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.

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.

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.

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.

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.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging.

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIFDSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIFDCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIFDSC and AIFDCE were obtained from each image. Volume transfer constant (Ktrans), volume of vascular plasma space (vp), and volume of extravascular extracellular space (ve) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIFDSC-driven mean Ktrans and ve were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIFDCE (area under the ROC curve: mean Ktrans, 0.796 vs 0.645, P = .038; mean ve, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.737 vs 0.095; vp, 0.848 vs 0.728; ve, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIFDSC than AIFDCE. Conclusion AIFDSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIFDCE. © RSNA, 2017 Online supplemental material is available for this article.

Prevention of total thyroidectomy in noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) based on combined interpretation of ultrasonographic and cytopathologic results.

OBJECTIVE: To explore the potential preoperative ultrasonography (US) and cytopathological features to avoid total thyroidectomy in NIFTP. CONTEXT: Recently, it has been proposed that that noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) be classified as tumours, rather than cancer. PATIENTS: A total of 142 surgically proven follicular variant papillary thyroid carcinomas (FVPTCs; 45 NIFTP, 97 non-NIFTP; mean size: 20.4±11.0 mm, range: 10.0-65.0 mm) from 142 patients were included in this study. MEASUREMENTS: Three preoperative features of thyroid nodules (each US finding, US and Bethesda category) were compared in NIFTP and non-NIFTP groups. The preoperative decision-making process to avoid total thyroidectomy in NIFTP was evaluated based on combination of those features. RESULTS: In each US finding, there was only significantly less macrocalcification in the NIFTP group than in the non-NIFTP group (8.8% [4/45] vs 32.0% [31/97], P = .006). In US category, all of the NIFTP nodules were a low or intermediate suspicion (100% [45/45]). In Bethesda category, 26.7% [12/45] of the NIFTP was diagnosed as either suspicious malignancy or malignant, which increased the risk of a total thyroidectomy. In our study, a total thyroidectomy might be avoided in all of the NIFTP cases if lobectomy was selected for the nodules classified as a low or intermediate suspicion in US, despite being classified as a suspicious malignancy or malignant by cytopathology. CONCLUSIONS: Combining the US and cytopathological results could sensitively reduce total thyroidectomy in cases of NIFTP.

Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging.

OBJECTIVES: To evaluate the diagnostic performance of arterial spin labelling perfusion weighted images (ASL-PWIs) to differentiate primary CNS lymphoma (PCNSL) from glioblastoma (GBM). METHODS: ASL-PWIs of pathologically confirmed PCNSL (n = 21) or GBM (n = 93) were analysed. For qualitative analysis, tumours were visually scored into five categories based on ASL-CBF maps. For quantitative analysis, normalised CBF values were derived by contralateral grey matter (GM) in intra- and peritumoral areas (nCBFintratumoral and nCBFperitumoral, respectively). Visual scoring scales and quantitative parameters from PCNSL and GBM were compared. In addition, the area under the receiver-operating characteristic (ROC) curve was used to determine the diagnostic accuracy of ASL-PWI for differentiating PCNSL from GBM. Weighted kappa or intraclass correlation coefficients (ICCs) were used to assess reliability between two observers. RESULTS: In qualitative analysis, scores 5 (CBFintratumoral>CBFGM, 68.8% [64/93]) and 4 (CBFintratumoral ≈ CBFGM, 47.6% [10/21]) were the most frequently reported scores for GBM and PCNSL, respectively. In quantitative analysis, both nCBFintratumoral and nCBFperitumoral in PCNSL were significantly lower than those in the GBM (nCBFintratumoral, 0.89 ± 0.59 [mean and SD] vs. 2.68 ± 1.89, p < 0.001; nCBFperitumoral, 0.17 ± 0.08 vs. 0.45 ± 0.28, p < 0.001). nCBFperitumoral demonstrated the best diagnostic performance (area under the ROC curve: visual scoring, 0.814; nCBFintratumoral, 0.849; nCBFperitumoral, 0.908; p < 0.001 for all). Interobserver agreements for visual scoring (weighted kappa = 0.869), nCBFintratumoral_GM (ICC = 0.958) and nCBFperitumoral_GM (ICC = 0.947) were all excellent. CONCLUSIONS: ASL-PWI performs well in differentiating PCNSL from GBM in both qualitative and quantitative analyses. KEY POINTS: • ASL-PWI performs well (AUC > 0.8) in differentiating PCNSL from GBM. • The visual scoring template demonstrated good diagnostic performance, similar to quantitative analysis. • nCBFperitumoral demonstrated better diagnostic performance than nCBFintratumoral or visual scoring.

PREDICTION OF NONDIAGNOSTIC RESULTS IN FINE-NEEDLE ASPIRATION OF THYROID NODULES: UTILITY OF ON-SITE GROSS VISUAL ASSESSMENT OF SPECIMENS FOR LIQUID-BASED CYTOLOGY.

