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Sung-yeon Song

Korea University · Medicine

About the Lab

Professor Sung-yeon Song's research lab specializes in advanced breast imaging, focusing on optimizing MRI techniques—particularly diffusion-weighted imaging, perfusion modeling, and computer-aided detection—for improved diagnosis and preoperative prediction in breast cancer. The lab investigates multiparametric and radiomic features from MRI to enhance the accuracy of assessing tumor burden, lymph node involvement, and metastatic risk, especially in aggressive subtypes like triple-negative and HER2-positive breast cancer. Key research directions include the development and validation of quantitative imaging biomarkers using 3T MRI, IVIM-DWI, and DCE-MRI to support personalized treatment planning and reduce diagnostic uncertainty. The lab also emphasizes clinical translation by integrating radiological findings with pathological outcomes to improve early detection and prognostic modeling.

breast MRIradiomicsdiffusion-weighted imagingperfusion imagingcomputer-aided detection

Research Overview

Papers
104
Total Citations
1,387
Papers (5y)
30
Primary Field
Medicine

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
30total
2022
2023
2024
2025
2026
Citations per year (5y)
192total
20222023202420252026

Selected Papers

15
1
Article|53 citations·2010
Malignant glomus tumor of the stomach with multiorgan metastases: Report of a case
Sung Eun Song, Chang Hee Lee, Kyeong Ah Kim, Hyun Joo Lee, Cheol Min Park
SJR Q2Surgery Today
Pathology and Forensic MedicineMedicine
2
Article|53 citations·2015
Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study
Sung Eun Song, Bo Kyoung Seo, Kyu Ran Cho, Ok Hee Woo, Gil Soo Son, Chulhan Kim, Sung Bum Cho, Soon-Sun Kwon
SJR Q1Cancer ImagingOA

BACKGROUND: We aimed to investigate the efficacy of computer-aided detection (CAD) for MRI in the assessment of tumor extent, lymph node status, and multifocality in invasive breast cancers in comparison with other breast imaging modalities. METHODS: Two radiologists measured the maximum tumor size, as well as, analyzed lymph node status and multifocality in 86 patients with invasive breast cancers using mammography, ultrasound, CT, MRI with and without CAD, and 18-fludeoxyglucose positron emiss

Radiology, Nuclear Medicine and ImagingMedicine
3
Article|42 citations·2017
MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case–control study
Sung Eun Song, Sung Ui Shin, Hyeong‐Gon Moon, Han Suk Ryu, Kwangsoo Kim, Woo Kyung Moon
SJR Q1Breast Cancer Research and Treatment
Radiology, Nuclear Medicine and ImagingMedicine
4
Article|35 citations·2017
Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI
Sung Eun Song, Eunkyung Park, Kyu Ran Cho, Bo Kyoung Seo, Ok Hee Woo, Seung Pil Jung, Sung Bum Cho
SJR Q1European Radiology
Radiology, Nuclear Medicine and ImagingMedicine
5
Article|35 citations·2018
Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis
Sung Eun Song, Kyu Ran Cho, Bo Kyoung Seo, Ok Hee Woo, Kyong Hwa Park, Yo Han Son, Robert Grimm
SJR Q1Journal of Magnetic Resonance Imaging

BACKGROUND: As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI. PURPOSE: To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CA

Radiology, Nuclear Medicine and ImagingMedicine
6
Article|34 citations·2020
Simultaneous Multislice Readout‐Segmented Echo Planar Imaging for Diffusion‐Weighted MRI in Patients With Invasive Breast Cancers
Sung Eun Song, Ok Hee Woo, Kyu Ran Cho, Bo Kyoung Seo, Yo Han Son, Robert Grimm, Wei Liu, Woo Kyung Moon
SJR Q1Journal of Magnetic Resonance Imaging

BACKGROUND: In diffusion-weighted imaging (DWI) of breast MRI, simultaneous multislice acceleration techniques can be used for readout-segmented echo planar imaging (rs-EPI) to shorten the scan time. PURPOSE: To compare the image quality, apparent diffusion coefficient (ADC) value, and scan time of rs-EPI and simultaneous multislice rs-EPI (SMS rs-EPI) sequences. STUDY TYPE: Retrospective. SUBJECTS: In all, 134 consecutive women (mean age: 55.3 years) with invasive breast cancer who underwent pr

Radiology, Nuclear Medicine and ImagingMedicine
7
Article|32 citations·2021
Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer
Sung Eun Song, Kyu Ran Cho, Yongwon Cho, Kwangsoo Kim, Seung Pil Jung, Bo Kyoung Seo, Ok Hee Woo
SJR Q1European Radiology
Cancer ResearchBiochemistry, Genetics and Molecular Biology
8
Article|27 citations·2015
Undiagnosed Breast Cancer: Features at Supplemental Screening US
Sung Eun Song, Nariya Cho, A Jung Chu, Sung Ui Shin, Ann Yi, Su Hyun Lee, Won Hwa Kim, Min Sun Bae, Woo Kyung Moon
SJR Q1Radiology

At supplemental screening breast US, close attention should be paid to the presence of a margin that is not circumscribed, and multiple lesions should be separately assessed to reduce the number of missed breast cancers.

