Jong-Jin Yoon

The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

Background: A deep convolutional neural network (DCNN) model that predicts the degree of arteriovenous fistula (AVF) stenosis and 6-month primary patency (PP) based on AVF shunt sounds was developed, and was compared with various machine learning (ML) models trained on patients' clinical data. Methods: Forty dysfunctional AVF patients were recruited prospectively, and AVF shunt sounds were recorded before and after percutaneous transluminal angioplasty using a wireless stethoscope. The audio fil

BACKGROUND AND AIMS: Differences in combined hepatocellular-cholangiocarcinomas (cHCC-CCAs) arising in high-risk patients with or without liver cirrhosis have not been elucidated. This study aimed to compare the clinicopathologic and imaging characteristics of cHCC-CCAs in patients with or without cirrhosis and to determine the prognostic factors for recurrence-free survival (RFS) after curative resections of single cHCC-CCAs. METHODS: This retrospective study included 113 patients with surgical

Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

BACKGROUND: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared. METHODS: In total, 155 facial expressions from p

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology and interpretation of liver imaging. The association between the LI-RADS category and tumor recurrence in patients with intrahepatic cholangiocarcinomas (iCCAs) has not yet been evaluated in a multicenter study. PURPOSE: To retrospectively investigate the preoperative clinical and imaging features associated with recurrence-free survival (RFS) after curative resection of

Abstract Background and Aims Kidney radiomics has been used to develop more accurate diagnostic tools of renal tumor and predict outcomes. However, radiomics studies for diabetic kidney disease (DKD) remain few. In this light, we hypothesized that computed tomography (CT) radiomics features could differentiate DKD from normal kidneys and assess the severity of DKD. Method This retrospective study included 343 subjects with type 2 diabetes mellitus (T2DM) (male 65.5%, mean age 63.6±14.8) and 90 h

This multi-center, retrospective study focused on periampullary cancer patients undergoing MRI for hepatic metastasis and abscess differentiation. T1-weighted, T2-weighted, and arterial phase images were utilized to create radiomics models. In the training-set, 112 lesions in 54 patients (median age [IQR, interquartile range], 73 [63-80]; 38 men) were analyzed, and 123 lesions in 55 patients (72 [66-78]; 34 men) comprised the validation set. The T1-weighted + T2-weighted radiomics model showed t

Motivation: Prostate cancer is the leading male malignancy in South Korea, with BRCA1/2 mutations critical for PARP inhibitor use in personalized therapy. Goal(s): Build a model to predict BRCA mutations using MRI, aiming to reduce NGS costs and improve personalized treatment. Approach: In a retrospective study of 204 patients, BRCA mutations were identified with NGS. T2-weighted MRIs were segmented, and 1,422 radiomic features extracted. Feature selection included ICC, LASSO, and classifiers (K

Motivation: Prostate cancer has diverse genetic subtypes affecting prognosis and treatment response. Goal(s): Develop a machine learning model to predict four genetic subtypes (Luminal A, Luminal S, AVPC-I, ACPV-M) using radiomic features from T2-weighted MRI, supporting personalized treatment. Approach: In 195 patients, RNA sequencing identified subtypes. T2-weighted MRIs were segmented, and 1,422 radiomic features were extracted. Feature selection used ICC, and classification models were train