Yonsei University · 医学
Professor Sung Jun Ahn's research lab specializes in radiomics and advanced neuroimaging, focusing on the non-invasive characterization of brain tumors, metastases, and neurological disorders using quantitative MRI techniques. The lab investigates the correlation between imaging phenotypes and underlying molecular or biological subtypes, particularly in brain metastases from lung and breast cancer, as well as in stroke and major depressive disorder. By integrating machine learning with radiological imaging, the lab aims to improve diagnostic accuracy, predict treatment response, and uncover pathophysiological mechanisms through spatial and functional brain network analysis. Their work emphasizes the clinical translation of advanced MRI sequences and radiomic biomarkers for precision medicine.
Figures are computed from collected data and may differ slightly.
ADC values were significantly dependent on ROI method. ADCs from the whole tumor volume had the most reproducible results. ADCmean from the whole tumor volume may aid in differentiating between lymphoma and GBM. However, multi-modal imaging approaches are recommended than ADC alone for differentiation.
Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validat
The aim of our study was to test the hypothesis that the spatial distribution of breast cancer brain metastases (BM) differ according to their biological subtypes. MR images of 100 patients with BM from primary breast cancer were retrospectively reviewed. Patients were divided according to the biological subtype of the primary tumor, (triple-negative: 24, HER2 positive: 48, luminal: 28). All images marked with BMs were standardized to the human brain MRI atlas provided by the Montreal Neurologic
In MCA stroke, FLAIR hyperintense vessels were more prominent on T2-PROPELLER-FLAIR compared with T2-FLAIR. In addition, FLAIR hyperintense vessels on T2-PROPELLER-FLAIR have a significantly higher sensitivity for predicting large-artery stenosis than they do on T2-FLAIR. Moreover, the areas showing FLAIR hyperintense vessels on T2-PROPELLER-FLAIR were more closely associated with perfusion abnormality than those on T2-FLAIR.
While contrast-enhanced fluid-attenuated inversion recovery (FLAIR) has long been regarded as an adjunct sequence to evaluate leptomeningeal disease in addition to contrast-enhanced T1-weighted imaging, it is gradually being used for more diverse pathologies beyond leptomeningeal disease. Contrast-enhanced FLAIR is known to be highly sensitive to low concentrations of gadolinium within the fluid. Accordingly, recent research has suggested the potential utility of contrast-enhanced FLAIR in vario
Our findings suggest a complicated dysfunction of networks between cortical-subcortical circuits in MDD. In addition, increased GMD in postcentral gyrus and a possible reduction of GMD in the orbitofrontal cortex of MDD patients with abuse subgroup may be associated with abnormalities of body perception and emotional dysregulation.
Quantitative analysis of T1-weighted DCE-MRI using hotspot ROI may provide a better histologic match than whole transverse section ROI. Within the hotspots, K(trans) and K(ep) tend to have a reverse correlation with MVD in this colon cancer mouse model.
Small brain metastases showed higher signal intensity than large brain metastases on the DD Gd enhanced 3D T2 FLAIR images. DD Gd enhanced 3D T2 FLAIR imaging may have a complementary role to 3D T1 GRE for evaluating small brain metastases.
Despite good overall diagnostic accuracy, coronary CTA in this group of patients was limited by low specificity. However, CTA was a better predictor of obstructive CHD compared with clinical predictors, and it avoided unnecessary ICA, even in patients with extensive coronary artery calcification.
In this study, SUV was shown to be correlated with K(trans) in advanced HCCs; the higher the glucose metabolism a tumor had, the lower the perfusion it had, which might help in guiding target therapy.
Flow velocities, FA of refocusing pulse, TE, and PROPELLER technique significantly affected the degree of FHVs. Optimized MR parameters should be used consistently in future studies, which may provide more reliable results.
Focal eosinophilic infiltration showed mixed hypointensity, irregular margins, and nonspherical shapes with excellent lesion-to-liver contrast in the hepatobiliary phase of the gadoxetic acid-enhanced MRI. Diffusion-weighted imaging and ADCs may not be helpful for characterizing these benign hepatic lesions.
Our aims for this study were to investigate the relationship between diffusion weighted image (DWI) parameters of brain metastases (BMs) and biological markers of breast cancer, and moreover, to assess whether DWI parameters accurately predict patient outcomes. DWI data for 34 patients with BMs from breast cancer were retrospectively reviewed. Apparent diffusion coefficient (ADC) histogram parameters were calculated from all measurable BMs. Two region of interest (ROI) methods are used for the a
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