: To develop a cycle-consistent generative adversarial network (cycleGAN)-based deep learning model to generate synthetic TOF (synTOF) using PETRA. STUDY TYPE: Retrospective. POPULATION: A total of 377 patients (mean age: 60 ± 11; 293 females) with treated intracranial aneurysms who underwent both PETRA and TOF from October 2017 to January 2021. Data were randomly divided into training (49.9%, 188/377) and validation (50.1%, 189/377) groups. FIELD STRENGTH/SEQUENCE: Ultra-short echo time and TOF-MRA on a 3-T MR system. ASSESSMENT: For the cycleGAN model, the peak SNR (PSNR) and structural similarity (SSIM) were evaluated. Image quality was compared qualitatively (5-point Likert scale) and quantitatively (SNR). A multireader diagnostic optimality evaluation was performed with 17 radiologists (experience of 1-18 years). STATISTICAL TESTS: Generalized estimating equation analysis, Friedman's test, McNemar test, and Spearman's rank correlation. P < 0.05 indicated statistical significance. RESULTS: The PSNR and SSIM between synTOF and TOF were 17.51 [16.76; 18.31] dB and 0.71 ± 0.02. The median values of overall image quality, noise, sharpness, and vascular conspicuity were significantly higher for synTOF than for PETRA (4.00 [4.00; 5.00] vs. 4.00 [3.00; 4.00]; 5.00 [4.00; 5.00] vs. 3.00 [2.00; 4.00]; 4.00 [4.00; 4.00] vs. 4.00 [3.00; 4.00]; 3.00 [3.00; 4.00] vs. 3.00 [2.00; 3.00]). The SNRs of the middle cerebral arteries were the highest for synTOF (synTOF vs. TOF vs. PETRA; 63.67 [43.25; 105.00] vs. 52.42 [32.88; 74.67] vs. 21.05 [12.34; 37.88]). In the multireader evaluation, there was no significant difference in diagnostic optimality or preference between synTOF and TOF (19.00 [18.00; 19.00] vs. 20.00 [18.00; 20.00], P = 0.510; 8.00 [6.00; 11.00] vs. 11.00 [9.00, 14.00], P = 1.000). DATA CONCLUSION: The cycleGAN-based deep learning model provided synTOF free from background artifact. The synTOF could be a versatile alternative to TOF in patients who have undergone PETRA for evaluating treated aneurysms. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Clinical Relevance of Computed Tomography Perfusion-Estimated Infarct Volume in Acute Ischemic Stroke Patients within the 6-h Therapeutic Time Window.

INTRODUCTION: Although the estimated infarct volume on baseline computed tomography perfusion (CTP) can identify patients who are likely to benefit from endovascular thrombectomy (EVT) in late time window strokes, the role of CTP imaging in early time windows has not been established. We assessed the clinical impact of CTP-estimated infarct volume on long-term prognosis after EVT, particularly in patients with early time window stroke. METHODS: We retrospectively reviewed patients who underwent pretreatment CTP and EVT for large vessel occlusion in the anterior circulation within 6 h after symptom onset between March 2014 and February 2019. The infarct volume at baseline CTP was estimated using commercially available software (RAPID, iSchemaView, Menlo Park, CA, USA) with a cerebral blood flow threshold <30% of the normal brain. Risk factors for poor outcome after EVT were evaluated, and a receiver operating characteristic (ROC) curve analysis was used to identify CTP-estimated infarct volumes that optimally predicted the development of symptomatic intracranial hemorrhage (sICH) and poor outcomes (modified Rankin Scale [mRS] 3-6) at 90 days. RESULTS: Of 120 patients, successful recanalization was achieved in 89 (74.2%) patients, while 61 (50.8%) showed poor outcomes at 90 days. Among 89 patients with successful recanalization after EVT, age, diabetes, clinical stroke severity, CTP-estimated infarct volume, and sICH development were independently associated with 90-day clinical outcomes. ROC analysis identified infarct volumes of ≥88.5 mL and ≥74 mL as the optimal thresholds for predicting poor outcome and development of sICH, respectively. Patients with large infarct volumes showed poorer outcomes (odds ratio [OR], 7.704; 95% confidence interval [CI], 1.528-38.839) and higher rates of sICH development (OR, 10.857; 95% CI, 1.835-64.235). Among patients with large infarction volumes (≥88.5 mL), the 90-day mRS demonstrated a shift toward better outcomes in patients with successful recanalization. CONCLUSION: Larger initial infarct volumes are significantly associated with worse clinical outcomes in patients who underwent EVT because of early time window stroke. Furthermore, our study of 6-h data demonstrated that an initial infarction of more than a certain volume might be an independent risk factor for sICH development and poor outcomes even in patients with successful recanalization. However, we still observed benefits of EVT in patients with large ischemic cores. The CTP-estimated infarct volume might be an important prognostic factor after EVT rather than a biomarker predicting treatment effectiveness.

