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To evaluate the association between thyroid echogenicity and heterogeneity seen on ultrasonography (US) and thyroid function in pediatric and adolescent populations with autoimmune diffuse thyroid diseases (AITD). From 2000 to 2020, we reviewed thyroid ultrasound (US) images and thyroid function statuses in 133 children and adolescent AITD patients. Our review of the images focused on decreased echogenicity and heterogeneity, which were classified into four grades. Among patients with overt hypothyroidism or overt hyperthyroidism, 94.2% (65/69) showed a US grade of 3 or 4. In patients with subclinical hyper/hypothyroidism or euthyroidism, 45.3% (29/64) showed grades 1 or 2. There were no overt hyper/hypothyroidism patients with US grade 1. When we compared US grades according to thyroid status, more severe thyroid dysfunction was significantly associated with higher US grade (p = 0.047). Thyroid stimulating hormone (TSH) level differed significantly according to US grades when we evaluated hyperthyroid (p = 0.035) and hypothyroid (p = 0.027) states independently. 11 patients showed both US grade and thyroid function status changes on follow-up US. In children and adolescent AITD patients, there was an association between decreased echogenicity and heterogeneity on US and thyroid dysfunction.

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

Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).

Hydroxychloroquine has recently received attention as a treatment for COVID-19. However, it may prolong the QTc interval. Furthermore, when hydroxychloroquine is administered concomitantly with other drugs, it can exacerbate the risk of QT prolongation. Nevertheless, the risk of QT prolongation due to drug-drug interactions (DDIs) between hydroxychloroquine and concomitant medications has not yet been identified. To evaluate the risk of QT prolongation due to DDIs between hydroxychloroquine and 118 concurrent drugs frequently used in real-world practice, we analyzed the electrocardiogram results obtained for 447,632 patients and their relevant electronic health records in a tertiary teaching hospital in Korea from 1996 to 2018. We repeated the case–control analysis for each drug. In each analysis, we performed multiple logistic regression and calculated the odds ratio (OR) for each target drug, hydroxychloroquine, and the interaction terms between those two drugs. The DDIs were observed in 12 drugs (trimebutine, tacrolimus, tramadol, rosuvastatin, cyclosporin, sulfasalazine, rofecoxib, diltiazem, piperacillin/tazobactam, isoniazid, clarithromycin, and furosemide), all with a p value of

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.

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 P

The second-in-class proteasome inhibitor (PI) carfilzomib (Kyprolis, Cfz) has contributed to a substantial advancement in multiple myeloma treatment by improving patient survival and quality of life. A considerable portion of patients however display intrinsic resistance to Cfz. Our mechanistic understanding of intrinsic Cfz resistance is limited due to a lack of suitable cell-based models. We report that H727 human bronchial carcinoid cells are inherently resistant to Cfz, yet susceptible to other PIs and inhibitors targeting upstream components of the ubiquitin-proteasome system (UPS). These results indicate that H727 cells remain dependent on the UPS for cell survival and growth despite harboring intrinsic resistance to Cfz. Alterations in the composition of proteasome catalytic subunits via interferon-γ treatment or siRNA knockdown results in sensitization of H727 cells to Cfz. We postulate that a potential link may exist between the composition of proteasome catalytic subunits and the cellular response to Cfz. Overall, H727 cells may serve as a useful cell-based model for de novo Cfz resistance and our results suggest previously unexplored mechanisms of de novo PI resistance.

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

The aim of this study was to evaluate prognostic factors including surgical, radiographic, and histopathologic analyses in anaplastic oligodendroglioma (AO) patients. We reviewed the electronic records of 95 patients who underwent surgery and were diagnosed with AO for 20 years. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Univariate and multivariable analyses included clinical, histopathological, and radiographic prognostic factors. Subgroup analysis was performed in isocitrate dehydrogenase (IDH1/2)-mutant and 1p/19q-codeleted patients. The median PFS and OS were 24.7 months and 50.8 months, respectively. The 1-, 3-, 5-, and 10-year PFS were 75.8%, 42.9%, 32.4%, and 16.4%, respectively. Furthermore, the 1-, 3-, 5-, and 10-year OS were 98.9%, 76.9%, 42.9%, and 29.7%, respectively. The median PFS and OS of the IDH1/2-mutant and 1p/19q-codeleted patients were 54.2 and 57.8 months, respectively. In univariate analyses, young age, frontal lobe, weak enhancement, gross total resection (GTR), low Ki-67 index, 1p/19q codeletion, and IDH1/2 mutations were associated with a favorable outcome. In multivariable analyses, IDH1/2 mutation was related to better PFS and OS. In subgroup analysis, GTR was associated with favorable outcomes.

