AI-Enabled Lung Cancer Prognosis

TL;DR

This paper addresses the challenge of predicting prognosis in non-small cell lung cancer (NSCLC) using AI in the context of high-dimensional, multi-omics, imaging, and pathology data. It surveys approaches spanning high-dimensional gene expression profiling, radiomics-based imaging biomarkers, and deep learning integrated prognostic models, highlighting landmark gene signatures and liquid-biopsy modalities that improve risk stratification and treatment guidance. The contributions include a synthesis of imaging and molecular data fusion, interpretable pathomics, and DL architectures (autoencoders, CNNs, graph networks) that enhance survival prediction. The work underscores the potential of AI to enable precision medicine in NSCLC, while noting challenges in standardization, generalizability, and integration into clinical workflows.

Abstract

Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.

Figures (3)

• Figure 1: The AI-based NSCLC therapy pipeline shows the major HD data integration involving imaging biomarkers, omics, and clinical information. Imaging biomarkers can be integrated with other data, omics, and clinicals, to improve precision medicine.
• Figure 2: Radiomics involves the extraction and analysis of quantitative features from medical images, enabling the characterization of tumor phenotype and behavior. These features are categorized into four main groups: first-order statistics, which describe voxel intensity distributions and capture spatial relationships between voxel intensities; shape features, which decode the information related to the shape of tumor; and gray level and spatiotemporal features, which encounter texture, pixel intensity, and different features such as Laplacian of Gaussian (LoG), and Wavelet descriptors, respectively, providing insights into tumor heterogeneity and complexity.
• Figure 3: Deep learning has demonstrated notable applications in NSCLC prognosis, including the integration of genomic, multiomics, pathological, and imaging data for survival prediction. Future research in this domain aims to refine deep learning models, enhance interpretability, and incorporate real-time clinical data to further improve prognostic accuracy and guide personalized treatment strategies for NSCLC patients. The future direction of deep neural networks entails the development of more sophisticated models and the integration of attention mechanisms and transformers for improved models.