Development and validation of visual grading system for stenosis in intracranial atherosclerotic disease on time-of-flight magnetic resonance angiography.

OBJECTIVES: Although the overestimation problem of time-of-flight magnetic resonance angiography (TOF-MRA) applying the warfarin-aspirin symptomatic intracranial disease (WASID) method to assess intracranial arterial stenosis has often been suggested, no pertinent grading system for TOF-MRA has been developed. We aimed to develop and evaluate the performance of a visual grading system for intracranial arterial stenosis on TOF-MRA (MRAVICAST). METHODS: This single-center cohort study analyzed prospective observational registry data from a comprehensive stroke center between January 2014 and February 2020. Patients with confirmed stenosis of the intracranial large arteries who underwent confirmative digital subtraction angiography (DSA) were included; a 4-point grading system was developed based on physical characteristics of TOF-MRA. The overall diagnostic accuracies of MRAVICAST for each grade, interobserver reproducibility, and positive predictive values for > 50% and > 70% stenoses were evaluated. RESULTS: We analyzed 132 segments with intracranial atherosclerotic stenosis from 71 patients (34 men and 37 women; mean age, 61.0 ± 15.25 years; range, 21-89 years). The overall diagnostic accuracy of MRAVICAST (93.9%, 124/132) was higher than that of MRAWASID (50.8%, 67/132) for each grade. The degree of stenosis did not differ significantly between MRAVICAST and DSAWASID (p = .849). Regarding reproducibility, MRAVICAST demonstrated excellent interobserver agreement (ICC, 0.989; 95% CI, 0.979-0.999). The positive predictive values of MRAVICAST for the diagnosis of > 50% and > 70% stenoses were 97.3% and 100.0%, respectively. CONCLUSIONS: The new intuitive grading system accurately and reliably determined the degree of stenosis in intracranial arterial atherosclerosis patients. MRAVICAST could be a versatile alternative to MRAWASID for evaluating intracranial arterial stenosis. KEY POINTS: • In this retrospective diagnostic study (sample: 132 stenotic segments), the overall accuracy of the visual grading system (MRAVICAST) was 94%, and positive predictive value for > 50% stenosis was 97%. • In the era of medical treatment for intracranial atherosclerotic stenosis, MRAVICAST could be a versatile alternative method to MRAWASID for evaluating intracranial arterial stenosis.

Transient global amnesia: Signal alteration in 2D/3D T2-FLAIR sequences.

