Yonsei University · 医学
Professor Hong In Yoon's research lab specializes in radiation oncology and multimodal cancer treatment strategies, with a focus on improving outcomes for patients with advanced or metastatic cancers. The lab investigates the role of radiotherapy in combination with chemotherapy, surgery, and targeted therapies across various malignancies, including cervical cancer, hepatocellular carcinoma (HCC), diffuse intrinsic pontine glioma (DMG), and locally advanced rectal cancer (LARC). Key research directions include optimizing treatment protocols to enhance tumor response, reduce toxicity, and improve resectability through strategic sequencing of therapies. The lab also explores predictive biomarkers—such as ICG-R15 for radiation-induced liver toxicity and H3K27M mutations for DMG diagnosis—to personalize treatment approaches.
Figures are computed from collected data and may differ slightly.
Our institutional experiences showed that EFRT was an effective treatment for cervical cancer patients with PAN metastasis. The addition of chemotherapy to EFRT seems to have uncertain survival benefit with higher hematologic toxicity.
These results suggest that pre-RT ICG-R15 could be a useful factor in predicting radiation hepatotoxicity in HCC patients treated with RT.
The detection of H3K27M mutation is the most important diagnostic criteria for DMG. Combination of surgery (if amenable to surgery), radiotherapy, and chemotherapy based on comprehensive multidisciplinary discussion can be considered as the treatment options for DMG.
These findings demonstrated that a strong possibility that upfront chemotherapy and short-course RT with delayed surgery are an effective alternative treatment for LARC with potentially resectable distant metastasis, owing to achievement of pathologic down-staging, R0 resection, and favorable compliance and toxicity, despite the long treatment duration.
Our findings show that bladder volume could be maintained more consistently during RT by protocol-based management using a bladder scan.
Recently, several efforts have been made to develop the deep learning (DL) algorithms for automatic detection and segmentation of brain metastases (BM). In this study, we developed an advanced DL model to BM detection and segmentation, especially for small-volume BM. From the institutional cancer registry, contrast-enhanced magnetic resonance images of 65 patients and 603 BM were collected to train and evaluate our DL model. Of the 65 patients, 12 patients with 58 BM were assigned to test-set fo
RT-involving multimodality treatments can enhance the overall therapeutic successes for advanced liver-confined HCC and also provide potential cures to some patients via conversion to a resectable condition.
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