Ji-Seok Jang
Yonsei University · Medicine
About the Lab
Professor Ji-Seok Jang's research lab specializes in radiation oncology and medical physics, focusing on improving treatment accuracy and outcomes in cancer patients through advanced imaging, treatment planning, and artificial intelligence. The lab investigates risk factors for treatment-related complications such as lymphedema and explores the integration of deep learning for automated contouring in radiotherapy. A key focus is on optimizing radiotherapy for rare and aggressive cancers, including mucosal melanoma and rectal cancer, particularly through combined modality approaches involving immunotherapy and stereotactic radiotherapy.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15OBJECTIVE: The aim of this study was to identify the comprehensive risk factors for lymphedema, thereby enabling a more informed multidisciplinary treatment decision-making. SUMMARY BACKGROUND DATA: Lymphedema is a serious long-term complication in breast cancer patients post-surgery; however, the influence of multimodal therapy on its occurrence remains unclear. METHODS: We retrospectively collected treatment-related data from 5549 breast cancer patients who underwent surgery between 2007 and 2
BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contour
Until recently, chemotherapy with a combination of anthracycline and cytosine arabinoside was the most effective treatment for acute promyelocytic leukaemia (APL) ( Fenaux & Degos, 1996). This can be safely combined with anti-fibrinolytic drugs such as tranexamic acid for the attempted prophylaxis of haemorrhage without the occurrence of thrombolytic complications. However, several studies have now demonstrated that all-trans retinoic acid (ATRA) can induce complete remission of APL with progres
While atlas segmentation (AS) has proven to be a time-saving and promising method for radiation therapy contouring, optimal methods for its use have not been well-established. Therefore, we investigated the relationship between the size of the atlas patient population and the atlas segmentation auto contouring (AC) performance. A total of 110 patients’ head planning CT images were selected. The mandible and thyroid were selected for this study. The mandibles and thyroids of the patient populatio
We did not observe that preoperative CRT increased the risk of postoperative AL after LAR in patients with rectal cancer, using propensity score matching analysis.
<b>Objective:</b> Mucosal melanoma is an aggressive malignancy with a poor response to conventional therapies. The efficacy of radiotherapy (RT), especially combined with immune checkpoint inhibitors (ICIs), for this rare melanoma subtype remains unknown. We investigated the reciprocal effect of RT and ICI on mucosal melanoma patients. <b>Materials and Methods:</b> We identified 23 patients with 31 tumors who were treated with RT between July 2008 and February 2017. All patients received RT for
PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. METHODS: Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice sim
Research Areas
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