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 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.

Purpose: In pancreatic cancer, therapeutic investigations targeting liver metastases could improve survival. However, the use of local treatment for oligometastasis in pancreatic cancer remains controversial. This study aimed to investigate the oncological role of local therapy in patients who underwent curative pancreatectomy and subsequently developed liver metastases. Materials and Methods: Data concerning patients who underwent curative pancreatectomy for pancreatic cancer at Severance Hospital in Seoul, South Korea between 2006 and 2018 were retrospectively reviewed. We included patients with one or two liver metastases, as confirmed on imaging. We excluded those with metastases in other organs. The patients were divided into two groups: the NT group, receiving conventional therapy without local treatment; and the LT group, receiving local treatments for liver metastases alongside standard therapy. Results: Of the 43 included patients (NT group, n=33; LT group, n=10), no significant differences were observed in overall survival (OS) [hazard ratio (HR) 0.846; 95% confidence interval (CI) 0.397–1.804; p=0.665] or post-recurrence survival (HR 0.932; 95% CI 0.437–1.985, p=0.855) between the two groups. In multivariate analysis, early recurrence within 6 months (p<0.001) and the use of 5-fluorouracil (FU)-based adjuvant chemotherapy (CTx) (p=0.011), as well as 5-FU-based CTx after liver metastasis (p=0.008) when compared with gemcitabine-based regimens, were significant predictors of poor OS. Conclusion The oncologic role of local treatment for hepatic metastasis remains controversial in patients with hepatic metastasis after radical pancreatectomy. In the era of potent chemotherapeutic regimens, further research is needed to clarify the efficacy of such regimens.

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 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.