Ji Woo Park

Abstract In this prospective, multi-reader, multi-vendor study, we evaluated the performance of a commercially available deep neural network (DNN)–based MR image reconstruction in enabling accelerated 2D fast spin-echo (FSE) knee imaging. Forty-five subjects were prospectively enrolled and randomly divided into three 3T MRIs. Conventional 2D FSE and accelerated 2D FSE sequences were acquired for each subject, and the accelerated FSE images were reconstructed and enhanced with DNN–based reconstru

BACKGROUND: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on

Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify pa

Si1−xGe x (:B) epitaxial layers were deposited by using reduced pressure chemical vapor deposition with SiH4, GeH4, and B2H6 source gases, and the dependences of the surface roughness of undoped Si1−xGe x on the GeH4 flow rate and of Si1−xGe x :B on the B2H6 flow rate were investigated. The root-mean-square (RMS) roughness value of the undoped Si1−xGe x at constant thickness increased gradually with increasing Ge composition, resulting from an increase in the amplitude of the wavy surface before

We investigated whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone, using pathologically confirmed bone metastasis as the reference standard, in patients with gastric cancer. In this retrospective study, 96 patients (mean age, 58.4 ± 13.3 years; range, 28-85 years) with pathologically confirmed bone metastasis in iliac bones were included. The dataset was categorized into three feature sets: (1) mean and

Generative AI-including Generative Adversarial Networks, diffusion models, Large Language Models (LLMs), and more recently, vision-language models-is increasingly utilized in clinical practice for musculoskeletal imaging tasks such as disease diagnosis, image enhancement, image reconstruction, electronic health record summarization, and radiologic report generation. Integrating these technologies into radiology workflows can significantly advance radiology report generation, structured reporting

F] fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to predict small-cell lung cancer (SCLC) prognosis. This retrospective study included 520 patients with SCLC (mean age ± standard deviation, 67 ± 5.6 years; 84.8% men) who underwent PET/CT for staging. Of these, 364 scans were used for training (n = 291) and internal (n = 73) tests, while 156 other scans were used for external testing. Clinical data (age, sex, and stage) were reviewed. Volumes of interest were ma

The identification of patient-specific cancer driver genes plays a crucial role in the development of personalized cancer treatment and drug development. Several computational methods have been proposed for identifying patient-specific cancer driver genes, most of which rank driver genes ac-cording to scores calculated from various gene or protein network information. In this paper, we propose a machine learning model for more accurate identification of patient-specific cancer driver genes. The

issue of the Journal of the Korean Society of Radiology, which explores the early clinical application of low-dose chest CT (LDCT) as a practical, lower-risk alternative to routine CT for sarcopenia assessment.In their prospective study, the authors enrolled 100 patients who underwent both routine-dose contrast-enhanced chest CT and LDCT within a six-month interval.Addressing a notable gap in clinical practice, this study investigates whether LDCT-already widely adopted for lung cancer screening

Purpose: This study compared 3D-T1 high resolution isovolumetric examination (3D-THRIVE) multiplanar reconstruction (MPR) imaging of shoulder magnetic resonance arthrography (MRA) with conventional MR images and validated the diagnostic agreements of isovolumetric MRA with and without compressed sensing (CS).Materials and Methods: Seventy-three patients who underwent shoulder MRA, including image sets of conventional 2D fast spin echo (FSE) sequences and isotropic 3D-THRIVE sequences with and wi

The risks arising from precipitation are immediate and broad. The Korea Meteorological Administration also carries out predictions and observations on precipitation in Korea. Still, the results are inaccurate due to cases where precipitation in the north is not well matched or incorrect observations are made due to difficulties in identifying the surface. We intend to construct accurate precipitation data by analyzing a machine-learning model. If highly influential precipitation data are properl

<div>AbstractPurpose:<p>This open-label, phase IB/II study evaluated the efficacy and safety of standard-of-care doxorubicin combined with durvalumab [a programmed death 1 ligand (PD-L1) immune checkpoint inhibitor] in patients with advanced anthracycline-naïve soft tissue sarcoma (STS) and identified patients who would most likely benefit from this combination treatment.</p>Patients and Methods:<p>This trial (NCT03802071) included patients with metastatic and/or recurren