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.

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.

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.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Background: A nationwide Rehabilitation at Home Care Pilot Program for patients undergoing lower extremity orthopedic surgeries has been ongoing since 2020. The program was designed to improve clinical outcomes through early mobilization and rehabilitation after discharge. This study aimed to analyze the interim data to assess its effectiveness and suggest improvements, particularly for older patients who are more vulnerable compared to younger patients. Methods: We analyzed the data of 872 patients from seven hospitals. Patients were divided into an older-age group (OG; ≥60 years) and younger-age group (YG; <60 years). The Berg Balance Scale (BBS) and Numeric Rating Scale (NRS) scores for pain were the main outcomes. Results: Participants were categorized into OG (n=801) and YG (n=71). The most common surgeries were knee joint replacement (63.7%) in OG and hip joint replacement in YG (77.5%). It took more days to enroll in the pilot program in OG (4.4 days) than YG (3.2 days). OG showed less improvement in BBS than YG for all surgeries (23.3 vs. 31.9) and hip joint surgery (19.8 vs. 33.5). In patients with hip joint replacement in OG, those with a higher number of comorbidities (≥3) showed less BBS improvement than those with fewer comorbidities (<3). In the pain domain, NRS scores decreased in both groups. Conclusion The postoperative rehabilitation education program appeared to be effective in improving physical function and reducing pain severity, though the improvement was less obvious in older patients who may require a more comprehensive approach compared to younger patients.

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.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Hepatocellular carcinoma (HCC), a leading cause of cancer-related mortality, is often diagnosed at an advanced stage. As a result, approximately half of patients with HCC received systemic therapy during the disease course. In recent years, the introduction of immune checkpoint inhibitors (ICIs)—notably, atezolizumab plus bevacizumab and tremelimumab plus durvalumab—has transformed the treatment paradigm for advanced HCC, making them a firstline treatment option. Considering the increasing use of ICIs, understanding the incidence and appropriate management of their adverse events (AEs) is necessary for patient care. Hence, this review summarizes the incidence and management strategies for key immune-related AEs, including liver injury (hepatitis), thyroiditis, colitis, skin rash, and pneumonitis. Additionally, we discuss AEs associated with vascular endothelial growth factor inhibitors, such as hypertension, proteinuria, and bleeding. By thoroughly understanding these treatment-related AEs, the clinical outcomes of patients with HCC undergoing ICI-based therapy may be improved.

Background: A nationwide Rehabilitation at Home Care Pilot Program for patients undergoing lower extremity orthopedic surgeries has been ongoing since 2020. The program was designed to improve clinical outcomes through early mobilization and rehabilitation after discharge. This study aimed to analyze the interim data to assess its effectiveness and suggest improvements, particularly for older patients who are more vulnerable compared to younger patients. Methods: We analyzed the data of 872 patients from seven hospitals. Patients were divided into an older-age group (OG; ≥60 years) and younger-age group (YG; <60 years). The Berg Balance Scale (BBS) and Numeric Rating Scale (NRS) scores for pain were the main outcomes. Results: Participants were categorized into OG (n=801) and YG (n=71). The most common surgeries were knee joint replacement (63.7%) in OG and hip joint replacement in YG (77.5%). It took more days to enroll in the pilot program in OG (4.4 days) than YG (3.2 days). OG showed less improvement in BBS than YG for all surgeries (23.3 vs. 31.9) and hip joint surgery (19.8 vs. 33.5). In patients with hip joint replacement in OG, those with a higher number of comorbidities (≥3) showed less BBS improvement than those with fewer comorbidities (<3). In the pain domain, NRS scores decreased in both groups. Conclusion The postoperative rehabilitation education program appeared to be effective in improving physical function and reducing pain severity, though the improvement was less obvious in older patients who may require a more comprehensive approach compared to younger patients.