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.

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.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) is dominating coronavirus disease 2019 (COVID-19) worldwide. The waning protective effect of available vaccines against the Omicron variant is a critical public health issue. This study aimed to assess the impact of the third COVID-19 vaccination on immunity against the SARS-CoV-2 Omicron BA.1 strain in older individuals. Materials and Methods: Adults aged ≥60 years who had completed two doses of the homologous COVID-19 vaccine with either BNT162b2 (Pfizer/BioNTech, New York, NY, USA, BNT) or ChAdOx1 nCoV (SK bioscience, Andong-si, Gyeongsangbuk-do, Korea, ChAd) were registered to receive the third vaccination. Participants chose either BNT or mRNA-1273 (Moderna, Norwood, MA, USA, m1273) mRNA vaccine for the third dose and were categorized into four groups: ChAd/ChAd/BNT, ChAd/ChAd/m1273, BNT/BNT/BNT, and BNT/BNT/m1273. Four serum specimens were obtained from each participant at 0, 4, 12, and 24 weeks after the third dose (V1, V2, V3, and V4, respectively).Serum-neutralizing antibody (NAb) activity against BetaCoV/Korea/KCDC03/2020 (NCCP43326, ancestral strain) and B.1.1.529 (NCCP43411, Omicron BA.1 variant) was measured using plaque reduction neutralization tests. A 50% neutralizing dilution (ND 50 ) >10 was considered indicative of protective NAb titers. Results: In total, 186 participants were enrolled between November 24, 2021, and June 30, 2022. The respective groups received the third dose at a median (interquartile range [IQR]) of 132 (125 - 191), 123 (122 - 126), 186 (166 -193), and 182 (175 - 198) days after the second dose. Overall, ND 50 was lower at V1 against Omicron BA.1 than against the ancestral strain. NAb titers against the ancestral strain and Omicron BA.1 variant at V2 were increased at least 30-fold (median [IQR], 1235.35 [1021.45 - 2374.65)] and 129.8 [65.3 - 250.7], respectively). ND 50 titers against the ancestral strain and Omicron variant did not differ significantly among the four groups (P= 0.57). NAb titers were significantly lower against the Omicron variant than against the ancestral strain at V3 (median [IQR], 36.4 (17.55 - 75.09) vs. 325.9 [276.07 - 686.97]; P = 0.012). NAb titers against Omicron at V4 were 16 times lower than that at V3. Most sera exhibited a protective level (ND 50 >10) at V4 (75.0% [24/32], 73.0% [27/37], 73.3% [22/30], and 70.6% [12/17] in the ChAd/ChAd/BNT, ChAd/ChAd/m1273, BNT/BNT/BNT, and BNT/BNT/m1273 groups, respectively), with no significant differences among groups (P = 0.99). Conclusion A third COVID-19 mRNA vaccine dose restored waning NAb titers against Omicron BA.1. Our findings support a third-dose vaccination program to prevent the waning of humoral immunity to SARS-CoV-2.