Diagnostic cholangioscopy for surgical planning of extrahepatic cholangiocarcinoma.

The recent clinical outcomes of multi-regimen chemotherapy included prolonged survival and a high rate of conversion to surgery in Asian patients with advanced biliary tract cancer. The ability of single-operator cholangioscopy (SOC) to detect and stage extrahepatic cholangiocarcinoma (CCC) in intraductal lesions is becoming more important in determining the extent of surgery. The aim of this study was to evaluate the role of SOC in surgical planning for extrahepatic CCC.

Noncontrast Magnetic Resonance Imaging Vs Ultrasonography for Hepatocellular Carcinoma Surveillance: A Randomized, Single-Center Trial.

Background & Aims: This study aimed to compare ultrasonography (US) and noncontrast magnetic resonance imaging (MRI) in the surveillance of hepatic malignancy.

Methods: We conducted a randomized, nonblinded, single-center trial at a single center in South Korea. Eligible individuals were aged 20 to 70 years with liver cirrhosis, Child-Pugh class A, and no history of liver cancer or other recent malignancy.

Association between incidental perirenal fat stranding on CT and metabolic syndrome in otherwise healthy adults.

Purpose: To investigate the association between metabolic syndrome and perirenal fat stranding (PRFS), which is defined as linear or curvilinear soft tissue densities in the perirenal fat on computed tomography (CT).

Material And Methods: Adults who had abdominal CT for health screening at a single institution between October 2022 and March 2023 were included retrospectively. Two radiologists assessed the extent of PRFS for each CT and graded it as absent, mild/moderate, and severe.

CT Evaluation of Long-Term Changes in Common Bile Duct Diameter after Cholecystectomy.

Purpose: The present study aimed to investigate the frequency and extent of compensatory common bile duct (CBD) dilatation after cholecystectomy, assess the time between cholecystectomy and CBD dilatation, and identify potentially useful CT findings suggestive of obstructive CBD dilatation.

Materials And Methods: This retrospective study included 121 patients without biliary obstruction who underwent multiple CT scans before and after cholecystectomy at a single center between 2009 and 2011. The maximum short-axis diameters of the CBD and intrahepatic duct (IHD) were measured on each CT scan.

Distinct Characteristics and Changes in Liver Function of Patients with Hepatocellular Carcinoma Treated with Atezolizumab Plus Bevacizumab for More Than 1 Year.

Purpose: Since 2020, atezolizumab plus bevacizumab (Ate/Bev) has been the standard first-line therapy for unresectable hepatocellular carcinoma (HCC), but long-term treatment studies are limited. This study evaluated the clinical characteristics and effects of Ate/Bev for over 1 year.

Materials And Methods: This study included patients with unresectable HCC treated with first-line Ate/Bev between May 2020 and April 2022.

Additional nodules detected using EOB-MRI in patients with resectable single hepatocellular carcinoma: an implication for active treatment strategy.

Background/aim: Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOBMRI) further enhances the identification of additional hepatic nodules compared with computed tomography (CT) alone; however, the optimal treatment for such additional nodules remains unclear. We investigated the long-term oncological effect of aggressive treatment strategies for additional lesions identified using EOB-MRI in patients with hepatocellular carcinoma (HCC).

Methods: Data from 522 patients diagnosed with solitary HCC using CT between January 2008 and December 2012 were retrospectively reviewed.

Suspicious findings observed retrospectively on CT imaging performed before the diagnosis of pancreatic cancer.

Background: Few studies have focused on computed tomography findings before a pancreatic cancer diagnosis. We aimed to investigate the prediagnostic computed tomography findings of patients who had undergone computed tomography within the prediagnostic period of their pancreatic cancer diagnosis.

Methods: Between January 2008 and December 2019, 27 patients who underwent contrast-enhanced abdominal or chest computed tomography including the pancreas within 1 year of a pancreatic cancer diagnosis were enrolled in this retrospective study.

Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis.

Background And Purpose: To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis.

Materials And Methods: The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume.

Purpose: To examine the effect of lung volume on the size and volume of pulmonary subsolid nodules (SSNs) measured on CT.

Materials And Methods: A total of 42 SSNs from 31 patients were included. CT examination was first performed at total lung capacity (TLC), and a section containing the nodule was additionally scanned at tidal volume (TV).

Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography.

Objectives: To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner.

Methods: We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images.

Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant.

Purpose: In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant.

Methods: From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification).

An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets.

Bowel Peristalsis Artifact on Dual-Energy CT: In Vitro Study on the Influence of Different Dual-Energy CT Platforms and Enteric Contrast Agents.

. The value of dual-energy CT (DECT) for bowel wall assessment is increasingly recognized. Although technical improvements reduce peristalsis artifact in conventional CT, the effects of peristalsis on DECT image reconstructions remain poorly studied.

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results.

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) "Simple" task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) "difficult" task, low- [n = 163] vs.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma.

Purpose: We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

Methods: This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained.

Prediction of the risk of developing hepatocellular carcinoma in health screening examinees: a Korean cohort study.

Background: Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data.

Methods: The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146).

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.

Objectives: To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging.

Methods: A total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed.

Recognizing and Minimizing Artifacts at Dual-Energy CT.

Dual-energy CT (DECT) is an exciting innovation in CT technology with profound capabilities to improve diagnosis and add value to patient care. Significant advances in this technology over the past decade have improved our ability to successfully adopt DECT into the clinical routine. To enable effective use of DECT, one must be aware of the pitfalls and artifacts related to this technology.

Development and validation of a nomogram to predict pulmonary function and the presence of chronic obstructive pulmonary disease in a Korean population.

Background: Early suspicion followed by assessing lung function with spirometry could decrease the underdiagnosis of chronic obstructive pulmonary disease (COPD) in primary care. We aimed to develop a nomogram to predict the FEV/FVC ratio and the presence of COPD.

Methods: We retrospectively reviewed the data of 4241 adult patients who underwent spirometry between 2013 and 2019.

Dual-Energy CT Images: Pearls and Pitfalls.

Dual-energy CT (DECT) is a tremendous innovation in CT technology that allows creation of numerous imaging datasets by enabling discrete acquisitions at more than one energy level. The wide range of images generated from a single DECT acquisition provides several benefits such as improved lesion detection and characterization, superior determination of material composition, reduction in the dose of iodine, and more robust quantification. Technological advances and the proliferation of various processing methods have led to the availability of diverse vendor-based DECT approaches, each with a different acquisition and image reconstruction process.

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer.

Purpose: To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC).

Methods: A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI.