To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.

Diagnostic accuracy of US in the evaluation of lymph node (LN) metastasis for thyroid cancer patients is limited. We investigated the value of CT added to US for characterizing LNs in preoperative thyroid cancer patients by node-by-node correlation. A total of 225 primary thyroid cancer patients who underwent LN biopsy were included. Based on node-by-node correlation, 274 LNs were classified into probably benign, indeterminate, and suspicious categories on US, CT, and combined US/CT. Malignancy risks were calculated for each category and were compared between US/CT concordant and discordant cases. On US, CT, and combined US/CT, malignancy risks were 1.7%, 8.7%, and 0% in the probably benign category, 22.4%, 5.9%, and 8.0% in the indeterminate category, and 77.2%, 82.0%, and 75.6% in the suspicious category, respectively. Malignancy risk of the concordant suspicious category was higher than that of the discordant suspicious category (84.7% vs. 43.2%, p < 0.001). The addition of CT helped correctly detect additional metastasis in 16.4% of the US indeterminate LNs and in 1.7% of the US probably benign LNs. CT may complement US for LN characterization in thyroid cancer patients by suggesting the diagnostic confidence level for the suspicious category and helping correctly detect metastasis in US indeterminate LNs.

We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.

Cortical venous drainage (CVD) increases the probability of intracranial hemorrhage and mortality rate of dural arteriovenous fistulas (DAVF). Although digital subtraction angiography (DSA) is the most accurate method to determine CVD in DAVFs, this modality has limitations due to its invasive nature and radiation issues. The purpose of this study was to evaluate the diagnostic utility of arterial spin-labeling perfusion-weighted images (ASL-PWI) to identify CVD in patients with DAVF.The Institutional Review Board of our hospital approved this retrospective study. ASL-PWI features of 22 patients with DAVF were retrospectively reviewed for the presence of bright signal intensity in cortical veins and brain parenchyma. DAVF with bright signal intensity in cortical veins and/or brain parenchyma was regarded as having CVD. Using DSA as a reference standard, sensitivity, specificity, positive predictive value, and negative predictive value of ASL-PWI for detecting CVD were calculated.Based on DSA features, 11 (11/22, 50%) patients were classified as having "aggressive" pattern with CVD. Eleven (11/22, 50%) patients also showed bright signal intensity in cortical veins (9/22, 41%) and/or brain parenchyma (4/22, 18%) on ASL-PWI. The 11 patients who had "Aggressive" pattern on DSA were the same 11 patients who were classified as having "aggressive" pattern on ASL-PWI. ASL-PWI showed perfect diagnostic performance for identifying CVD with sensitivity, specificity, positive predictive value, and negative predictive value of 100% for all.Thus, ASL-PWI could be used as a noninvasive mean to predict the presence of CVD in patients with DAVFs. It has the potential as a screening tool to evaluate DAVF prior to invasive DSA.

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

This study evaluated thyroid cancer risk in a lung cancer screening population according to the presence of an incidental thyroid nodule (ITN) detected on low-dose chest computed tomography (LDCT).

This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR).

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

To validate the performances of two prediction models (Brock and Lee models) for the differentiation of minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions among subsolid nodules (SSNs).

To evaluate the diagnostic performance of cerebral blood flow (CBF) by using arterial spin labeling (ASL) perfusion magnetic resonance (MR) imaging to differentiate glioblastoma (GBM) from brain metastasis.

This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance.

The purpose of this study was to investigate the time-dependent effects of contrast medium on multi-dynamic, multi-echo (MDME) sequence in patients with brain metastases.

The purpose of this study was to present the distribution of lesions among the six categories of the pathology reporting system for thyroid core-needle biopsy (CNB), along with the range of malignancy risk of each category based on different diagnostic criteria for benignity in a clinical cohort.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.

Arterial spin-labeling (ASL) was recently introduced as a noninvasive method to evaluate cerebral hemodynamics. The purposes of this study were to assess the ability of ASL imaging to detect crossed cerebellar diaschisis (CCD) in patients with their first unilateral supratentorial hyperacute stroke and to identify imaging or clinical factors significantly associated with CCD.

