장범섭 교수
Bum-Sup Jang
서울대학교 · 의학
연구실 소개
장범섭 교수의 연구실은 뇌신경종양, 특히 간질성 뇌종양의 정밀의료를 목표로 하며, 영상기반 기계학습 및 딥러닝 기반 진단 모델 개발에 주력하고 있습니다. 특히, 뇌종양 환자에서 위약진행(PSiPD)과 재발을 정확히 구분하는 AI 기반 진단 알고리즘의 임상적 적용 가능성을 탐색하고 있으며, 방사선 치료의 효율성을 높이기 위한 종양 미세환경 분석 및 기능적 영상 기반 치료계획 수립 기술도 개발하고 있습니다. 이와 함께 저소득층 환자에서의 예후 불균형 문제에 대한 인식 제고와 임상적 지원 전략 모색도 연구의 한 축을 이룹니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were alloc
Some patients with glioblastoma show a worsening presentation in imaging after concurrent chemoradiation, even when they receive gross total resection. Previously, we showed the feasibility of a machine learning model to predict pseudoprogression (PsPD) versus progressive disease (PD) in glioblastoma patients. The previous model was based on the dataset from two institutions (termed as the Seoul National University Hospital (SNUH) dataset, <i>N</i> = 78). To test this model in a larger dataset,
Low SES at the time of cancer diagnosis is associated with increased risk of OS and CSS in elderly patients. Depending on cancer sites, different patterns of OS and CSS were observed according to SES. Further elucidation of the causes underlying these phenomena is needed along with appropriate support for elderly cancer patients with low SES.
Taken together, PD-L1-high-RR group could potentially benefit from radiotherapy combined with PD-1/PD-L1 blockade and angiogenesis inhibition.
The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chine
The results indicate that the cGAN model used here can generate functional areas from RT planning chest CT images. This could be used for functional image-guided RT planning, for example, to spare patients' lung function without additional imaging modalities and costs. Additional studies are needed with many more training and test sets.
Abstract Background There are only limited data on the failure patterns after surgical resection for duodenal cancer, and the role of adjuvant chemoradiotherapy (CRT) also remains controversial. In this study, the treatment outcomes of surgery alone were compared to those of surgery plus adjuvant CRT for duodenal cancer. Methods Between January 1991 and February 2013, a total of 47 patients with duodenal cancer had pancreaticoduodenectomy, and their age ranged from 31 to 80 (median 62). Twenty‐f
Radiation therapy for patients with pN1mi or pN1 disease breast cancer undergoing mastectomy has been debated for a long time. Even in low metastatic burden in sentinel node biopsy, occult non-sentinel axillary nodal involvement can exist. Radiotherapy can sterilize axillary metastatic burden and seems to contribute a very low local recurrence rate in mastectomy patients with minimally involved lymph nodes. However, it should be considered that systemic therapy is evolving and the local recurren
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