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Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer
- Title
- Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer
- Authors
- Kim, Gihyeon; Moon, Sehwa; Choi, Jang-Hwan
- Ewha Authors
- 최장환
- SCOPUS Author ID
- 최장환
- Issue Date
- 2022
- Journal Title
- SENSORS
- ISSN
- 1424-8220
- Citation
- SENSORS vol. 22, no. 17
- Keywords
- clinical feature; handcrafted radiomics; deep learning-based radiomics; non-small cell lung cancer; cancer recurrence
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.
- DOI
- 10.3390/s22176594
- Appears in Collections:
- 인공지능대학 > 인공지능학과 > Journal papers
- Files in This Item:
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sensors-22-06594-v2.pdf(3.31 MB)
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