Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Background/Aims: This study evaluated the long-term cardiovascular complications among Korean patients with hypertension and compared them with that of controls without hypertension. Methods: The Korean Hypertension Cohort (KHC) enrolled 11,043 patients with hypertension and followed them for more than 10 years. Age- and sex-matched controls without hypertension were enrolled at a 1:10 ratio. We compared the incidence of cardiovascular events and death among patients and controls without hypertension. Results: The mean age was 59 years, and 34.8% and 16.5% of the patients belonged to the high and moderate cardiovascular risk groups, respectively. During the 10-year follow-up, 1,591 cardiovascular events (14.4%) with 588 deaths (5.3%) occurred among patients with hypertension and 7,635 cardiovascular events (6.9%) with 4,826 deaths (4.4%) occurred among controls. Even the low-risk population with hypertension showed a higher cardiovascular event rate than the population without hypertension. Although blood pressure measurements in the clinic showed remarkable inaccuracy compared with those measured in the national health examinations, systolic blood pressure (SBP) ≥ 150 mmHg was significantly associated with a higher risk of cardiovascular events. Conclusions This long-term follow-up study confirmed the cardiovascular event rates among Korean hypertensive patients were substantial, reaching 15% in 10 years. SBP levels ≥ 150 mmHg were highly associated with occurrence of cardiovascular event rates.

Background/Aims: Cardiorespiratory fitness (CRF), as measured by maximal oxygen consumption (VO2max), is an important independent predictive factor of cardiovascular outcomes in patients with heart failure (HF). However, it is unclear whether conventional equations for estimating CRF are applicable to patients with HF with preserved ejection fraction (HFpEF). Methods: This study included 521 patients with HFpEF (EF ≥ 50%) whose CRF was directly measured by cardiopulmonary exercise test using a treadmill. We developed a new equation (Kor-HFpEF) for half of the patients in the HFpEF cohort (group A, n = 253) and validated it for the remaining half (group B, n = 268). The accuracy of the Kor-HFpEF equation was compared to that of the other equations in the validation group. Results: In the total HFpEF cohort, the directly measured VO2max was significantly overestimated by the FRIEND and ACSM equations (p < 0.001) and underestimated by the FRIEND-HF equation (p <0.001) (direct 21.2 ± 5.9 mL/kg/min; FRIEND 29.1 ± 11.8 mL/kg/min; ACSM 32.5 ± 13.4 mL/kg/min; FRIEND-HF 14.1 ± 4.9 mL/kg/min). However, the VO2max estimated by the Kor-HFpEF equation (21.3 ± 4.6 mL/kg/min) was similar to the directly measured VO2max (21.7 ± 5.9 mL/kg/min, p = 0.124), whereas the VO2max estimated by the other three equations was still significantly different from the directly measured VO2max in group B (all p < 0.001). Conclusions Traditional equations used to estimate VO2max were not applicable to patients with HFpEF. We developed and validated a new Kor-HFpEF equation for these patients, which had a high accuracy.

Background: Toe ganglion cysts are often symptomatic and recurrent. Communicating lesions between ganglion cysts and the interphalangeal joint (IPJ) or tendon sheath make it difficult to prevent a recurrence. Temporary restriction of the joint and tendon motion can facilitate surgical site healing. This study analyzed the clinical results of temporary pin fixation of the IPJ after toe ganglion cyst excision. Methods: Sixteen patients with symptomatic toe ganglion cysts underwent surgical treatment. Excision alone was initially performed on 10 patients. Six patients underwent temporary pin fixation of the IPJ after ganglion cyst excision. Repeat excision with pin fixation was performed for recurrence in two patients after excision only. Clinical evaluations and postoperative complications were analyzed. Results: Fourteen of 16 toe ganglion cysts were located near the IPJ. Two cysts not adjacent to the joint completely healed after excision alone. Seven of 14 cysts near the joint recurred after initial excision alone and required repeated reoperation. Eight cysts did not recur after excision with pin fixation, including 2 that recurred after excision alone. Conclusions Temporary IPJ pin fixation after excision for ganglion cysts can be effective for preventing the recurrence of ganglion cysts adjacent to toe IPJ.

Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson’s disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7–12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343;median age [IQR], 10 [4–15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5–14] years; male:female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

Background: The purpose of this study was to identify the connection between handwashing and toothbrushing, focusing on eating habits, and to verify whether eating habits can be used as an action cue for forming health habits. Methods: This was a cross-sectional study using secondary data from the 2019 community health survey. The participants included 229,099 adults aged 19 years or older, representative of the South Korean people. We employed two dependent variables: one was washing hands, and the other was brushing teeth. Eating habits was a major independent variable. Socioeconomic variables, such as age, gender, income, occupation, economic activity, education, and residence were adjusted as confounders. Multivariate logistic regression was performed to calculate adjusted odds ratio and 95% confidence intervals. Results: Most of the participants had good health behaviors: those who wash their hands and brush their teeth were each approximately 80%. Our finding indicated that brushing teeth and washing hands can be connected with eating habits. After adjusting for confounders, it was found that people who wash their hands before meals (compared to those who did not wash their hands before meals) had a higher toothbrushing rate after meals (i.e., socioeconomic status) (Adjusted Odds Ratio: 2.0, Confidence Intervals: 1.9 to 2.1). Conclusion Those who practice either washing hands before meals or brushing teeth after meals were found to have a connection between washing hands and brushing teeth based on the results of practicing other health behaviors. This implies that eating habits can be connected as a behavior cue to promote health habits, such as washing hands before meals and brushing teeth after meals.