OBJECTIVE: To explore a comprehensive approach for on-site gross visual assessments of liquid-based cytology (LBC) specimens of thyroid nodules and determine morphologic criteria that help predict nondiagnostic rates. METHODS: Two-hundred nodules from 165 patients who underwent fine-needle aspiration (FNA) at our hospital were included in this prospectively designed, retrospective analysis. Specimens were visually assessed on-site for three morphologic categories (specimen color, specimen volume, and particle count) using a 5-point grading. RESULTS: Twenty-two nodules (11%) showed nondiagnostic results. Regarding specimen color, nondiagnostic rates tended to be higher in grades 1 (75%) and 5 (100%) than in grades 2 (18%), 3 (8%), or 4 (17%), with a significant difference between grade 1 and grade 3 ( P = .003). For specimen volume, nondiagnostic results were significantly more common in grade 1 (33%) and 5 (33%) than in grades 3 (5%) or 4 (1%) ( P<.005). There was a significant negative correlation between the grading of the particle count and the nondiagnostic rate (Spearman ρ = -1.000; P<.001). The sensitivity and specificity in the prediction of nondiagnostic results were 77% and 76%, respectively, at the optimal cutoff value of 2 (grade 2 or lower). CONCLUSION: Particle count was an important morphologic criterion that helped predict nondiagnostic rates in LBC specimens of thyroid nodules, and the specimen color and volume were also useful adjuncts. In routine practice, on-site gross visual assessment followed by resampling (if necessary) may potentially help reduce the rates of nondiagnostic results, repeat FNAs, and the number of unnecessary needle passes. ABBREVIATIONS: FNA = fine-needle aspiration; LBC = liquid-based cytology; ROC = receiver operating characteristic; US = ultrasonography.

Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study.

OBJECTIVES: To prospectively explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the progression of enhancing lesions persisting after standard treatment in patients with surgically resected glioblastoma (GBM). METHODS: Forty-seven GBM patients, who underwent near-total tumorectomy followed by concurrent chemoradiation therapy (CCRT) with temozolomide (TMZ) between May 2014 and February 2016, were enrolled. Twenty-four patients were finally analyzed for measurable enhancing lesions persisting after standard treatment. DCE-MRI parameters were calculated at enhancing lesions. Mann-Whitney U tests and multivariable stepwise logistic regression were used to compare parameters between progression (n = 16) and non-progression (n = 8) groups. RESULTS: Mean Ktrans and ve were significantly lower in progression than in non-progression (P = 0.037 and P = 0.037, respectively). The 5th percentile of the cumulative Ktrans histogram was also significantly lower in the progression than in non-progression group (P = 0.017). Mean ve was the only independent predictor of progression (P = 0.007), with a sensitivity of 100%, specificity of 63%, and an overall accuracy of 88% at a cut-off value of 0.873. CONCLUSIONS: DCE-MRI may help predict the progression of enhancing lesions persisting after the completion of standard treatment in patients with surgically resected GBM, with mean ve serving as an independent predictor of progression. KEY POINTS: • Enhancing lesions may persist after standard treatment in GBM patients. • DCE-MRI may help predict the progression of the enhancing lesions. • Mean K trans and v e were lower in progression than in non-progression group. • DCE-MRI may help identify patients requiring close follow-up after standard treatment. • DCE-MRI may help plan treatment strategies for GBM patients.

Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging.

PURPOSE: The purpose of this study is to improve the qualitative and quantitative image quality of the time-resolved angiography with interleaved stochastic trajectories technique (4D-TWIST-MRA) using deep neural network (DNN)-based MR image reconstruction software. MATERIALS AND METHODS: A total of 520 consecutive patients underwent 4D-TWIST-MRA for ischemic stroke or intracranial vessel stenosis evaluation. Four-dimensional DNN-reconstructed MRA (4D-DNR) was generated using commercially available software (SwiftMR v.3.0.0.0, AIRS Medical, Seoul, Republic of Korea). Among those evaluated, 397 (76.3%) patients received concurrent time-of-flight MRA (TOF-MRA) to compare the signal-to-noise ratio (SNR), image quality, noise, sharpness, vascular conspicuity, and degree of venous contamination with a 5-point Likert scale. Two radiologists independently evaluated the detection rate of intracranial aneurysm in TOF-MRA, 4D-TWIST-MRA, and 4D-DNR in separate sessions. The other 123 (23.7%) patients received 4D-TWIST-MRA due to a suspicion of acute ischemic stroke. The confidence level and decision time for large vessel occlusion were evaluated in these patients. RESULTS: In qualitative analysis, 4D-DNR demonstrated better overall image quality, sharpness, vascular conspicuity, and noise reduction compared to 4D-TWIST-MRA. Moreover, 4D-DNR exhibited a higher SNR than 4D-TWIST-MRA. The venous contamination and aneurysm detection rates were not significantly different between the two MRA images. When compared to TOF-MRA, 4D-CE-MRA underestimated the aneurysm size (2.66 ± 0.51 vs. 1.75 ± 0.62, p = 0.029); however, 4D-DNR showed no significant difference in size compared to TOF-MRA (2.66 ± 0.51 vs. 2.10 ± 0.41, p = 0.327). In the diagnosis of large vessel occlusion, 4D-DNR showed a better confidence level and shorter decision time than 4D-TWIST-MRA. CONCLUSION: DNN reconstruction may improve the qualitative and quantitative image quality of 4D-TWIST-MRA, and also enhance diagnostic performance for intracranial aneurysm and large vessel occlusion.

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.

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.

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.