PURPOSE: Transient global amnesia (TGA) is one of the most enigmatic syndromes in clinical neurology. The detection rate of TGA lesions in 2D/3D FLAIR sequences has not been evaluated. METHODS: A total of 201 patients (44 men and 157 women; mean age, 60.34 ± 9.23 years; range, 22-91 years) diagnosed with TGA, who underwent diffusion-weighted imaging (DWI; b = 1000 s/mm2, b = 2000 s/mm2, 4 mm) and/or 2D/3D-FLAIR sequences (4 mm, 0.9 mm; respectively) using 3-T MRI within 28 days after onset of TGA symptoms, were included in this single-center, retrospective, cross-sectional study. Hippocampal lesions were visually assessed in all sequences and detection rates were analyzed according to imaging timing (1 day, 2-4 days, 5-7 days, 8-11 days, and 12-28 days) and kinds of sequences. RESULTS: The detection rates were highest 2-4 days after symptom onset in all sequences, and that was higher in order of b = 2000 (75.28% [67/89]), b = 1000 (63.92% [62/97]), 3D-FLAIR (59.38% [19/32]), and 2D-FLAIR (15.15% [15/99]). On FLAIR sequences, detectability was lower 5-7 days after onset than that 2-4 days after onset (2D-FLAIR, 15.15% [15/99] vs. 5.56% [1/18]; 3D-FLAIR, 59.38% [19/32] vs. 0.00% [0/1]). CONCLUSION: FLAIR signal changes occur in approximately 60% of TGA patients 2-4 days after symptom onset, and decrease after 5 days. It is postulated that the pathophysiology of TGA might differ from common ischemic changes.

Ultrashort Echo Time Magnetic Resonance Angiography in Follow-up of Intracranial Aneurysms Treated With Endovascular Coiling: Comparison of Time-of-Flight, Pointwise Encoding Time Reduction With Radial Acquisition, and Contrast-Enhanced Magnetic Resonance Angiography.

BACKGROUND: The optimal magnetic resonance angiography (MRA) sequence for assessing the aneurysm occlusion state or in-stent flow after endovascular coiling is not well established. OBJECTIVE: To evaluate the diagnostic performance of pointwise encoding time reduction with radial acquisition (PETRA)-MRA in patients who underwent endovascular coiling relative to that of time-of-flight (TOF)-MRA and contrast-enhanced (CE)-MRA. METHODS: We evaluated the aneurysm occlusion state using digital subtraction angiography (DSA) and MRA. In patients who underwent stent-assisted coiling, we estimated the visibility of in-stent flow. RESULTS: We enrolled 189 patients with assessable TOF, PETRA, and CE-MRAs after coiling. In patients who underwent simple coiling (128 patients), PETRA showed a higher sensitivity in the detection of residual flow than TOF and CE (PETRA, 100%; CE, 83%; TOF, 80%). There were no significant differences in the height of residual flow between DSA (0.68 ± 1.45 mm) and PETRA (0.70 ± 1.50 mm; P = 1.000). In patients who underwent stent-assisted coiling (61 patients), PETRA showed the highest sensitivity (88%) in detecting residual flow (CE, 56%; TOF, 31%). Regarding in-stent flow, PETRA, CE, and TOF showed visual scores of ≥3 with frequencies of 96.7%, 85.2%, and 37.7%, respectively. Relative signal-to-noise ratio of PETRA (0.62 ± 0.18) was significantly higher than that of CE (0.56 ± 0.12) and TOF (0.39 ± 0.12; P < .001 for both). CONCLUSION: PETRA-MRA showed excellent diagnostic performance in terms of residual flow detection and in-stent flow assessment. PETRA could be a versatile alternative sequence for following up patients with coiled aneurysm.

A Multicenter Survey of Acute Stroke Imaging Protocols for Endovascular Thrombectomy.

