Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Abstract

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

“…6,7 Currently, LDCT is the only internationally recognized screening method that has demonstrated a decrease in mortality rates in high-risk populations for LC. 8 The NLST found that 26.8% of participants had lung nodules larger than 4 mm. 9 Pulmonary nodules are clinically relevant as they can be the initial manifestation of LC.…”

“…According to the National Lung Screening Trial (NLST) in the US and the Dutch–Belgian Randomized Lung Cancer Screening Trial (NELSON) in Europe, screening with LDCT reduces LC mortality 6,7 . Currently, LDCT is the only internationally recognized screening method that has demonstrated a decrease in mortality rates in high‐risk populations for LC 8 . The NLST found that 26.8% of participants had lung nodules larger than 4 mm 9 .…”

Enhanced Lung Nodule Segmentation using Dung Beetle Optimization based LNS-DualMAGNet Model

“…Due to their adaptability, efforts are still being made to enhance CNN architectures to enable them to manage a diverse array of nodule kinds, sizes, and complexity. Early detection programmes could be revolutionised by CNN integration with computer-aided diagnostic systems, which would also give medical practitioners reliable diagnostic tools quickly and accurately [13].…”

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans

“…Thresholding, Atlas, and Region Growing are some examples of classic automatic segmentation methods. These approaches use the shallow qualities of an image, such as grayscale, texture, gradient, and many more, to segment the object [5]. However, conventional segmentation techniques have difficulty distinguishing between tumours and surrounding healthy tissue because their intensity distributions are similar.…”