Purpose: Adjuvant chemotherapy (AC) is recommended for muscle-invasive or lymph node-positive upper urinary tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU). However, disease recurrences are frequently observed in pT1 disease, and AC may increase the risk of overtreatment in pT2 UTUC patients. This study aimed to validate a risk-adapted scoring model for selecting UTUC patients with ≤pT2 disease who would benefit from AC. Materials and Methods: We retrospectively analyzed 443 ≤pT2 UTUC patients who underwent RNU. A risk-adapted scoring model was applied, categorizing patients into low- or high-risk groups. Recurrence-free survival (RFS) and cancer-specific survival (CSS) were analyzed according to risk group. Results: Overall, 355 patients (80.1%) and 88 patients (19.9%) were categorized into the low- and high-risk groups, respectively, with the latter having higher pathological stages, concurrent carcinoma in situ, and synchronous bladder tumors. Disease recurrence occurred in 45 patients (10.2%), among whom 19 (5.4%) and 26 (29.5%) belonged to the low- and high-risk groups, respectively (p<0.001). High-risk patients had significantly shorter RFS (64.3% vs. 93.6% at 60 months; hazard ratio [HR] 13.66; p<0.001) and worse CSS (80.7% vs. 91.5% at 60 months; HR 4.25; p=0.002). Multivariate analysis confirmed that pT2 stage and the high-risk group were independent predictors of recurrence and cancer-specific death (p<0.001). Decision curve analysis for RFS showed larger net benefits with our model than with the T stage model. Conclusions The risk-adapted scoring model effectively predicts recurrence and identifies optimal candidates for AC post RNU in non-metastatic UTUC.

Background: Liver transplantation (LT) is the standard treatment for end-stage liver disease; however, it can lead to biliary strictures in 25%–30% of cases. We aimed to develop a biodegradable stent loaded with paclitaxel that could be inserted during surgery without requiring removal. We evaluated the safety and efficacy of this stent using a porcine model. Methods: Fourteen pigs underwent simulated ischemic injury during LT, and a biodegradable paclitaxel-eluting stent was inserted after duct-to-duct anastomosis.Pigs were divided into four groups: no stent (n=3), bare stent (n=3), 300 µg paclitaxel stent (n=4), and 900 µg paclitaxel stent (n=4). After 3 months of follow-up, autopsies were conducted to obtain common bile duct tissue samples, and inflammation and fibrosis thicknesses were assessed under a microscope. Results: Most tissues had resolved the inflammatory reactions by the 3-month mark. The thinnest fibrosis thickness was observed in the 900 µg group (359.08±167.23 µm); however, no statistical significance was observed. Conclusion This study demonstrated the safety of paclitaxel-eluting biodegradable biliary stents and their positive effects on fibrosis in an ischemic bile duct porcine model. This biodegradable stent represents a potential approach for overcoming the complications associated with biliary strictures after LT.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.

Background: The purposes of this study were to determine the accuracy of our cup positioning method and to evaluate the dislocation rate after total hip arthroplasty (THA). Methods: After positioning the patient in the lateral decubitus position on the operation table, an anteroposterior view of the hip was taken. The pelvic pitch was measured on the X-ray. A positive pitch was defined as the caudal rotation of the upper hemipelvis. Our target abduction of the cup was 43°. We used the cup holder to guesstimate the cup abduction. In a preliminary study, we found that the weight of the cup holder increased the pelvic pitch by 5°. Thus, the target abduction of the cup holder was calculated by a formula: 43° – pelvic pitch – 5°. During the cup insertion, the cup holder was anteverted to the calculated target according to the concept of combined anteversion. We evaluated 478 THAs (429 patients), which were done with the use of the method. Results: The mean cup abduction was 43.9° (range, 32.0°–53.0°) and the mean error of cup abduction was 2.4° (standard deviation [SD], 2.0°; range, 0.0°–11.0°). The mean cup anteversion was 28.5° (range, 10.0°–42.0°) and the mean error of cup anteversion was 6.7° (SD, 5.2°; range, 0.0°–27.6°). Of all, 82.4% of the cups (394 / 478) were within the safe zone: 30°–50° abduction and 10°–35° anteversion. During 2- to 5-year follow-up, no hip dislocated. Conclusions Our adjusting method according to the pelvic pitch can be a reliable option for optimizing the cup abduction in THA.

Giant adrenal myelolipomas are rare, benign, and hormonally inactive tumors. We present the case of a 53-year-old man with a 19-cm retroperitoneal mass, initially suspected to be a retroperitoneal liposarcoma, angiomyolipoma, or adrenal myelolipoma. After conducting endocrine assessments, which were within normal ranges, we decided to perform surgical excision using a modified Makuuchi incision. The tumor was successfully removed, and the final pathological examination confirmed the diagnosis of adrenal myelolipoma. The patient was discharged with no complications and remained without disease recurrence or distant metastasis as of 1 year postoperatively. In conclusion, giant myelolipomas are rare and cause symptoms owing to their large size. Surgical removal is recommended for large or symptomatic myelolipomas. The modified Makuuchi incision allows efficient and safe tumor removal in open surgery for giant myelolipomas.

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma. Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

Background: In 2018, Femoral Neck System (FNS), a dedicated fixator for femoral neck fractures, was introduced. This device has been in increasing use because it provides excellent rotational and angular stability. However, the shortest bolt of FNS is 75 mm long. Thus, it is not usable when the axial length of the proximal femur (ALPF), the distance between the innominate tubercle and the surface of the femoral head, is less than 80 mm. In this study, we investigated the incidence and associated factors of small ALPF (< 80 mm) in femoral neck fracture patients. Methods: We measured the ALPF on preoperative computed tomography (CT) scans of 261 patients (166 women and 55 men), who were operated due to nondisplaced or impacted femoral neck fractures. The ALPF was measured on reconstructed oblique coronal images along the femoral neck. We evaluated the distribution of ALPF, calculated the incidence of small ALPF (< 80 mm), and correlated it with patient’s height, weight, body mass index, age, bone mineral density (T-score), and caput-column-diaphysis angle. Results: The ALPF ranged from 67.4 mm to 107.1 mm (mean, 88.4 mm; standard deviation, 7.2 mm). In 19 patients (8.6%, 19 / 221), the length was < 80 mm. The ALPF was strongly correlated with height (correlation coefficient = 0.707, R2 = 0.500, p < 0.001) and moderately correlated with weight (correlation coefficient = 0.551, R2 = 0.304, p < 0.001). The T-score was moderately correlated with the ALPF (correlation coefficient = 0.433, R2 = 0.187, p < 0.001). The age was moderately correlated with the ALPF (correlation coefficient = –0.353, R2 = 0.123, p < 0.001). Conclusions A considerable percentage of femoral neck fracture patients (8.6%) had small proximal femurs (ALPF < 80 mm), which cannot be operated with FNS. We recommend measuring the ALPF using reconstructed oblique coronal CT images or scaled hip radiographs: en face view of the femoral neck prior to surgery in patients with short stature and/or low body weight. If the ALPF is < 80 mm, the surgeon should prepare other fixation devices.