Application of deep learning in prognostic prediction of non-small cell lung cancer.

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Abstract

Non-small cell lung cancer (NSCLC) is characterized by high heterogeneity. Traditional prognostic evaluation methods such as TNM staging are difficult to accurately characterize the survival differences of patients, and there is an urgent clinical need for more efficient individualized prediction tools. With its powerful automatic feature extraction and nonlinear modeling capabilities, deep learning has shown significant advantages in the processing of multimodal medical data and has become a core technical direction in NSCLC prognosis research. This paper reviews the research progress of deep learning in NSCLC prognosis prediction, sorts out the technical characteristics and challenges of core modalities such as CT/PET-CT imaging, pathological whole-slide images, genomics and clinical data, analyzes the design logic and applicable scenarios of key architectures including 2D/3D convolutional neural networks, multiple instance learning, graph convolutional networks, deep survival models and Transformers, and expounds the evolution path of multimodal fusion from early concatenation and late fusion to attention interaction, tensor fusion and graph interaction modeling. It also introduces the application status of model interpretability techniques such as Grad-CAM and SHAP. On this basis, this paper summarizes the bottlenecks of current research in data heterogeneity, small-sample overfitting, insufficient verification of interpretability, lack of deep multimodal fusion and weak clinical generalization verification, and prospects future development directions. This paper aims to provide technical references for researchers in the field of medical artificial intelligence and facilitate the development of clinically translatable high-precision NSCLC prognosis prediction models.

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