Application of Artificial Intelligence in Lung Cancer

Abstract

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient's prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Keywords: artificial intelligence; lung cancer; machine learning; radiomics; survival prediction; whole slide imaging.

Figure 1

Venn diagram of artificial intelligence (AI), machine learning (ML), neural network, deep learning, and further algorithms in each category. AI is a general term for a program that predicts an answer to a certain problem, where one of the conventional methods is logistic regression. ML learns the algorithm through input data without explicit programming. ML includes algorithms such as decision trees (DTs), support vector machines (SVMs), and Bayesian networks (BNs). By using each ML algorithm as a neuron with multiple inputs and a single output, a neural network is a structure that mimics the human brain. Deep learning is formed with multiple layers of neural networks, and convolutional neural network (CNN) is one of the elements of the famous architecture.

Figure 2

The concept map of supervised learning, unsupervised learning and reinforcement learning.

Figure 3

The comparison of traditional AI server architecture and federated learning server architecture. (a) In traditional server architecture, the main server processes all the raw data at the same site, leading to concerns about privacy; (b) In federated learning, the datasets are processed at each individual site and only the trained models are shared with the main server. Privacy of each dataset is protected.