PURPOSE: Identifying current practices in acute stroke imaging is essential for establishing optimal imaging protocols. We surveyed and assessed the current status of acute stroke imaging for endovascular thrombectomy (EVT) at tertiary hospitals throughout South Korea. MATERIALS AND METHODS: An electronic questionnaire on imaging protocols for EVT in patients with acute ischemic stroke was e-mailed to physicians at 42 registered tertiary hospitals, and their responses were collected between February and March 2020. RESULTS: Of the 36 hospitals participating in the survey, 69% (25/36) adopted computed tomography (CT)-based protocols, whereas 31% (11/36) adopted magnetic resonance (MR)-based protocols. Non-enhanced CT (NECT) was the initial imaging study at 28%, NECT with CT angiography (CTA) at 36%, and NECT with CTA and CT perfusion (CTP) at 33% of hospitals. Perfusion imaging was performed at 61% (22/36), CTP at 44% (16/36), and MR perfusion at 17% (6/36) of hospitals. Multiphase CTA was performed at 67%, single-phase CTA at 11%, time-of-flight MR angiography (MRA) at 8%, contrast-enhanced MRA at 8%, and both at 6% of hospitals. For late time window stroke, 50% of hospitals used identical imaging protocols to those for early time window stroke, 39% used additional MR imaging (MRI), and 6% converted the imaging strategy from CT to MRI. Post-processing programs were used at 28% (10/36), and RAPID software at 14% (5/36) of hospitals, respectively. Most hospitals (92%) used the same imaging protocols for posterior and anterior circulation strokes. CONCLUSION: Our multicenter survey demonstrated considerable heterogeneity in acute stroke imaging protocols across South Korean tertiary hospitals, suggesting that hospitals refine their imaging protocols according to hospital-specific conditions.

Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.

OBJECTIVES: This study aimed to compare the diagnostic performance of deep learning algorithm trained by single view (anterior-posterior (AP) or lateral view) with that trained by multiple views (both views together) in diagnosis of mastoiditis on mastoid series and compare the diagnostic performance between the algorithm and radiologists. METHODS: Total 9,988 mastoid series (AP and lateral views) were classified as normal or abnormal (mastoiditis) based on radiographic findings. Among them 792 image sets with temporal bone CT were classified as the gold standard test set and remaining sets were randomly divided into training (n = 8,276) and validation (n = 920) sets by 9:1 for developing a deep learning algorithm. Temporal (n = 294) and geographic (n = 308) external test sets were also collected. Diagnostic performance of deep learning algorithm trained by single view was compared with that trained by multiple views. Diagnostic performance of the algorithm and two radiologists was assessed. Inter-observer agreement between the algorithm and radiologists and between two radiologists was calculated. RESULTS: Area under the receiver operating characteristic curves of algorithm using multiple views (0.971, 0.978, and 0.965 for gold standard, temporal, and geographic external test sets, respectively) showed higher values than those using single view (0.964/0.953, 0.952/0.961, and 0.961/0.942 for AP view/lateral view of gold standard, temporal external, and geographic external test sets, respectively) in all test sets. The algorithm showed statistically significant higher specificity compared with radiologists (p = 0.018 and 0.012). There was substantial agreement between the algorithm and two radiologists and between two radiologists (κ = 0.79, 0.8, and 0.76). CONCLUSION: The deep learning algorithm trained by multiple views showed better performance than that trained by single view. The diagnostic performance of the algorithm for detecting mastoiditis on mastoid series was similar to or higher than that of radiologists.

Differentiation of hemorrhagic infarction from primary intracerebral hemorrhage in the chronic period.

Regarding incidentally found old hemorrhagic foci on gradient-echo magnetic resonance imaging (GRE), it is difficult to distinguish whether the foci are the consequence of hemorrhagic infarction (HI) or primary intracerebral hemorrhage (PICH). We analyzed the radiological characteristics of patients with a definite history of HI or PICH by reviewing long-term follow-up GRE. We retrospectively enrolled patients with HI or PICH, verified by clinical history and radiological findings, who had undergone follow-up GRE at least 3 months after the first imaging. The shape of the hemorrhagic lesion was classified as "cavitation" or "no cavitation." The shape of the hemosiderin rim was classified as total dark rim and partial dark rim. Hyperintense perilesional signal was determined when an obvious hyperintensity on T2-weighted image was present. Further, we compared the radiological characteristics between HI and PICH. In total, 69 patients (38 with HI and 31 with PICH) were enrolled, of whom 45 (65%) were men. The mean patient age was 65.5 ± 12.7 years. The mean time interval from the initial stroke onset to the follow-up image was 56.2 months. Hyperintense perilesional signal was observed in 38 patients; it was associated with HI (33/38 vs. 5/31, p < 0.001). Furthermore, partial dark rim was associated with HI (34/40 vs. 4/29, p < 0.001). Cavitation was more frequently observed in patients with HI than in those with PICH (36/60 vs. 2/9, p = 0.068). Presence of hyperintense perilesional signal and partially encasing dark hemosiderin rim suggest that chronic hemorrhagic foci are the sequelae of HI, not PICH.

