Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Background: Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

The Comparison of Efficacy and Safety between Radiofrequency Ablation Alone and Ethanol Ablation Followed by Radiofrequency Ablation in the Treatment of Mixed Cystic and Solid Thyroid Nodule.

Purpose: To compare the efficacy and safety of radiofrequency ablation (RFA) and ethanol ablation (EA) followed by RFA in treating mixed cystic and solid thyroid nodules. Materials and Methods: We included 243 nodules from 243 patients who underwent RFA for mixed cystic and solid benign nodules. The nodules were divided into two groups (RFA alone and EA + RFA). We evaluated volume reduction rate (VRR), therapeutic success rate, improvement in symptomatic and cosmetic issues, complications, and adverse effects. Results: The RFA group included 204 patients, and the EA + RFA group included 39 patients. The long-term success rates in the RFA only and EA + RFA groups were 90.2% and 97.4%, respectively. The mean VRR at the last follow-up in the RFA and EA + RFA groups were 81.6% and 87.2%, respectively. Therapeutic results were similar in both groups at the last follow-up. Cosmetic and symptomatic problems markedly improved in both groups. No major complications were observed. Conclusion: Both RFA alone and EA + RA are safe and effective methods for treating mixed cystic and solid thyroid nodules, although EA + RFA is slightly more effective.

Cerebrovascular Malformation Mimicking Recurrent Lymphoma on Dual Time-Point 18F-FDOPA PET.

ABSTRACT: Although 18F-FDG is the dominant radiotracer for PET imaging of hematological malignancies, radiolabeled amino acids have also been investigated to improve image quality in areas of high 18F-FDG uptake such as the central nervous system. We present a case of a 57-year-old woman who underwent an 18F-FDOPA scan for primary CNS lymphoma, which demonstrated an unexpected false-positive uptake in the right frontal lobe, due to a developmental venous anomaly.

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

Diagnostic Performance Using a Combination of MRI Findings for Evaluating Cognitive Decline.

Purpose: We investigated potentially promising imaging findings and their combinations in the evaluation of cognitive decline. Materials and Methods: This retrospective study included 138 patients with subjective cognitive impairments, who underwent brain MRI. We classified the same group of patients into Alzheimer's disease (AD) and non-AD groups, based on the neuropsychiatric evaluation. We analyzed imaging findings, including white matter hyperintensity (WMH) and cerebral microbleeds (CMBs), using the Kruskal-Wallis test for group comparison, and receiver operating characteristic (ROC) curve analysis for assessing the diagnostic performance of imaging findings. Results: CMBs in the lobar or deep locations demonstrated higher prevalence in the patients with AD compared to those in the non-AD group. The presence of lobar CMBs combined with periventricular WMH (area under the ROC curve [AUC] = 0.702 [95% confidence interval: 0.599-0.806], p < 0.001) showed the highest performance in differentiation of AD from non-AD group. Conclusion: Combinations of imaging findings can serve as useful additive diagnostic tools in the assessment of cognitive decline.

Comparison of the Therapeutic Efficacy and Technical Outcomes between Conventional Fixed Electrodes and Adjustable Electrodes in the Radiofrequency Ablation of Benign Thyroid Nodules.

OBJECTIVE: This study aimed to compare therapeutic efficacy and technical outcomes between adjustable electrode (AE) and conventional fixed electrode (FE) for radiofrequency ablation (RFA) of benign thyroid nodules. MATERIALS AND METHODS: Between 2013 and 2021, RFA was performed on histologically proven benign thyroid nodules. For the AE method, AE length ≥ 1 cm with higher power and < 1 cm with lower power were utilized for ablating feeding vessels and nodules, especially those near anatomical structures, respectively. The therapeutic efficacy (volume reduction rate [VRR], complication rate, and regrowth rate) and technical outcomes (total energy delivery, ablated volume/energy, RFA time, and ablated volume/time) of FE and AE were compared. Continuous parameters were compared using a two-sample t-test or Mann-Whitney U test, and categorical parameters were compared using a chi-squared test or Fisher's exact test. RESULTS: A total of 182 nodules (FE: 92 vs. AE: 90) in 173 patients (mean age ± standard deviation, 47.0 ± 14.7 years; female, 90.8% [157/173]; median follow-up, 726 days [interquartile range, 441-1075 days]) were analyzed. The therapeutic efficacy was comparable, whereas technical outcomes were more favorable for AE. Both electrodes demonstrated comparable overall median VRR (FE: 92.4% vs. AE: 84.9%, P = 0.240) without immediate major complications. Overall regrowth rates were comparable between the two groups (FE: 2.2% [2/90] vs. AE: 1.1% [1/90], P > 0.99). AE demonstrated a shorter median RFA time (FE: 811 vs. AE: 627 seconds, P = 0.009). Both delivered comparable median energy (FE: 42.8 vs. AE: 29.2 kJ, P = 0.069), but AE demonstrated higher median ablated volume/energy and median ablated volume/time (FE: 0.2 vs. AE: 0.3 cc/kJ, P < 0.001; and FE: 0.7 vs. AE: 1.0 cc/min, P < 0.001, respectively). CONCLUSION: Therapeutic efficacy between FE and AE was comparable. AE demonstrated better technical outcomes than FE in terms of RFA time, ablated volume/energy, and ablated volume/time.

