Purpose: Topical hemostatic powders may help limit blood loss when applied to a wound early in the course of bleeding. This study evaluated the effectiveness and safety of a hemostatic powder composed of carboxymethyl starch and calcium ions for reducing hemorrhagic complications (specifically hemoglobin loss) after transrectal prostate biopsy. Materials and Methods: In this randomized controlled trial, 95 patients undergoing transrectal prostate biopsy received either hemostatic powder with povidone-iodine–soaked gauze or povidone-iodine–soaked gauze alone. The primary outcome was hemoglobin level measured before biopsy and 1 day after biopsy. Secondary outcomes included the incidence of hematuria, hematochezia, infection, and other complications. Results: Postbiopsy hemoglobin levels did not differ significantly between the control (12.3±1.6 g/dL) and experimental (12.2±1.8 g/dL) groups (p=0.872). In both groups, hemoglobin level decreased significantly from 13.1±1.9 g/dL before biopsy to 12.2±1.8 g/dL after biopsy (p<0.001). Hematuria occurred in 95.1% of the experimental group and 87.2% of the control group (p=0.389). The incidence of hematochezia was significantly lower in the experimental group (18.8%) than in the control group (59.6%) (p<0.001). No significant differences were observed in other complications, including infection, urinary retention, and transfusion requirement. No severe adverse events were reported in either group. Conclusion Application of the hemostatic powder did not prevent postbiopsy hemoglobin decline; however, it significantly reduced the incidence of hematochezia. The procedure appeared safe in this trial, although further studies are needed to identify more effective strategies to prevent cumulative hemoglobin loss after transrectal prostate biopsy.

Background/Aims: Biliary tract cancer (BTC) is a rare malignancy with poor prognosis. We investigated genomic determinants of clinical benefit from gemcitabine, cisplatin, and nab-paclitaxel (GAP) versus gemcitabine and cisplatin (GC) in advanced BTC. Methods: Clinical and genomic data using TruSight Oncology 500 were analyzed from patients treated with GAP (N=198) or GC (N=89) as first-line therapy. Results: With a median follow-up of 33.0 months, GAP modestly improved progression-free survival (PFS) (hazard ratio [HR] 0.764; 95% confidence interval [CI] 0.591–0.989) without significant overall survival (OS) difference compared to GC. Genomic profiling revealed frequent alterations in TP53 (35.2%), KRAS (16.4%), SMAD4 (10.5%), and TNFRSF14 (10.5%), involving RTK/RAS (44.3%), TP53 (41.8%), and PI3K (20.2%) pathways. Single-gene mutations did not predict treatment benefit. However, pathway-level analysis identified PI3K pathway activation as significantly associated with inferior PFS (HR 2.148; 95% CI 1.478–3.124) and OS (HR 2.096; 95% CI 1.413–3.109) in patients receiving GAP, an effect not observed with GC. Importantly, GAP conferred clinical benefit only in patients without PI3K pathway activation, while no survival advantage was seen in those with such alterations (Pinteraction=0.023 for PFS, Pinteraction=0.003 for OS). Similar results were obtained in the independent validation cohort treated with GAP (N=103) or GC (N=64) for BTC. Conclusions Genomic profiling using next-generation sequencing identified PI3K pathway activation as key molecular determinant that differentiates patient outcomes between GAP and GC treatments in advanced BTC.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Pneumocystis jirovecii pneumonia (PJP) is a life-threatening infection commonly observed in immunocompromised patients, necessitating prompt diagnosis and treatment. This review evaluates the diagnostic performance of various tests used for PJP diagnosis through a comprehensive literature review. Additionally, we propose a diagnostic algorithm tailored to non-human immunodeficiency virus immunocompromised patients, considering the specific characteristics of current medical resources in Korea.

Background: The removal of a well-fixed acetabular cup is a challenging, labor-intensive, and time-consuming step during revision hip arthroplasty. Although the advent of the manual osteotome, Explant, has simplified the procedure, it is still a stressful process as it dissipates the surgeon’s strength and time and risks an iatrogenic pelvic fracture. Recently, EZX, a powered tool for extraction of well-fixed acetabular cups with semicircular blade was invented. This study aimed to compare Explant and EZX in an experimental condition for their efficacy and safety. Methods: Cementless acetabular cups were press-fitted to 20 hemipelvic polyurethane models using foam adhesives. Ten cups were removed with each tool for comparison of the elapsed time, loads on the entire hemipelvis, periacetabular strain and temperature, volume of periacetabular bone removed, and diameter of the remaining acetabular rim. Strains and loads were quantitatively assessed using strain gauges and load cells for precise and reliable measurements. Results: The mean duration required to remove a well-fixed cup with EZX was 38.5 seconds (range, 25–55), whereas that with Explant was 543.7 seconds (range, 214–1,051) (p < 0.001). The load on the entire hemipelvis with EZX (mean, 9.1 kgf; range, 6.4–11.3) was 33% lower than that with Explant (mean, 13.6 kgf; range, 9.2–17.1) (p < 0.001). The periacetabular peak strains at the 3 positions with EZX were significantly lower than those with Explant (p < 0.001). The temperature during the removal did not differ significantly between the 2 tools. Although the mean volume of bone loss with Explant was 2.4 mL more than that with EZX (p < 0.001), the mean diameters of the remaining acetabular rim were not significantly different, measuring 54.1 mm with both tools. Conclusions The present experiment revealed that a well-fixed cup could be removed using a powered tool with less strength and time and less load on the entire pelvis. Although the powered tool removed a larger volume of bone, the diameters of the remaining acetabular rims were equivalent. This tool may help surgeons remove well-fixed cups in a short time and reduce the deforming load on the bone around the cup without increasing the size of the subsequent reconstruction cup.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.