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach.

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIFDSC) and AIF measured at DCE MRI (AIFDCE). The model was trained to translate AIFDCE into AIFDSC, and after training, outputted neural-network-generated AIF (AIFgenerated DSC) with input AIFDCE. By using the three different AIFs, volume transfer constant (Ktrans), fractional volume of extravascular extracellular space (Ve), and vascular plasma space (Vp) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs. Results The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIFDCE (mean Ktrans, 0.88 [95% confidence interval {CI}: 0.81, 0.93] vs 0.72 [95% CI: 0.63, 0.79], P = .04; mean Ve, 0.87 [95% CI: 0.79, 0.92] vs 0.70 [95% CI: 0.61, 0.77], P = .049, respectively). Ktrans and Ve showed higher intraclass correlation coefficients for AIFgenerated DSC than for AIFDCE (0.91 vs 0.38, P < .001; and 0.86 vs 0.60, P < .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. © RSNA, 2020 Online supplemental material is available for this article.

Altered Vascular Permeability in Migraine-associated Brain Regions: Evaluation with Dynamic Contrast-enhanced MRI.

Background Recent studies showed the possible association between inflammation-induced blood-brain barrier (BBB) structural changes followed by greater permeability of the BBB and chronic pain. Thus, measurement of BBB breakdown would be a valuable aid in the diagnosis in migraine. Dynamic contrast material-enhanced (DCE) MRI can determine perfusion and permeability properties related to the BBB. Purpose To evaluate the relationship between permeability of the BBB in migraine-associated brain regions by using DCE MRI. Materials and Methods In this prospective study, from September 2016 to December 2017, 56 study participants underwent DCE MRI after gadobutrol administration and were classified into migraine (n = 35) and healthy control (n = 21) groups. Automatic volumetric segmentation was performed on the pre-contrast-enhanced T1-weighted images by using FreeSurfer, and migraine-associated brain region masks were extracted by using the software NordicICE. The corresponding maps for pharmacokinetic parameters Ktrans (the volume transfer constant) and Vp (the fractional plasma volume) were coregistered with the region-of-interest masks, and their mean values of corresponding total volume of interest were calculated. For comparison analyses, the Mann-Whitney tests were used. Receiver operating characteristic curve analysis and Spearman rank correlation tests were used to identify correlations between clinical characteristics and the aforementioned perfusion parameters. Results Mean age was younger in the migraine group (mean ± standard deviation, 57 years ± 12) than in the healthy control group (mean, 71 years ± 8) (P < .001). In the migraine group, the mean value of Vp in the left amygdala (median, 0.27 mL/100 g) was lower than that in the healthy control group (median, 0.39 mL/100 g) (P = .04). The mean value of Vp in the left amygdala was correlated with the intensity of headache attack in participants with migraine (correlation coefficient, -0.34; P = .04). Conclusion Lower fractional plasma volume in the left amygdala was observed in participants with migraine than in healthy participants. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Carroll and Ginat in this issue.

Cervical necrotic lymphadenopathy: a diagnostic tree analysis model based on CT and clinical findings.

