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MINDS Seminar on Data ScienceㅣUsing Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lu

기간 : 2022-11-16 ~ 2022-11-16
시간 : 10:00 ~ 11:00
분야Field
날짜Date 2022-11-16 ~ 2022-11-16 시간Time 10:00 ~ 11:00
장소Place 초청자Host
연사Speaker Chul Moon 소속Affiliation Southern Methodist University
TOPIC MINDS Seminar on Data ScienceㅣUsing Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lu
소개 및 안내사항Content Tumor shape is a key factor that affects tumor growth and metastasis. We propose a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examine its effect on the time-to-event data. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.

https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09
ID : 688 896 1076 / PW : 54321
학회명Field MINDS Seminar on Data ScienceㅣUsing Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lu
날짜Date 2022-11-16 ~ 2022-11-16 시간Time 10:00 ~ 11:00
장소Place 초청자Host
소개 및 안내사항Content Tumor shape is a key factor that affects tumor growth and metastasis. We propose a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examine its effect on the time-to-event data. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.

https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09
ID : 688 896 1076 / PW : 54321
성명Field MINDS Seminar on Data ScienceㅣUsing Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lu
날짜Date 2022-11-16 ~ 2022-11-16 시간Time 10:00 ~ 11:00
소속Affiliation Southern Methodist University 초청자Host
소개 및 안내사항Content Tumor shape is a key factor that affects tumor growth and metastasis. We propose a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examine its effect on the time-to-event data. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.

https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09
ID : 688 896 1076 / PW : 54321
성명Field MINDS Seminar on Data ScienceㅣUsing Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lu
날짜Date 2022-11-16 ~ 2022-11-16 시간Time 10:00 ~ 11:00
호실Host 인원수Affiliation Chul Moon
사용목적Affiliation 신청방식Host Southern Methodist University
소개 및 안내사항Content Tumor shape is a key factor that affects tumor growth and metastasis. We propose a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examine its effect on the time-to-event data. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.

https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09
ID : 688 896 1076 / PW : 54321
Admin Admin · 2022-11-16 14:39 · 조회 35
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