Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Abstract

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Machine Learning and Texture Analysis of [18F]FDG PET/CT Images for the Prediction of Distant Metastases in Non-Small-Cell Lung Cancer Patients

“…Previous studies used machine learning, deep learning, and radiomics for the analysis of [ 18 F]FDG PET/CT images of lung cancer patients in different clinical contexts. They reported AUC values up to 0.97 in the diagnosis of lung cancer using deep learning algorithms [ 7 , 22 ]. In the prediction of treatment response and prognosis, AUC values up to 0.95 were achieved using machine learning algorithms [ 7 , 22 ].…”

“…They reported AUC values up to 0.97 in the diagnosis of lung cancer using deep learning algorithms [ 7 , 22 ]. In the prediction of treatment response and prognosis, AUC values up to 0.95 were achieved using machine learning algorithms [ 7 , 22 ]. For staging purposes, lymph node involvement was predicted by both machine learning and deep learning models, which often included radiomic features and clinical data with AUC values up to 0.94 [ 19 , 22 , 37 , 38 , 39 , 40 ].…”

“…Lung cancer diagnosis, staging, evaluation of response to treatment, and follow up are based on multimodality imaging, including Computed Tomography (CT), Positron Emission Tomography/Computed Tomography (PET/CT), and Magnetic Resonance Imaging (MRI). The recent development of methods for the analysis of medical images based on artificial intelligence (AI) significantly improved the accuracy and efficiency of image analysis in several clinical contexts, such as lung cancer screening and diagnosis, nodule detection, molecular characterization, lung cancer staging, response to treatment, and prognosis [ 7 , 8 ]. In particular, AI-based algorithms including machine learning, deep learning, and radiomics have significantly enhanced clinical decision making based on individual patient’s imaging, clinical, molecular, and pathological data.…”

Fifteen years of precision medicine in the lung cancer management: Perspectives and Challenges

“…In recent years, artificial intelligence (AI) is being studied to help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data to predict the expression levels of PD-L1, tumor TMB and tumor microenvironment in cancer patients or predict the likelihood of immunotherapy benefits and side effects 143,144 .…”

“…AI, in combination with big data analytics, has the potential to extract valuable insights and hidden patterns from these large data sets. This tool is already known to be resourceful for lung cancer diagnosis, screening, and assisting pathology reports 143,144,167 . With proper radiomics AI may predict prognostic features such as tumor responses to treatment, the occurrence of metastasis 168 and may assist physicians on how to decide which treatment may fit better to each patient based on molecular profiling.…”