• Imaging Science in Dentistry
• /
• v.51 no.2
• /
• pp.129-136
• /
• 2021

Purpose: This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients. Materials and Methods: This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC). Results: Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists. Conclusion: These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

• Korean Journal of Radiology
• /
• v.25 no.1
• /
• pp.11-23
• /
• 2024

Objective: To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). Materials and Methods: A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm² was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). Results: Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002). Conclusion: Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

• Journal of the Korean Society of Radiology
• /
• v.83 no.2
• /
• pp.284-292
• /
• 2022

To provide high-quality training to residents in a rapidly changing medical environment, it is very important to improve the annual training curriculum centered on competency and ensure that training hospitals maintain an environment suitable for training. The Korean Society of Radiology (KSR) has been steadily improving the training system and has suggested the improvement of the training system by strengthening the competency-based evaluation and faculty development. Currently, KSR was selected for the second annual training curriculum systematization construction project in July 2021, and developed entrustable professional activities, core competencies, and assessment guidelines required by the construction project. Therefore, the development process and assessment guidelines will be introduced to residents and the faculty.

• Korean Journal of Radiology
• /
• v.22 no.3
• /
• pp.415-424
• /
• 2021

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

• Korean Journal of Radiology
• /
• v.24 no.11
• /
• pp.1043-1045
• /
• 2023

• Korean Journal of Radiology
• /
• v.24 no.5
• /
• pp.478-479
• /
• 2023

• Korean Journal of Radiology
• /
• v.20 no.1
• /
• pp.166-170
• /
• 2019

Objective: To establish a cost-effective and easily available phantom for training residents in ultrasound-guided fine needle thyroid nodule targeting punctures. Materials and Methods: Tofu, drinking straws filled with coupling gel, a urine tube, and 21-gauge needles were used to generate a phantom thyroid with nodules for training. Twelve radiology residents were involved in the study. The puncture success rates were recorded and compared before and after phantom training using the Wilcoxon signed-rank test. Results: On ultrasonography, tofu mimicked the texture of the thyroid. Drinking straws filled with coupling gel mimicked vessels. The urine tube filled with air mimicked the trachea, and 21-gauge needles mimicked small nodules in the transverse section. The entire phantom was similar to the structure of the thyroid and surrounding tissues. The puncture success rates of radiology residents were significantly increased from 34.4 ± 14.2% to 66.7 ± 19.5% after training (p = 0.003). The phantom was constructed in approximately 10 minutes and materials cost less than CNY 10 (approximately $ 1.5) at a local store. Conclusion: The tofu model was cost-effective, easily attainable, and effective for training residents in ultrasound-guided fine needle thyroid nodule targeting punctures in vitro.

• Journal of the Korean Society of Radiology
• /
• v.6 no.4
• /
• pp.291-298
• /
• 2012

Department of radiology implements the hospital-based clinical training to accept medical treatment techniques and to adapt experiences for students. However, it might cause negative effects to training education, leading to doubt about major and pressure about training as lots of students experience clinical treatment and complex stress in unfamiliar environment. Regarding this, pressure element that students can experience and diverse variables of training were compared and analyzed. With students in department of radiology for 6 colleges and universities, from September 15th to October 25th in 2011. The degree of stress for students in training was shown high in the fields of cost(3.06) and trainers(3.02). Value and ideal(2.94), role and experiment(2.93), training environment(2.74) and relationships among trainees(2.64) were followed in the order. Except expense regarding stress from clinical training, but in all factors, women showed higher pressure level than men(P<.05) and in stress range according to BEPSI-K, a meaningful difference was shown in fields of training environment, relationships among trainees and role and experiment(P<.01, P<.001, P<.05). Therefore, It has been confirmed that there is correlation between stress of students and satisfaction for clinical training with each other closely.

• Korean Journal of Radiology
• /
• v.25 no.2
• /
• pp.126-133
• /
• 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

• Korean Journal of Radiology
• /
• v.21 no.6
• /
• pp.670-683
• /
• 2020

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.