Detection rate of contrast-enhanced brain magnetic resonance imaging in patients with cognitive impairment.

INTRODUCTION: The number of brain MRI with contrast media performed in patients with cognitive impairment has increased without universal agreement. We aimed to evaluate the detection rate of contrast-enhanced brain MRI in patients with cognitive impairment. MATERIALS AND METHODS: This single-institution, retrospective study included 4,838 patients who attended outpatient clinics for cognitive impairment evaluation and underwent brain MRI with or without contrast enhancement from December 2015 to February 2020. Patients who tested positive for cognitive impairment were followed-up to confirm whether the result was true-positive and provide follow-up management. Detection rate was defined as the proportion of patients with true-positive results and was compared between groups with and without contrast enhancement. Individual matching in a 1:2 ratio according to age, sex, and year of test was performed. RESULTS: The overall detection rates of brain MRI with and without contrast media were 4.7% (57/1,203; 95% CI: 3.6%-6.1%) and 1.8% (65/3,635; 95% CI: 1.4%-2.3%), respectively (P<0.001); individual matching demonstrated similar results (4.7% and 1.9%). Among 1,203 patients with contrast media, 3.6% was only detectable with the aid of contrast media. The proportion of patients who underwent follow-up imaging or treatment for the detected lesions were significantly higher in the group with contrast media (2.0% and 0.6%, P < .001). CONCLUSIONS: Detection rate of brain MRI for lesions only detectable with contrast media in patients with cognitive impairment was not high enough and further study is needed to identify whom would truly benefit with contrast media.

Diagnostic Performance of Ultrasound-Based Risk Stratification Systems for Thyroid Nodules: A Systematic Review and Meta-Analysis.

BACKGRUOUND: This study investigated the diagnostic performance of biopsy criteria in four society ultrasonography risk stratification systems (RSSs) for thyroid nodules, including the 2021 Korean (K)-Thyroid Imaging Reporting and Data System (TIRADS). METHODS: The Ovid-MEDLINE, Embase, Cochrane, and KoreaMed databases were searched and a manual search was conducted to identify original articles investigating the diagnostic performance of biopsy criteria for thyroid nodules (≥1 cm) in four widely used society RSSs. RESULTS: Eleven articles were included. The pooled sensitivity and specificity were 82% (95% confidence interval [CI], 74% to 87%) and 60% (95% CI, 52% to 67%) for the American College of Radiology (ACR)-TIRADS, 89% (95% CI, 85% to 93%) and 34% (95% CI, 26% to 42%) for the American Thyroid Association (ATA) system, 88% (95% CI, 81% to 92%) and 42% (95% CI, 22% to 67%) for the European (EU)-TIRADS, and 96% (95% CI, 94% to 97%) and 21% (95% CI, 17% to 25%) for the 2016 K-TIRADS. The sensitivity and specificity were 76% (95% CI, 74% to 79%) and 50% (95% CI, 49% to 52%) for the 2021 K-TIRADS1.5 (1.5-cm size cut-off for intermediate-suspicion nodules). The pooled unnecessary biopsy rates of the ACR-TIRADS, ATA system, EU-TIRADS, and 2016 K-TIRADS were 41% (95% CI, 32% to 49%), 65% (95% CI, 56% to 74%), 68% (95% CI, 60% to 75%), and 79% (95% CI, 74% to 83%), respectively. The unnecessary biopsy rate was 50% (95% CI, 47% to 53%) for the 2021 K-TIRADS1.5. CONCLUSION: The unnecessary biopsy rate of the 2021 K-TIRADS1.5 was substantially lower than that of the 2016 K-TIRADS and comparable to that of the ACR-TIRADS. The 2021 K-TIRADS may help reduce potential harm due to unnecessary biopsies.

Standardized Imaging and Reporting for Thyroid Ultrasound: Korean Society of Thyroid Radiology Consensus Statement and Recommendation.

Ultrasonography (US) is a primary imaging modality for diagnosing nodular thyroid disease and has an essential role in identifying the most appropriate management strategy for patients with nodular thyroid disease. Standardized imaging techniques and reporting formats for thyroid US are necessary. For this purpose, the Korean Society of Thyroid Radiology (KSThR) organized a task force in June 2021 and developed recommendations for standardized imaging technique and reporting format, based on the 2021 KSThR consensus statement and recommendations for US-based diagnosis and management of thyroid nodules. The goal was to achieve an expert consensus applicable to clinical practice.

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. METHODS: This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). RESULTS: Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. CONCLUSION: Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma.