A highly efficient strategy for simultaneous analysis of eleven monomeric hydroxylated-stilbenes in grape wines and juices by a salting-out assisted liquid-liquid (SALLE) extraction combined with a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS).

Wine and grape juice are known to have hydroxylated stilbenes, a group of phytoalexins, health-promoting compounds. The determination of stilbene species in wine and grape juices remains a challenging task. Here, we propose an efficient strategy for the simultaneous determination of eleven hydroxylated stilbenes in grape wines and grape juice by a SALLE in conjunction with an HPLC-ESI-MS/MS. SALLE and HPLC-MS/MS conditions were optimized. The proposed method was validated and found to be a simple, sensitive, and reliable measure for the determination of the stilbene species in wine and grape juice. The SALLE was fast and efficient, taking only 10 min. The HPLC-MS/MS was able to quickly separate and quantitate the eleven stilbenes (9 min running). This method was successfully applied to determine the contents of stilbene species in commercial wine and grape juice in Korea. This method is advantageous in reduced sample preparation time, low labor, and cost efficiency.

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.

Correlation between radiologic depth of invasion and pathologic depth of invasion in oral cavity squamous cell carcinoma: A systematic review and meta-analysis.

OBJECTIVES: To comprehensively assess the correlation between radiologic depth of invasion (rDOI) and pathologic depth of invasion (pDOI) in oral cavity squamous cell carcinoma (OSCC) by meta-analysis. MATERIALS AND METHODS: PubMed and Embase databases were searched to find pertinent articles reporting rDOI of OSCC. Studies evaluating the correlations and mean differences (MDs) between rDOI and pDOI were included. The rDOI was measured based on ultrasound (US), computed tomography (CT), or magnetic resonance imaging (MRI). The correlation coefficients and MDs between rDOI and pDOI were meta-analytically pooled. Between-study heterogeneity was assessed using Higgins' inconsistency index (I2). Subgroup analysis was performed based on imaging modality. RESULTS: Twenty-three studies with 1787 patients were included. The pooled correlation coefficient and MD were 0.86 (95 % confidence interval [CI], 0.82-0.90; I2 = 66.9 %) and 1.84 mm (95 % CI, 1.02-2.65 mm; I2 = 88.2 %), respectively. In subgroup analysis, MRI showed the largest MD (n = 12, 2.61 mm), followed by US (n = 2, -0.41 mm) and CT (n = 2, 0.12 mm). US showed the highest correlation coefficient (n = 3, 0.91), followed by MRI (n = 12, 0.85) and CT (n = 3, 0.82). CONCLUSION: rDOI measured by US, CT, and MRI demonstrated excellent correlations with pDOI.

Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.

This study aimed to assess the performance of deep learning (DL) algorithms in the diagnosis of nasal bone fractures on radiographs and compare it with that of experienced radiologists. In this retrospective study, 6713 patients whose nasal radiographs were examined for suspected nasal bone fractures between January 2009 and October 2020 were assessed. Our dataset was randomly split into training (n = 4325), validation (n = 481), and internal test (n = 1250) sets; a separate external dataset (n = 102) was used. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the DL algorithm and the two radiologists were compared. The AUCs of the DL algorithm for the internal and external test sets were 0.85 (95% CI, 0.83-0.86) and 0.86 (95% CI, 0.78-0.93), respectively, and those of the two radiologists for the external test set were 0.80 (95% CI, 0.73-0.87) and 0.75 (95% CI, 0.68-0.82). The DL algorithm therefore significantly exceeded radiologist 2 (P = 0.021) but did not significantly differ from radiologist 1 (P = 0.142). The sensitivity and specificity of the DL algorithm were 83.1% (95% CI, 71.2-93.2%) and 83.7% (95% CI, 69.8-93.0%), respectively. Our DL algorithm performs comparably to experienced radiologists in diagnosing nasal bone fractures on radiographs.

MRI-Based Demonstration of the Normal Glymphatic System in a Human Population: A Systematic Review.

