Purpose: This study aimed to evaluate the role of hepatic resection in patients with objective responses after combined transarterial chemoembolization (TACE) and radiotherapy (RT) for hepatocellular carcinoma (HCC) with macroscopic vascular invasion (MVI). Materials and Methods: We retrospectively reviewed the patients treated with combined TACE and RT for HCC with MVI between 2010 and 2015. Some of the patients with objective responses underwent hepatic resection or liver transplantation; to investigate the impact of surgery, patients with objective responses who did not undergo surgery were selected as the control group. Survival outcomes were compared using a propensity score–based stabilized inverse probability of treatment weighting method. Results: Out of the 170 patients with objective responses after combined TACE and RT, 41 patients underwent surgery, including eight liver transplantations. The unweighted surgery group was younger and had a higher proportion of solitary tumors and unilateral vascular involvement. After adjustment, the 3-year overall survival (OS) rates were 61.0% and 28.6% in the surgery and non-surgery groups, respectively. The most important prognostic factor for OS was surgery (adjusted Cox hazard ratio [HR], 0.28; 95% confidence interval [CI], 0.17 to 0.46; p < 0.001). Complete response after TACE and RT (vs. partial response) was also a significant prognostic factor for OS (adjusted HR, 0.41; 95% CI, 0.27 to 0.61; p < 0.001). There was no surgical mortality. Four patients (9.8%) required additional surgery due to bleeding or graft failure. Conclusion Hepatic resection was significantly associated with improved OS in patients who showed objective responses after receiving combined TACE and RT for HCC with MVI.

Objective: Prolonged fasting has been reported to have a negative effect on patient outcomes. Although fasting is routinely practiced in the emergency department (ED), there is limited evidence on the current status and appropriateness of fasting in the ED. This study examined the status and appropriateness of fasting in the ED and identified the characteristics of patients who experienced unnecessary fasting. Methods: A retrospective observational study was performed at an urban ED in Seoul, Korea. The fasting status and characteristics of adult patients who visited the ED and were admitted or discharged between April 1 and April 30, 2022, were analyzed. A checklist for discontinuing fasting in the ED was used to screen for unnecessary fasting. Results: Among 2,292 patients who met the study inclusion criteria, all patients were instructed to begin fasting upon ED arrival. Fasting was discontinued by the physician’s order in 300 patients (13.1%). Patients remained fasting for 92.0%±23.2% of their total ED length of stay. Of the 1,577 patients eligible for fasting discontinuation according to the checklist, 249 patients (15.8%) were ordered to discontinue fasting, and 1,328 patients (84.2%) experienced unnecessary fasting. Conclusion ED patients spent significant time fasting during their ED length of stay. A high prevalence of unnecessary fasting was identified in the ED.

Objective: To evaluate the effects of reduced kVp and reduced iodinated contrast volume on radiation dose and image quality in canine abdominal CT. Methods: A phantom study (0%–10% iodine dilutions) at 120, 100, and 80 kVp quantified the relationship between iodine concentration and CT attenuation. Eight healthy Beagle dogs underwent multiphase abdominal CT at 120, 100, and 80 kVp (1-week intervals) with weightbased contrast volumes adjusted using phantom-derived slope ratios. Radiation output (CTDIvol , dose-length product) and quantitative image quality (attenuation, background noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) were assessed, along with blinded qualitative scoring by 2 readers. Results: Compared with 120 kVp, CTDIvol decreased by 41% at 100 kVp and 74% at 80 kVp, with similar proportional decreases in dose-length product. Lower kVp increased iodinerelated attenuation, permitting iodine-dose reductions (17% at 100 kVp; 34% at 80 kVp).Quantitative noise increased and SNR decreased at 80 kVp, particularly in delayed-phase images; qualitative noise scores were significantly worse at 80 kVp, whereas overall qualitative image quality did not differ across protocols. Conclusions and Relevance: In dogs, a 100 kVp protocol reduced radiation output and iodine dose while maintaining diagnostic image quality, supporting 100 kVp as a practical optimization strategy for clinical abdominal CT.

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.