OBJECTIVE: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. CONCLUSION: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

Imaging finding and analysis of brain lymphoma in contrast-enhanced fluid attenuated inversion recovery sequence.

OBJECTIVE: The purpose of this retrospective study was to report and analyze the image findings of contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) sequence of lymphoma in the brain. MATERIAL AND METHODS: Thirty-two immunocompetent patients with biopsy-proven diffuse large B-cell type lymphoma in the brain were evaluated with pre-treatment MRI examinations from August 2014 to April 2020. As stereotactic studies on the day of biopsy, FLAIR and T1-weighted axial images were acquired in 2 mm thickness, before and after administrating gadolinium-based contrast agents, with 3.0 Tesla MR machines. Respective subtraction images were also obtained for both CE-FLAIR and contrast-enhanced T1-wieghted image (CE-T1WI) sequences. The imaging findings, especially the enhancement pattern on CE-FLAIR sequence, were analyzed qualitatively and quantitatively, using semi-automatic segmentation. RESULTS: On CE-FLAIR images, brain lymphomas were poorly enhanced, while showing peripheral rim enhancement (54 of 58 lesions, 93.1 %) and central enhancing foci (40 of 58 lesions, 69.0 %). Seventy percent of central enhancing foci were correlated to areas with low signal intensity on CE-T1WI. In quantitative analysis, the mean signal intensity of CE-T1WI subtraction was 490.44 and that of FLAIR subtraction was 206.13. The standard deviation of all signal intensity values in CE-T1WI subtraction sequence was 143.45, while that of CE-FLAIR subtraction sequence was 118.41. CONCLUSION: On CE-FLAIR, brain lymphomas showed relatively poor and homogeneous enhancement, when compared to CE-T1WI. Most brain lymphomas displayed peripheral rim enhancement and central enhancing foci.

Impact of Brain MRI on the Diagnosis of Infective Endocarditis and Treatment Decisions: Systematic Review and Meta-Analysis.

BACKGROUND. Despite increasing use of brain MRI to evaluate patients with suspected infective endocarditis, the clinical impact of brain MRI in this setting has not yet been systematically reviewed. OBJECTIVE. The purpose of this study was to evaluate the frequency of brain MRI findings in patients with suspected or confirmed infective endocarditis and to determine the impact of such findings on clinical decisions. EVIDENCE ACQUISITION. A systematic search of the PubMed, Embase, and Cochrane databases was performed from January 1, 1990, to December 31, 2020, to identify original research investigations of brain MRI in patients with suspected or confirmed infective endocarditis. Study quality was assessed with QUADAS-2. Study endpoints included the frequency of brain MRI findings and the frequency of diagnostic modifications, modification of therapeutic plan, and modification of valve surgery plan resulting from MRI findings. Frequencies were pooled by means of the inverse variance method. Subgroup analysis was performed. EVIDENCE SYNTHESIS. A total of 21 studies with 2133 patients were included. Overall study quality was considered moderate. In terms of brain MRI findings, the pooled frequency of acute ischemic lesions was 61.9% (95% CI, 50.7-71.9%); of cerebral microbleeds, 52.9% (95% CI, 41.6-63.9%); hemorrhagic lesions, 24.7% (95% CI, 15.1-37.9%); abscess or meningitis, 9.5% (95% CI, 5.6-15.6%); and intracranial mycotic aneurysm, 6.2% (95% CI, 4.0-9.4%). Subgroup analysis after exclusion of three studies in which neurologic findings were the indication for all brain MRI examinations yielded similar frequencies of these findings. Six studies included results on the impact of brain MRI findings on clinical decisions. The frequencies of diagnostic modifications in two studies were 5.4% and 32.1%. The pooled frequency of therapeutic plan modification in six studies was 12.8% (95% CI, 6.5-23.7%) and of surgical plan modification in five studies was 14.2% (95% CI, 8.2-23.4%). CONCLUSION. In patients with suspected or confirmed infective endocarditis, brain MRI examinations commonly show relevant abnormalities that affect diagnostic and therapeutic clinical decisions. CLINICAL IMPACT. The findings support a potential role for screening brain MRI in the evaluation of patients with suspected or confirmed infective endocarditis, regardless of the presence or absence of neurologic symptoms.

Comparison of Core Needle Biopsy and Repeat Fine-Needle Aspiration in Avoiding Diagnostic Surgery for Thyroid Nodules Initially Diagnosed as Atypia/Follicular Lesion of Undetermined Significance.