Background: The glymphatic system has been described as one that facilitates the exchange between the cerebrospinal fluid (CSF) and interstitial fluid, and many recent studies have demonstrated glymphatic flow based on magnetic resonance imaging (MRI). We aim to systematically review the studies demonstrating a normal glymphatic flow in a human population using MRI and to propose a detailed glymphatic imaging protocol. Methods: We searched the MEDLINE and EMBASE databases to identify studies with human participants involving MRI-based demonstrations of the normal glymphatic flow. We extracted data on the imaging sequence, imaging protocol, and the targeted anatomical structures on each study. Results: According to contrast-enhanced MRI studies, peak enhancement was sequentially detected first in the CSF space, followed by the brain parenchyma, the meningeal lymphatic vessel (MLV), and, finally, the cervical lymph nodes, corresponding with glymphatic flow and explaining the drainage into the MLV. Non-contrast flow-sensitive MRI studies revealed similar glymphatic inflow from the CSF space to the brain parenchyma and efflux of exchanged fluid from the brain parenchyma to the MLV. Conclusion: We may recommend T1-weighted contrast-enhanced MRI for visualizing glymphatic flow. Our result can increase understanding of the glymphatic system and may lay the groundwork for establishing central nervous system fluid dynamic theories and developing standardized imaging protocols.

Deep convolutional neural network for classification of thyroid nodules on ultrasound: Comparison of the diagnostic performance with that of radiologists.

PURPOSE: This study aimed to train and validate deep learning (DL) models for differentiating malignant from benign thyroid nodules on US images and compare their performance with that of radiologists. METHODS: Images of thyroid nodules in patients who underwent US-guided fine-needle aspiration biopsy at our institution between January 2010 and March 2020 were retrospectively reviewed. Four radiologists independently classified the images. Images of thyroid nodules were trained using three different image classification DL models (VGG16, VGG19, and ResNet). The diagnostic performances of the DL models were calculated for the internal and external datasets and compared with the diagnoses of the four radiologists. Pairwise comparisons of the AUCs between the radiologists and DL models were made using bootstrap-based tests. RESULTS: In total, 15,409 images from 7,321 patients (mean age, 60 ± 13 years; malignant nodules, 20.7%) were randomly grouped into training (n = 12,327) and validation (n = 3,082) sets. Independent internal (n = 432; 197 patients) and external (n = 168; 59 patients) test sets were also acquired. The DL models demonstrated a higher diagnostic performance than the radiologists in the internal test set (AUC, 0.83 - 0.86 vs. 0.71 - 0.76, P < 0.05), but not in the external test set. The VGG16 model demonstrated the highest diagnostic performance in internal (AUC, 0.86; sensitivity, 91.8%; specificity, 73.2%) and external (AUC: 0.83; sensitivity: 78.6%; specificity: 76.8%) test sets. However, no statistical differences were found in the AUCs among the DL models. CONCLUSIONS: The DL models demonstrated comparable diagnostic performance to radiologists in distinguishing benign from malignant thyroid nodules on US images and may play a potential role in augmenting radiologists' diagnosis of thyroid nodules.

Preoperative Magnetic Resonance Image and Computerized Tomography Findings Predictive of Facial Nerve Invasion in Patients with Parotid Cancer without Preoperative Facial Weakness-A Retrospective Observational Study.

(1) Background: Facial nerve resection with reconstruction helps achieve optimal outcomes in the treatment of facial nerve invasion (FNI) of parotid cancer. Preoperative imaging is crucial to predict facial nerve reconstruction. The radiological findings of CT or MRI may predict FNI in the parotid cancer even without facial paralysis. Methods: We retrospectively reviewed the records of 151 patients without facial nerve paralysis before surgery who had undergone tumor resection. Previously untreated parotid cancers were included. (2) Results: The median follow-up duration was 62 months (range: 24-120 months). The FNI (+) group (n = 30) showed a significantly worse 5-year overall survival compared with the FNI (-) group (75.5 vs. 93.9%; hazard ratio = 4.19; 95% confidence interval: 1.74-10.08; p = 0.001). The tumor margin, tumor size, presence in the anterolateral parotid region (area 3), retromandibular vein involvement, distance from the stylomastoid foramen to the upper tumor margin, and a high tumor grade were significant factors related to FNI in the univariate analysis. A spiculated tumor margin, the tumor size (2.2 cm), and presence in area 3 were factors predicting FNI in the logistic regression model (p = 0.020, 0.005, and 0.050, respectively; odds ratio: 4.02, 6.40, and 8.16, respectively). (3) Conclusions: The tumor size (≥2.2 cm), spiculated margin, and presence in area 3 as presented in CT and MRI may help clinicians preoperatively predict FNI in patients with parotid cancer and establish an appropriate surgical plan.

