Sungwook Seo
Sungkyunkwan University · Medicine
About the Lab
Professor Sungwook Seo's research lab specializes in translational oncology and musculoskeletal oncology, focusing on improving diagnostic accuracy and treatment strategies for rare and aggressive sarcomas such as chondrosarcoma and myxoid liposarcoma. The lab integrates advanced imaging techniques, including whole-body MRI, with molecular biology approaches—such as siRNA-mediated gene silencing—to enhance radiosensitivity and understand metastatic patterns. A key emphasis is on developing personalized prognostic models using genomic biomarkers, particularly in epithelial ovarian cancer (EOC), and advancing artificial intelligence literacy in medical education to support future clinical decision-making. The lab also investigates musculoskeletal complications in genetic disorders like Prader-Willi syndrome, advocating for early and systematic orthopedic screening.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Our novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.
Case series, Level IV.
The main treatment for chondrosarcoma is surgical resection with a wide margin. However, there are certain chondrosarcomas, such as those found in the pelvis and the spine, which cannot be resected adequately with surgery alone. Unfortunately, most chondrosarcomas are resistant to radiation and chemotherapy. Radiation and chemotherapy are thought to kill chondrosarcoma cells by inducing apoptosis, or programmed cell death. In this article, we hypothesize that antiapoptotic gene silencing enhance
PURPOSE: Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. METHOD: An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online question
This study shows a high prevalence of spinal deformity, limb malalignment, and foot abnormality in PWS. The prevalences of musculoskeletal abnormalities were not found to be affected by age, genotype, or obesity. However, several musculoskeletal abnormalities were found to be correlated with each other, namely, scoliosis and limb malalignment, kyphotic deformity, and foot abnormality or severe limb malalignment. The authors recommend that pediatric orthopaedic surgeons conduct systemic clinical
No feasible method currently exists to evaluate systemic metastasis in patients with myxoid liposarcoma. The purpose of this study was to determine the feasibility of performing whole-body magnetic resonance imaging (MRI) to detect metastatic myxoid liposarcoma. From June 2008 to May 2010, all patients who were newly diagnosed with myxoid liposarcomas at our institution underwent whole-body MRI along with other conventional imaging methods. We divided the whole body into 38 sections (7 soft tiss
BACKGROUND: We retrospectively reviewed the outcomes of desmoid tumor (DT) patients treated by surgical excision. METHODS: Among 155 consecutive patients, 119 patients satisfied our inclusion criteria. The mean follow-up duration was 82 months. Age, gender, location, size, depth, resection margin, adjuvant radiotherapy, and excision history were analyzed for the outcomes. RESULTS: The recurrence-free survival (RFS) rates were 75% at 5 years and 72% at 10 years. Twenty-seven (93.1%) out of 29 rec
PURPOSE: Gastric cancer (GC) is the third-leading cause of cancer-related deaths. Several pivotal clinical trials of adjuvant treatments were performed during the previous decade; however, the optimal regimen for adjuvant treatment of GC remains controversial. PATIENTS AND METHODS: We developed a novel deep learning-based survival model (survival recurrent network [SRN]) in patients with GC by including all available clinical and pathologic data and treatment regimens. This model uses time-seque
BACKGROUND: Many studies have reported on the surgical outcomes of soft tissue sarcoma. However, there was no longitudinal cohort study. Because time is the most valuable factor for functional recovery, adjusting time value was the key for finding the causal relationship between other risk factors and postoperative function. Therefore, existing cross-sectional studies can neither fully explain the causal relationship between the risk factors and the functional score nor predict functional recove
Research Areas
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