We aimed to establish a high-performing and robust classification strategy, using magnetic resonance imaging (MRI), along with combinations of feature extraction and selection in human and machine learning using radiomics or deep features by employing a small dataset. Using diffusion and contrast-enhanced T1-weighted MR images obtained from patients with glioblastomas and primary central nervous system lymphomas, classification task was assigned to a combination of radiomic features and (1) supervised machine learning after feature selection or (2) multilayer perceptron (MLP) network; or MR image input without radiomic feature extraction to (3) two neuro-radiologists or (4) an end-to-end convolutional neural network (CNN). The results showed similar high performance in generalized linear model (GLM) classifier and MLP using radiomics features in the internal validation set, but MLP network remained robust in the external validation set obtained using different MRI protocols. CNN showed the lowest performance in both validation sets. Our results reveal that a combination of radiomic features and MLP network classifier serves a high-performing and generalizable model for classification task for a small dataset with heterogeneous MRI protocols.

The epidermal growth factor receptor (EGFR) inhibitors such as erlotinib and gefitinib are widely used for treatment of non-small cell lung cancer (NSCLC), but they have shown limited efficacy in an unselected population of patients. The KRAS mutations, which are identified in approximately 20% of NSCLC patients, have shown to be associated with the resistance to the EGFR tyrosine kinase inhibitors (TKIs). Currently, there is no clinically available targeted therapy which can effectively inhibit NSCLC tumors harboring KRAS mutations. This study aims to show the effectiveness of KYA1797K, a small molecule which revealed anti-cancer effect in colorectal cancer by destabilizing Ras via inhibiting the Wnt/β-catenin pathway, for the treatment of KRAS-mutated NSCLC. While erlotinib fail to have anti-transforming effect in NSCLC cell lines harboring KRAS mutations, KYA1797K effectively inhibited the Ras-ERK pathway in KRAS-mutant NSCLC cell lines. As a result, KYA1797K treatment suppressed the growth and transformation of KRAS mutant NSCLC cells and also induced apoptosis. Furthermore, KYA1797K effectively inhibited Kras-driven tumorigenesis in the KrasLA2 mouse model by suppressing the Ras-ERK pathway. The destabilization of Ras via inhibition of the Wnt/β-catenin pathway is a potential therapeutic strategy for KRAS-mutated NSCLC that is resistant to EGFR TKI.

We estimated the incidence of ectopic pregnancy (EP) and the success rate of expectant management of EP in South Korea. We analyzed data from 2009 to 2015 using the Health Insurance Review and Assessment Service National Inpatient Sample (HIRA-NIS) database. EP was identified by diagnostic codes, and strict EP was identified by both diagnostic codes and treatment codes. From 2009 to 2015, 369,701 cases of EP, abortion, or delivery were extracted from a total of 4,476,495 women. Of the total pregnancies, 8,556 cases were EPs. The incidence of EP was 34.1 ± 0.7 per 1,000 pregnancies and the incidence of strict EP was 17.3 ± 0.3 per 1,000 pregnancies. Among women aged 25–44 years, age was associated with a higher incidence of EP (odds ratio [OR]: 1.13; 95% confidence interval [CI]: 1.06, 1.19; P P = 0.51) did not significantly differ by year. The incidence of EP in Korea was 17.3 ± 0.3 per 1,000 pregnancies, and almost did not change over 7 years. About 50% of EPs were treated without surgery or methotrexate. This study provides an important reference for the treatment of EP.

Although numerous reports on nonprecious metal catalysts for replacing expensive Pt-based catalysts have been published, few of these studies have demonstrated their practical application in fuel cells. In this work, we report graphitic carbon nitride and carbon nanofiber hybrid materials synthesized by a facile and gram-scale method via liquid-based reactions, without the use of toxic materials or a high pressure-high temperature reactor, for use as fuel cell cathodes. The resulting materials exhibited remarkable methanol tolerance, selectivity and stability even without a metal dopant. Furthermore, these completely metal-free catalysts exhibited outstanding performance as cathode materials in an actual fuel cell device: a membrane electrode assembly with both acidic and alkaline polymer electrolytes. The fabrication method and remarkable performance of the single cell produced in this study represent progressive steps toward the realistic application of metal-free cathode electrocatalysts in fuel cells.