Purpose: Triple-negative breast cancer (TNBC) is a frequent phenotype of BRCA-mutanttumors. Tumors with BRCAness may show characteristics of BRCA-mutant tumors and respond to similar treatments. Next-generation sequencing is an efficient and cost-effective method for simultaneously sequencing multiple cancer susceptibility genes, surpassing conventional Sanger testing. Methods: A total of 148 women with TNBC were recruited from December 2015 to November 2018, as part of a sub-analysis based on the PEARLY trial data. Of them, 103 patients received neoadjuvant chemotherapy (NCT). The targeted genes related to hereditary cancers were sequenced using the 65-gene germline next-generation sequencing (gNGS) panel pathogenic and likely pathogenic variants (P&LPs) were determined by Sanger sequencing. We examined the occurrence of pathologic complete remission (ypCR) in patients with P&LPs. Results: The patients’ median age was 47 years (range, 27–69 years). Twenty (13.7%) of 148 patients had P&LP in six genes, including BARD1 (n = 2), BRCA1 (n = 9), BRCA2 (n = 5), CHEK2 (n = 1), RAD51C (n = 1), and RAD51D (n = 2). Among the 103 patients with NCT, 43 (41.7%) achieved ypCR (P&LPs; 9 individuals vs. non-variants; 34 individuals). Among the 103 patients with NCT, 14 (9.3%) had P&LPs. Nine of 14 patients with P&LPs, including BARD1 (n = 2), BRCA1 (n = 4), BRCA2 (n = 1), RAD51C (n = 1), and RAD51D (n = 1), achieved ypCR, showing a trend toward statistical significance (p = 0.066). Conclusion Germline P&LP mutations in TNBC patients can be detected by gNGS. This panel test can identify BRCA and BRCAness mutations that may predict ypCR in TNBC.

Objective: Primary frozen shoulder causes significant pain and progressively restricts shoulder movements. Diagnosis is made clinically based on patient history and physical examination. Management is mainly non-invasive owing to its self-limiting clinical course. However, clinical practice guidelines for frozen shoulder have not yet been developed in Korea. The developed guidelines aim to provide evidence-based recommendations for the diagnosis and treatment of frozen shoulder. Methods: A guideline development committee reviewed the literature from four databases (PubMed, Embase, Cochrane Library, and KMbase). Using the Population, Intervention, Comparator, and Outcome (PICO) framework, the committee formulated two backgrounds and 16 key questions to address common clinical concerns. Recommendations were made using the Grading of Recommendations, Assessment, Development, and Evaluation framework. Results: Diabetes, thyroid disease, and dyslipidemia significantly increase the risk of developing a frozen shoulder. Although frozen shoulder is often self-limiting, some patients may experience long-term functional disabilities. Ultrasound and magnetic resonance imaging should be used as adjunctive tools alongside clinical diagnosis, and rather than as independent diagnostic methods. Noninvasive approaches, such as medications, physical modalities, exercises, electrical stimulation, and manual therapy, may reduce pain and improve shoulder function. Other noninvasive interventions have limited evidence, and their application should be based on clinical judgment. Intra-articular steroid injections are recommended for treatment, and physiotherapy or hydrodilation with steroid injections can also be beneficial. Conclusion These guidelines provide evidence-based recommendations for diagnosing and treating primary frozen shoulder.

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: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.

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/Aims: Based on their anatomy, cholangiocarcinomas (CCAs) are classified into intrahepatic, hilar, and distal CCAs. Although the diagnosis and treatment of each type of CCA are thought to be different, real-world data studies on the current practice are limited. Therefore, this study was designed to capture the current practice of diagnosing and treating perihilar CCA in Korea. Methods: We conducted a survey using an online platform. The questionnaire consisted of 18 questions designed to evaluate the current practice of diagnosing and treating perihilar CCA in Korea. The targets of this survey were biliary endoscopists who are members of the Korean Pancreatobiliary Association. Results: In total, 119 biliary endoscopists completed the survey. Of the respondents, 89.9% thought that the use of the International Classification of Diseases, 11th Revision (ICD-11) system is necessary to classify CCA. Approximately half of the respondents would recommend surgery or chemotherapy until patients were 80 years of age. For the pathological diagnosis of CCA, endoscopic retrograde cholangiopancreatography with biopsy was the most preferred modality. Routine preoperative biliary drainage was performed by 44.5% of the respondents. For operable CCAs, 64.7% of the respondents preferred endoscopic biliary drainage using plastic stents. For palliative biliary drainage, 69.7% of the respondents used plastic stents. For palliative endoscopic biliary drainage using metal stents, 63% of the respondents preferred the stent-in-stent method. Conclusions A new coding system using the ICD-11 is needed for classifying CCAs. Guidelines for diagnosing and treating CCA based on the clinical situation in Korea are needed.

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: 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: Although there are many studies on prealbumin in individual diseases such as malignant or inflammatory diseases, there are few comparative studies. This study aimed to compare the clinical differences between prealbumin levels in cholecystitis and pancreaticobiliary malignancies and investigate the clinical impact of low prealbumin levels in pancreaticobiliary malignancies. Methods: From June 2021 to September 2021, 61 patients who had undergone surgery for various pancreaticobiliary diseases were enrolled in this study, and their clinicopathological data were retrospectively analyzed. Results: Many elderly patients with malignant diseases had poor American Society of Anesthesiologists (ASA) scores, significantly lower albumin and prealbumin levels, and higher systemic immune inflammation indices. The low prealbumin group was older; had poorer ASA scores; and had significantly lower body mass index and hemoglobin and albumin levels and higher systemic immune inflammation indices than the normal prealbumin group. In malignant diseases, the low prealbumin group had significantly lower body mass index and hemoglobin levels and a tendency toward more advanced disease (lymph node and distant metastasis). Conclusion Preoperative low prealbumin levels had an area under the receiver operator characteristic curve of 0.69, suggesting that it may be useful for predicting pancreaticobiliary malignancies. Prealbumin levels were lower in malignant diseases, possibly related to poor nutritional status and systemic immune inflammation. Low prealbumin levels may predict the risk of more advanced disease.