OBJECTIVE: To compare core needle biopsy (CNB) and repeat fine-needle aspiration (rFNA) to reduce the rate of diagnostic surgery and prevent unnecessary surgery in nodules initially diagnosed as atypia/follicular lesions of undetermined significance (AUS/FLUS). MATERIALS AND METHODS: This study included 231 consecutive patients (150 female and 81 male; mean age ± standard deviation, 51.9 ± 11.7 years) with 235 thyroid nodules (≥ 1 cm) initially diagnosed as AUS/FLUS, who later underwent both rFNA and CNB. The nodules that required diagnostic surgery after the biopsy were defined using three different scenarios according to the rFNA and CNB results: criterion 1, surgery for low-risk indeterminate (categories I and III); criterion 2, surgery for high-risk indeterminate (categories IV and V); and criterion 3, surgery for all indeterminate nodules (categories I, III, IV, and V). We compared the expected rates of diagnostic surgery between CNB and rFNA in all 235 nodules using the three surgical criteria. In addition, the expected rates of unnecessary surgery (i.e., surgery for benign pathology) were compared in a subgroup of 182 nodules with available final diagnoses. RESULTS: CNB showed significantly lower rates of nondiagnostic, AUS/FLUS, and suspicious for malignancy diagnoses (p ≤ 0.016) and higher rates of follicular neoplasm or suspicious for a follicular neoplasm (p < 0.001) and malignant diagnoses (p = 0.031). CNB showed a significantly lower expected rate of diagnostic surgery than rFNA for criterion 1 (29.8% vs. 48.1%, p < 0.001) and criterion 3 (46.4% vs. 55.3%, p = 0.029), and a significantly higher rate for criterion 2 (16.6% vs. 7.2%, p = 0.001). CNB showed a significantly lower expected rate of unnecessary surgery than rFNA for criterion 1 (18.7% vs. 29.7%, p = 0.024). CONCLUSION: CNB was superior to rFNA in reducing the rates of potential diagnostic surgery and unnecessary surgery for nodules initially diagnosed as AUS/FLUS in a scenario where nodules with low-risk indeterminate results (categories I and III) would undergo surgery.

Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients.

From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p < .002). Radiomics features in fast scanned T2-FLAIR showed variable stabilities according to the sequences compared with conventional T2-FLAIR. Therefore, radiomics features may be used cautiously in applications for feature analysis as their stability and robustness can be variable.

Prediction model for cervical lymph node metastasis in human papillomavirus-related oropharyngeal squamous cell carcinomas.

OBJECTIVES: To develop and validate a risk scoring system based on clinical and imaging findings to predict lymph node metastasis from HPV-related oropharyngeal squamous cell carcinomas. METHODS: This study population who had undergone neck dissections or lymph node biopsies in patients with HPV+ OPSCC was obtained from a historical cohort from two tertiary referral hospitals. The training set from one hospital included 455 lymph nodes from 82 patients, and the test set from the other hospital included 150 lymph nodes from 42 patients. The baseline clinical and imaging findings on pretreatment CT or MR were investigated and the reference standards were the histopathologic results. A risk scoring system was constructed based on logistic regression and validated both internally and externally. RESULTS: A 7-point risk scoring system was developed based on the following variables: central necrosis, infiltration of adjacent planes, lymph node level, and the maximal axial diameter of the lymph node. This risk scoring system showed good discriminative ability for metastasis in the training set (C-statistic 0.952; 95% CI, 0.931-0.972) and test set (C-statistic 0.968, 95% CI, 0.936-0.999) and good calibration ability in the training set (p = 0.723) and test set (p = 0.253). CONCLUSIONS: We developed and validated a reliable risk scoring system that predicts lymph node metastasis from HPV+ OPSCCs based on the clinical data and pretreatment imaging findings. We expect this risk scoring system to be a useful guide for better decision-making in practice. KEY POINTS: • It is important to diagnose lymph node metastasis from HPV+ OPSCC for treatment planning; however, there has been little research on that. • We developed and externally validated a new scoring system for stratifying the risk of lymph node metastasis from HPV+ OPSCC based on clinical and imaging data. • A predictive model combining both clinical and imaging data showed high diagnostic accuracy and efficiency for lymph node metastasis from HPV+ OPSCC.

Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation.

BACKGROUND: Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma. METHODS: Preoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier-based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019. RESULTS: Volume of peritumoral edema was an independent predictor of brain invasion (P < 0.001). The top 6 interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97; 95% CI: 0.95-0.98) and validation sets (AUC 0.91; 95% CI: 0.84-0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76; 95% CI: 0.66-0.86). CONCLUSION: An imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.