Radiofrequency ablation of recurrent thyroid cancers: anatomy-based management.

Although differentiated thyroid cancer has an excellent prognosis and low mortality, its recurrence rate has been reported to be very high. While surgery is recommended as the standard treatment for recurrent thyroid cancer, reoperation may increase the incidence of complications due to fibrosis and anatomical distortion. In patients with high surgical risk or those who refuse repeated surgery, ultrasonography-guided ablation techniques including radiofrequency ablation, ethanol ablation, and laser ablation are suggested as alternative treatments. In this manuscript, we introduce an anatomy-based management concept for radiofrequency ablation for recurrent thyroid cancer, and present a review of the relevant literature, including indications, basic techniques, clinical outcomes, and complications.

Effect of olfactory bulb atrophy on the success of olfactory training.

PURPOSE: To evaluate the success of olfactory training in patients with olfactory loss and olfactory bulb (OB) atrophy detected on magnetic resonance imaging (MRI) and other characteristics. METHODS: This study included 48 patients with olfactory loss who underwent a nasal endoscopic examination and MRI before olfactory training. The Korean Version of the Sniffin' Sticks Test was performed before and after training. The olfactory training success was defined as an improvement of more than 6 points in the Threshold-Discrimination-Identification (TDI) score. Patient characteristics and OB atrophy pre-training were compared between successful and unsuccessful groups. RESULTS: The etiology of olfactory loss included respiratory viral infection in 30 (62.5%), trauma in 10 (20.8%), and idiopathic loss in 8 (16.7%) patients. Twenty-three (47.9%) of 48 patients exhibited successful olfactory training. Etiology, age, gender, and symptom duration were not different between unsuccessful and successful groups. Pre-training discrimination, identification, and TDI scores were significantly different between unsuccessful and successful groups (P < 0.05). Success rate of patients with bilateral OB atrophy was significantly lower than that of patients with unilateral OB atrophy and normal morphology (P = 0.006). OB height was significantly lower in the unsuccessful group than in the successful group (P < 0.05). Bilateral OB atrophy was an independent risk factor for failure of olfactory training according to the multivariate analysis. CONCLUSION: Olfactory loss patients with bilateral OB atrophy may not be able to improve olfactory function after olfactory training.

Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis.

Early prediction of treatment response in nasopharyngeal carcinoma is clinically relevant for optimizing treatment strategies. This meta-analysis was performed to evaluate whether apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can predict treatment response of patients with nasopharyngeal carcinoma. A systematic search of PubMed-MEDLINE and Embase was performed to identify relevant original articles until July 22, 2021. We included studies which performed DWI for predicting locoregional treatment response in nasopharyngeal carcinoma treated with neoadjuvant chemotherapy, definitive chemoradiation, or radiation therapy. Hazard ratios were meta-analytically pooled using a random-effects model for the pooled estimates of overall survival, local relapse-free survival, distant metastasis-free survival and their 95% CIs. ADC showed a pooled sensitivity of 87% (95% CI 72-94%) and specificity of 70% (95% CI 56-80%) for predicting treatment response. Significant between-study heterogeneity was observed for both pooled sensitivity (I2 = 68.5%) and specificity (I2 = 92.2%) (P < 0.01). The pooled hazard ratios of low pretreatment ADC for assessing overall survival, local relapse-free survival, and distant metastasis-free survival were 1.42 (95% CI 1.09-1.85), 2.31 (95% CI 1.42-3.74), and 1.35 (95% CI 1.05-1.74), respectively. In patients with nasopharyngeal carcinoma, pretreatment ADC demonstrated good predictive performance for treatment response.

Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI.

White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer's disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.

2020 Imaging Guidelines for Thyroid Nodules and Differentiated Thyroid Cancer: Korean Society of Thyroid Radiology.

Imaging plays a key role in the diagnosis and characterization of thyroid diseases, and the information provided by imaging studies is essential for management planning. A referral guideline for imaging studies may help physicians make reasonable decisions and minimize the number of unnecessary examinations. The Korean Society of Thyroid Radiology (KSThR) developed imaging guidelines for thyroid nodules and differentiated thyroid cancer using an adaptation process through a collaboration between the National Evidence-based Healthcare Collaborating Agency and the working group of KSThR, which is composed of radiologists specializing in thyroid imaging. When evidence is either insufficient or equivocal, expert opinion may supplement the available evidence for recommending imaging. Therefore, we suggest rating the appropriateness of imaging for specific clinical situations in this guideline.

Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern.

OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI). METHODS: We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software. RESULTS: Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours). CONCLUSION: Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.