The anticancer drugs have evolved significantly, spanning molecular targeted therapeutics (MTTs), immune checkpoint inhibitors (ICIs), chimeric antigen receptor T-cell (CAR-T) therapy, and antibody-drug conjugates (ADCs). Complications associated with these drugs vary widely based on their mechanisms of action. MTTs that target angiogenesis can often lead to complications related to ischemia or endothelial damage across various organs, whereas non-anti-angiogenic MTTs present unique complications derived from their specific pharmacological actions. ICIs are predominantly associated with immunerelated adverse events, such as pneumonitis, colitis, hepatitis, thyroid disorders, hypophysitis, and sarcoid-like reactions. CAR-T therapy causes unique and severe complications including cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome. ADCs tend to cause complications associated with cytotoxic payloads. A comprehensive understanding of these drug-specific toxicities, particularly using medical imaging, is essential for providing optimal patient care. Based on this knowledge, radiologists can play a pivotal role in multidisciplinary teams. Therefore, radiologists must stay up-to-date on the imaging characteristics of these complications and the mechanisms underlying novel anticancer drugs.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Clinical trials for chimeric antigen receptor (CAR) T-cell therapy are in the early stages but are expected to progress alongside new treatment approaches. This suggests that imaging will play an important role in monitoring disease progression, treatment response, and treatment-related side effects. There are, however, challenges that remain unresolved, regarding imaging in CAR T-cell therapy. We herein discuss the role of imaging, focusing on how tumor response evaluation varies according to cancer type and target antigens in CAR T-cell therapy. CAR T-cell therapy often produces rapid and significant responses, and imaging is vital for identifying side effects such as cytokine release syndrome and neurotoxicity. Radiologists should be aware of drug mechanisms, response assessments, and associated toxicities to effectively support these therapies. Additionally, this article highlights the importance of the Lugano criteria, which is essential for standardized assessment of treatment response, particularly in lymphoma therapies, and also explores other factors influencing imaging-based evaluation, including emerging methodologies and their potential to improve the accuracy and consistency of response assessments.

Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

Objective: Previous studies have reported an association between cancer-related symptoms and perceived social support (PSS). The objective of this study was to analyze whether Chemotherapy-Induced Peripheral Neuropathy (CIPN), a prevalent side effect of chemotherapy, varies according to PSS level using a validated tool for CIPN at prospective follow-up. Methods: A total of 39 breast cancer patients were evaluated for PSS using the Multidimensional Scale of Perceived Social Support (MSPSS) prior to chemotherapy and were subsequently grouped into one of two categories for each subscale: low-to-moderate PSS and high PSS. CIPN was prospectively evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20 (CIPN20) at five time points. A linear mixed-effects model with square root transformation was employed to investigate whether the CIPN20 scales varied by PSS level and time point. Results: Statistical analysis of the MSPSS total scale and subscales revealed a significant effect of the friends subscale group and time point on the CIPN20 sensory scale. The sensory scale score of CIPN20 was found to be lower in participants with high PSS from friends in comparison to those with low-to-moderate PSS at 1 month post-chemotherapy (p=0.010). Conclusion This is the first study to prospectively follow the long-term effect of pre-treatment PSS from friends on CIPN. Further studies based on larger samples are required to analyze the effects of PSS on the pathophysiology of CIPN.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

With the increasing emphasis on diversity, equity, and inclusion (DEI) in organizations and institutions, academic societies in gastroenterology and hepatology are beginning to take actionable steps toward achieving DEI. The successful implementation of DEI initiatives leads to excellence in the field, improved patient outcomes, particularly in areas where health disparities are prevalent, and advances in the gastrointestinal discipline. Such implementation also results in a workforce that better reflects the growing diversity of the population. This review defines DEI and introduces the DEI policies and strategies adopted by the academic societies of gastroenterology in other countries. This paper proposes strategies to integrate DEI better into the Korean Society of Gastroenterology, emphasizing the importance of embedding DEI into the culture and strategic framework. The key strategies include establishing a DEI committee, setting clear targets, and conducting formal assessments to measure DEI progress. This study focused on enhancing workforce diversity, particularly among women and young doctors, and advocates for the need to support their academic development through male allyship and the promotion of equitable and inclusive academic cultures.

Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.