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.
Bibliographical noteFunding Information:
This work was partly supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [21YR2400, Development of image and medical intelligence core technology for rehabilitation diagnosis and treatment of brain and spinal cord diseases], the Technology development Program of MSS [S3146559], and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0016, 9991006689). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2022 by the authors.
- cancer recurrence
- clinical feature
- deep learning-based radiomics
- handcrafted radiomics
- non-small cell lung cancer