Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives

Abstract

Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare. This dynamic landscape has embraced the incorporation of advanced machine learning and deep learning techniques, particularly artificial intelligence (AI), into the realm of precision medicine. These groundbreaking innovations create fertile ground for the development of AI-based models adept at extracting valuable insights to inform clinical decisions, with the potential to quantitatively interpret patient data and impact overall patient outcomes significantly. In this comprehensive narrative review, a synthesis of various studies is presented, with a specific focus on three core areas aimed at providing clinicians with a practical understanding of AI-based technologies' potential applications in the diagnosis and management of non-small cell lung cancer (NSCLC). The emphasis is placed on methods for diagnosing malignancy in lung lesions, approaches to predicting histology and other pathological characteristics, and methods for predicting NSCLC gene mutations. The review culminates in a discussion of current trends and future perspectives within the domain of AI-based models, all directed toward enhancing patient care and outcomes in NSCLC. Furthermore, the review underscores the synthesis of diverse studies, accentuating AI applications in NSCLC diagnosis and management. It concludes with a forward-looking discussion on current trends and future perspectives, highlighting the LANTERN Study as a pioneering force set to elevate patient care and outcomes to unprecedented levels.

Keywords: Non-small cell lung cancer (NSCLC); artificial intelligence (AI); prediction model radiomics.

Figure 1

Implementation of deep learning as a form of supervised learning within the subset of machine learning methods in artificial intelligence (created with BioRender.com).

Figure 2

Analogous parallels between artificial neural networks and biological neural networks. Hidden layers in artificial neural networks can be compared to brain interneurons (created with BioRender.com).

Figure 3

Integrated use of big data (imaging, clinical and metabolic data) through artificial intelligence for predictive purposes at histological, pathological and genetic state.

Figure 4

LANTERN Study overview. The figure illustrates key phases of the LANTERN Study, from ‘NSCLC Patients’ through enrollment, and integrated data collection, to the creation of digital humanized avatars, culminating in practical implementation in the clinical setting. NSCLC, non-small-cell lung cancer; DHA, digital human avatar.