Pathology and Forensic MedicineMedicine
9
Article|27 citations·2010
MR imaging features of uterine adenomyomas
Sung Eun Song, Deuk Jae Sung, Beom Jin Park, Min Ju Kim, Sung Bum Cho, Kyeong Ah Kim
Abdominal Imaging
Reproductive MedicineMedicine
10
Article|23 citations·2023
Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI
Sung Eun Song, Ok Hee Woo, Yongwon Cho, Kyu Ran Cho, Kyong Hwa Park, Ju Won Kim
SJR Q1Academic RadiologyOA

A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.

Radiology, Nuclear Medicine and ImagingMedicine
11
Article|21 citations·2016
MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study
Sung Eun Song, Min Sun Bae, Jung Min Chang, Nariya Cho, Han Suk Ryu, Woo Kyung Moon
SJR Q3Acta Radiologica

Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- a

OncologyMedicine
12
Article|17 citations·2012
Classification of Metastatic versus Non-Metastatic Axillary Nodes in Breast Cancer Patients: Value of Cortex-Hilum Area Ratio with Ultrasound
Sung Eun Song, Bo Kyoung Seo, Seung Hwa Lee, Ann Yie, Ki Yeol Lee, Kyu Ran Cho, Ok Hee Woo, Sang Hoon, Baek Hyun Kim
SJR Q2Journal of Breast CancerOA

We recommend the CH area ratio of an axillary lymph node on ultrasound as a quantitative indicator for the classification of lymph nodes. The CH area ratio can improve diagnostic performance when compared with the LT axis ratio or blood flow pattern.

Cancer ResearchBiochemistry, Genetics and Molecular Biology
13
Article|14 citations·2019
Kinetic Features of Invasive Breast Cancers on Computer-Aided Diagnosis Using 3T MRI Data: Correlation with Clinical and Pathologic Prognostic Factors
Sung Eun Song, Kyu Ran Cho, Bo Kyoung Seo, Ok Hee Woo, Seung Pil Jung, Deuk Jae Sung
SJR Q1Korean Journal of RadiologyOA

Of the CAD-assessed kinetic features, higher peak enhancement may correlate with higher histologic grade, and higher delayed-plateau component and angio-volume correlate with higher Ki-67 index. These results support the clinical application of kinetic features in prognosis assessment.

Radiology, Nuclear Medicine and ImagingMedicine
14
Article|14 citations·2017
Diagnostic performances of supplemental breast ultrasound screening in women with personal history of breast cancer
Sung Eun Song, Nariya Cho, Jung Min Chang, A Jung Chu, Ann Yi, Woo Kyung Moon
SJR Q3Acta Radiologica

Background Supplemental breast ultrasonography (US) has been used as a surveillance imaging method in women with personal history of breast cancer (PHBC). However, there have been limited data regarding diagnostic performances. Purpose To evaluate diagnostic performances of supplemental breast US screening for women with PHBC and to compare with those for women without PHBC. Material and Methods Between 2011 and 2012, 12,230 supplemental US exams were performed in 12,230 women with negative mamm

Pathology and Forensic MedicineMedicine
15
Article|10 citations·2020
Preoperative tumor size measurement in breast cancer patients: which threshold is appropriate on computer-aided detection for breast MRI?
Sung Eun Song, Bo Kyoung Seo, Kyu Ran Cho, Ok Hee Woo, Eun Kyung Park, Jaehyung Cha, Seungju Han
SJR Q1Cancer ImagingOA

BACKGROUND: Computer-aided detection (CAD) can detect breast lesions by using an enhancement threshold. Threshold means the percentage of increased signal intensity in post-contrast imaging compared to precontrast imaging. If the pixel value of the enhanced tumor increases above the set threshold, CAD provides the size of the tumor, which is calculated differently depending on the set threshold. Therefore, CAD requires the accurate setting of thresholds. We aimed to compare the diagnostic accura

Radiology, Nuclear Medicine and ImagingMedicine

Research Areas

Radiology, Nuclear Medicine and ImagingPathology and Forensic MedicineSurgeryCancer ResearchArtificial IntelligencePulmonary and Respiratory Medicine

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