Predicting the Survival Period of Non-Small Cell Lung Cancer Based on Deep Learning

Non-Small Cell Lung Cancer(NSCLC) is characterized by poor prognosis and high mortality rates, making the prediction of survival duration crucial for NSCLC patients. Traditional cancer survival prediction relies mainly on the analysis of clinical and pathological characteristics, but these pieces of information often fail to fully reveal the complexity of the disease, thereby limiting the accuracy and reliability of survival prediction. Current deep learning techniques face several challenges, including inadequate utilization of data modalities, only predicting patient survival outcomes, and predicting shorter survival periods for patients. To address these issues, a multimodal fusion neural network, MTPSN (Multimodal Transformer and Convolutional Neural Network for Survival Prediction in Non-Small Cell Lung Cancer Patients), was designed. This model combines convolutional neural networks (CNNs) and transformers to predict the survival duration of NSCLC patients. The model uses CNNs to process multi-modal and high-dimensional genetic data, including clinical information, mRNA, miRNA, copy number variations, and DNA methylation, effectively extracting feature representations. By integrating and fusing these multi-modal data features, the model can utilize the fused feature representations to predict the survival duration of NSCLC patients. Experimental results demonstrate that on squamous cell carcinoma and adenocarcinoma datasets, the proposed survival prediction method achieves a Concordance Index (C-index) of 0.748 and an Integrated Brier Score (IBS) of 0.172, outperforming existing survival prediction models significantly.