Objective: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors combined with endocrine therapy (ET) constitute the standard systemic treatment for estrogen receptor-positive, human epidermal growth factor 2-negative (ER+/HER2-) metastatic breast cancer (MBC). However, treatment responses remain heterogeneous, highlighting the need for reliable prognostic markers. This study aimed to evaluate the prognostic significance of 18F-fluorodeoxyglucose (FDG) PET/CT findings in this setting. Materials and Methods: This retrospective single-center cohort study included patients with ER+/HER2- MBC who underwent18F-FDG PET/CT before initiating CDK4/6 inhibitors plus ET between 2018 and 2023. Maximum standardized uptake value(SUVmax), whole-body metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured. Progression-free survival (PFS) and overall survival (OS) were evaluated as the primary and secondary outcomes, respectively, using multivariable Cox models. PET parameters (SUVmax, MTV, and TLG) were analyzed as both continuous and dichotomized variables based on median values, adjusting for relevant clinical covariates. Results: Among the 374 patients, 82 (21.9%) presented with de novo metastatic disease, and 357 (95.5%) received CDK4/6 inhibitors as first-line therapy. In multivariable Cox analysis, all continuous PET parameters were independently associated with PFS (adjusted hazard ratio for SUVmax 1.05 [95% confidence interval 1.02–1.08]; log-transformed MTV 1.16 [1.08–1.25]; and log-transformed TLG 1.14 [1.07–1.23]) and OS (SUVmax 1.08 [1.04–1.11]; log-transformed MTV 1.24 [1.12–1.38]; and log-transformed TLG 1.22 [1.11–1.34]) with all P < 0.001. Results based on dichotomized PET parameters were similar to those obtained with continuous values: PFS (adjusted hazard ratio for SUVmax ≥ 7.6, 1.41 [1.08–1.85]; MTV ≥ 21.2 cm 3 , 1.41 [1.08–1.86]; and TLG ≥ 78.9, 1.51 [1.14–1.99]) with P ≤ 0.013 and OS (1.43 [1.01–2.04]; 1.84 [1.28– 2.66]; and 1.73 [1.20–2.50], respectively) with P ≤ 0.046. Conclusion Pretreatment 18F-FDG PET/CT parameters are independent prognostic markers in patients with ER+/HER2- MBC receiving CDK4/6 inhibitors with ET, supporting their potential utility in risk stratification.

Purpose: Based on the KEYNOTE-522 study, neoadjuvant pembrolizumab plus chemotherapy has become the standard treatment for early-stage triple-negative breast cancer (TNBC).This study evaluated the real-world efficacy, safety, and predictors of pathologic complete response (pCR) in Korean patients. Methods: We conducted a retrospective cohort study of 174 patients with early-stage TNBC who received the KEYNOTE-522 regimen (neoadjuvant pembrolizumab plus paclitaxel and carboplatin, followed by doxorubicin and cyclophosphamide) at a tertiary cancer center between August 2022 and July 2024. We assessed the primary endpoints, including pCR rate and event-free survival (EFS). We performed univariable and multivariable logistic regression analyses to identify independent predictors of pCR. Results: The median patient age was 50 years (range, 24–74 years). The clinical stages were II and III in 79.3% and 20.1% of patients, respectively, and 10.9% had clinical N3 disease. The overall pCR rate was 62.1%, and the N3 subgroup had a pCR rate of 47.4%. On multivariable analysis, high baseline Ki-67 expression (≥ median, 75%) was significantly associated with pCR (odds ratio, 2.84; 95% confidence interval, 1.45 to 5.66; p = 0.002). At a median followup of 18.4 months, the 12-month EFS rate was 97.4%, with significantly superior outcomes observed in patients who achieved pCR compared with those who did not achieve pCR (100% vs. 93.1%, p = 0.007). The treatment completion rate was 92.0%, and immune-related adverse events occurred in 13.8% of patients. Conclusion In this real-world analysis of one of the largest Asian cohorts of patients with earlystage TNBC treated with neoadjuvant pembrolizumab, the KEYNOTE-522 regimen demonstrated substantial efficacy and manageable toxicity, consistent with the original trial findings.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

As fluoroscopy-guided interventional procedures gain popularity, the associated health threats from radiation exposure to interventionalists during these procedures are increasing. Therefore, an understanding of the potential risks of radiation and careful consideration on minimizing exposure to radiation during the procedures are of paramount importance. The Korean Pancreatobiliary Association has developed a clinical practice guideline to minimize radiation exposure during fluoroscopy-guided interventional procedures. This guideline provides recommendations to deal with the risk of radiation exposure to interventionalists who perform fluoroscopy-guided procedures, and emphasizes the importance of proper and practical approaches to avoid unnecessary radiation exposure.

As fluoroscopy-guided interventional procedures gain popularity, the associated health threats from radiation exposure to interventionalists during these procedures are increasing. Therefore, an understanding of the potential risks of radiation and careful consideration on minimizing exposure to radiation during the procedures are of paramount importance. The Korean Pancreatobiliary Association has developed a clinical practice guideline to minimize radiation exposure during fluoroscopy-guided interventional procedures. This guideline provides recommendations to deal with the risk of radiation exposure to interventionalists who perform fluoroscopy-guided procedures, and emphasizes the importance of proper and practical approaches to avoid unnecessary radiation exposure.