Yonsei University · 医学
Professor Chae Jung Park's research lab specializes in radiomics and medical image analysis, focusing on integrating advanced imaging features with machine learning and clinical data to improve diagnostic accuracy and prognosis prediction in neurological and oncological conditions. The lab investigates brain tumors, including gliomas and meningiomas, as well as neurodegenerative diseases like Parkinson’s disease, aiming to identify early biomarkers for dementia and tumor recurrence. Their work emphasizes methodological rigor, adhering to international standards such as RQS, TRIPOD, and IBSI to ensure clinical translatability.
Figures are computed from collected data and may differ slightly.
MR imaging features integrated with machine learning classifiers may predict a subset of <i>IDH</i> wild-type lower-grade gliomas that carry molecular features of glioblastoma.
The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.
PURPOSE: The purpose of this study was to retrospectively investigate the reliability of breast ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) final assessment in mammographically negative patients with pathologic nipple discharge, and to determine the clinical and ultrasonographic variables associated with malignancy in this group of patients.\n\nMETHODS: A total of 65 patients with 67 mammographically negative breast lesions that were pathologically confirmed through US-gui
Cognitive impairment in Parkinson's disease (PD) severely affects patients' prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008-July 2017, follow-up >5 years) were in
Radiomics significantly contributes added value in predicting recurrence when integrated with the clinicopathological features in patients with grade 2 meningiomas. Furthermore, the combined model can be applied to identify high-risk patients who require ART.
Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors.
When US findings of thyroid nodules are assessed according to the 2015 ATA guidelines, nondiagnostic thyroid nodules with very-low- or low-suspicion US patterns can be followed up with US. Nondiagnostic nodules with intermediate or highly suspicious US patterns should be evaluated with repeat US-guided fine-needle aspiration biopsy.
CT-tumor depth and size could be used as independent predictors for prognosis. Preoperative CT can be used for prognostic stratification to select high risk patients for whom neoadjuvant therapies might be considered.
Contrast-enhanced 3D gradient recalled-echo was diagnostically superior in identifying neoplastic thyroid cartilage invasion compared with 2D spin-echo T1WI in patients with laryngohypopharyngeal cancer, and therefore, may provide more accurate preoperative staging.
CS may be useful for intracranial VW-MRI as it allows for larger scan coverage with slightly shorter scan time without compromising image quality.
Open papers in the app to read, cite, and organize with AI.
