Artificial intelligence in lung cancer: current applications and perspectives

Abstract

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

Keywords: Artificial intelligence; Deep learning; Diagnostic imaging; Lung neoplasms; Multidetector computed tomography.

Fig. 1

Detection of a pulmonary solid nodule. A Axial chest CT image shows a 7 × 4 mm solid nodule (arrow) in the right lower lobe. B, C A first computer-aided detection (CADe) tool based on classical machine learning method correctly detects the nodule at the cost of four false positives (an example of a false positive is pointed in C). D, E A second CADe tool based on classical machine learning method also correctly detects the nodule at the cost of numerous false positives when the sensitivity is adjusted for 3-mm nodule detection (D) whereas there are no false positives when the threshold is set at 6-mm (E). F A deep learning-based CADe tool also correctly identifies the nodule with no false positives

Fig. 2

Detection of a pulmonary ground-glass nodule. A Axial chest CT image shows a 10 × 8 mm ground-glass nodule (arrow) in the right upper lobe. The ground-glass nodule is not detected by two different computer-aided detection tools based on classical machine learning methods (B and C) but is correctly detected by the one based on deep learning (D)

Fig. 3

Lung nodule detection on chest X-ray. Lung nodule seen on chest X-ray (A) is correctly detected by the deep learning-based computer-aided detection tool (box with orange borders in B)

Fig. 4

Automated Lung-RADS classification. Automated lung nodule detection, volumetry and Lung-RADS classification using a deep-learning-based computer-aided detection tool in a patient who underwent an ultra-low-dose chest CT examination as part of a lung-cancer screening program