Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer.
Gihyeon KimSehwa MoonJang-Hwan ChoiPublished in: Sensors (Basel, Switzerland) (2022)
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.
Keyphrases
- deep learning
- free survival
- small cell lung cancer
- end stage renal disease
- lymph node
- convolutional neural network
- newly diagnosed
- ejection fraction
- chronic kidney disease
- advanced non small cell lung cancer
- artificial intelligence
- prognostic factors
- machine learning
- neoadjuvant chemotherapy
- high resolution
- peritoneal dialysis
- squamous cell carcinoma
- big data
- mass spectrometry
- brain metastases
- epidermal growth factor receptor