OBJECTIVES: To establish a diagnostic tree analysis (DTA) model based on computed tomography (CT) findings and clinical information for differential diagnosis of cervical necrotic lymphadenopathy, especially in regions where tuberculous lymphadenitis and Kikuchi disease are common. METHODS: A total of 290 patients (147 men and 143 women; mean age (years), 46.2 ± 19.5; range, 3-91) with pathologically confirmed metastasis (n = 110), tuberculous lymphadenitis (n = 73), Kikuchi disease (n = 71), and lymphoma (n = 36) who underwent contrast-enhanced neck CT were included. The patients were randomly divided into training (86%, 248/290) and validation (14%, 42/290) datasets to assess diagnostic performance of the DTA model. Two sorts of DTA models were created using a classification and regression tree algorithm on the basis of CT findings alone and that combined with clinical findings. RESULTS: In the DTA model based on CT findings alone, perinodal infiltration, number of the necrotic foci, percentage of necrotic lymph node (LN), degree of necrosis, margin and shape of the necrotic portion, shape of the LN, and enhancement ratio (cutoff value, 1.93) were significant predictors for differential diagnosis of cervical necrotic lymphadenopathy. The overall accuracy was 80.6% and 73.8% in training and validation datasets. In the model based on imaging and clinical findings, tenderness, history of underlying malignancy, percentage of necrotic LN, degree of necrosis, and number of necrotic foci were significant predictors. The overall accuracy was 87.1% and 88.1% in training and external validation datasets. CONCLUSIONS: The DTA model based on CT imaging and clinical findings may be helpful for the diagnosis of cervical necrotic lymphadenopathy. KEY POINTS: • The diagnostic tree analysis model based on CT may be useful for differential diagnosis of cervical necrotic lymphadenopathy. • Perinodal infiltration, number of necrotic foci, percentage of necrotic lymph nodes, degree of necrosis, margin and shape of necrotic portion, lymph node shape, and enhancement ratio were the most significant predictors.

Predicting the Development of Surgically Induced Chronic Kidney Disease After Total Nephrectomy Using Body Surface Area-Adjusted Renal Cortical Volume on CT Angiography.

OBJECTIVE: The purpose of this study was to predict the probability of surgically induced chronic kidney disease (CKD) developing in patients who underwent total nephrectomy by measuring the body surface area (BSA)-adjusted renal cortical volume (RCV) with preoperative CT angiography (CTA). MATERIALS AND METHODS: A total of 105 patients with a normal preoperative estimated glomerular filtration rate (eGFR) who underwent preoperative CTA and subsequent total nephrectomy for kidney donation (n = 67) or a renal tumor (n = 38) were included in this retrospective study. Patients were divided into group A (patients without surgically induced CKD; n = 61) and group B (patients with surgically induced CKD; n = 44) according to postoperative renal function. The preoperative and postoperative eGFR and other laboratory findings were collected, and the BSA-adjusted postnephrectomy RCV was measured using a semiautomated segmentation technique on CTA. Multiple logistic regression analysis was used to determine the formula for predicting the probability of development of surgically induced CKD; external validation was conducted using the validation dataset (n = 28). RESULTS: The estimated probability of surgically induced CKD developing can be calculated using the following formula: logit (probability of surgically induced CKD developing) = [1.431 × I (reason for operation was renal tumor)] + (-0.097 × preoperative eGFR) + (-0.033 × BSA-adjusted postnephrectomy RCV) + 10.937, where I denotes an indicator function (I = 1, reason for operation was renal tumor; I = 0, reason for operation was kidney donation). The optimal cutoff value derived from 10,000 bootstrapped samples was 0.444 (95% CI, 0.298-0.681). The formula was determined to be a good tool for prediction of surgically induced CKD on external validation (AUC value, 0.894). CONCLUSION: The probability of CKD developing in patients who undergo total nephrectomy may be predicted using a BSA-adjusted postnephrectomy RCV volume measured on preoperative CTA.