Cerebral venous thrombosis is a potentially lethal disease. Early diagnosis is essential to improve its prognosis. However, its early diagnosis based on conventional imaging modalities remains a challenge in clinical settings. The purpose of this study was to evaluate whether bright sinus appearance on arterial spin-labeling perfusion-weighted image (ASL-PWI) could help identify cerebral venous thrombosis.ASL-PWI of 13 patients who were confirmed as cerebral venous thrombosis based on neurologic symptoms and computed tomography (CT) or magnetic resonance (MR) venography (with/without cerebral angiography) were retrospectively analyzed for the presence or absence of the following: bright signal in dural sinus termed "bright sinus appearance"; and hypoperfusion in brain parenchyma drained by thrombosed sinus. In addition, conventional MR findings, including susceptibility vessel sign, empty delta sign, and atypical distribution against arterial territory, were also analyzed.Bright sinus appearance on ASL-PWI was found in all (100%) 13 patients. In addition, 10 (77%) patients showed hypoperfusion in the brain parenchyma drained by thrombosed sinus on ASL-PWI. Susceptibility vessel sign and empty delta sign were revealed in 11 (85%) and 7 (54%) patients, respectively. Atypical distribution against arterial territory was seen in 5 (50%) of the 10 patients with parenchymal abnormality on conventional MR sequences. Therefore, the bright sinus appearance had higher sensitivities for identifying cerebral venous thrombosis than the susceptibility vessel sign, empty delta sign, and atypical distribution against arterial territory (with differences of 15%; P = .500, 46%; P = .031, and 50%; P = .031, respectively).Bright sinus appearance on ASL-PWI can provide important diagnostic clue for identifying cerebral venous thrombosis. Therefore, this technique may have the potential to be used as a noninvasive diagnostic tool to identify the cerebral venous thrombosis.

Incidental thyroid nodules are commonly detected on ultrasonography (US). This has contributed to the rapidly rising incidence of low-risk papillary thyroid carcinoma over the last 20 years. The appropriate diagnosis and management of these patients is based on the risk factors related to the patients as well as the thyroid nodules. The Korean Society of Thyroid Radiology (KSThR) published consensus recommendations for US-based management of thyroid nodules in 2011 and revised them in 2016. These guidelines have been used as the standard guidelines in Korea. However, recent advances in the diagnosis and management of thyroid nodules have necessitated the revision of the original recommendations. The task force of the KSThR has revised the Korean Thyroid Imaging Reporting and Data System and recommendations for US lexicon, biopsy criteria, US criteria of extrathyroidal extension, optimal thyroid computed tomography protocol, and US follow-up of thyroid nodules before and after biopsy. The biopsy criteria were revised to reduce unnecessary biopsies for benign nodules while maintaining an appropriate sensitivity for the detection of malignant tumors in small (1-2 cm) thyroid nodules. The goal of these recommendations is to provide the optimal scientific evidence and expert opinion consensus regarding US-based diagnosis and management of thyroid nodules.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

This study aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in patients with suspected seizures in acute settings. A total of 164 patients who underwent ASL-PWI for suspected seizures in acute settings (with final diagnoses of seizure [n = 129], poststroke seizure [n = 18], and seizure mimickers [n = 17]), were included in this retrospective study. Perfusion abnormality was analyzed for: (1) pattern, (2) multifocality, and (3) atypical distribution against vascular territories. Perfusion abnormality was detected in 39% (50/129) of the seizure patients, most (94%, 47/50) being the hyperperfusion pattern. Of the patients with perfusion abnormality, multifocality or hemispheric involvement and atypical distribution against vascular territory were revealed in 46% (23/50) and 98% (49/50), respectively. In addition, seizures showed characteristic features including hyperperfusion (with or without non-territorial distribution) on ASL-PWI, thus differentiating them from poststroke seizures or seizure mimickers. In patients in whom seizure focus could be localized on both EEG and ASL-PWI, the concordance rate was 77%. The present study demonstrates that ASL-PWI can provide information regarding cerebral perfusion status in patients with seizures in acute settings and has the potential to be used as a non-invasive imaging tool to identify the cerebral perfusion in patients with seizures.

This study evaluated the possibility of accelerated gadolinium accumulation in irradiated brain parenchyma where the blood-brain barrier was weakened.