Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer

Autor:	Gihyeon Kim, Sehwa Moon, Jang-Hwan Choi
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	clinical feature handcrafted radiomics deep learning-based radiomics non-small cell lung cancer cancer recurrence Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 17, p 6594 (2022)
Druh dokumentu:	article
ISSN:	22176594 1424-8220 69068836
DOI:	10.3390/s22176594
Popis:	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.
Databáze:	Directory of Open Access Journals
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