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Review
. 2021 Feb;10(2):1186-1199.
doi: 10.21037/tlcr-20-708.

Radiomics and artificial intelligence in lung cancer screening

Franciszek Binczyk  1 , Wojciech Prazuch  1 , Paweł Bozek  2 , Joanna Polanska  1
Affiliations
Review

Radiomics and artificial intelligence in lung cancer screening

Franciszek Binczyk et al. Transl Lung Cancer Res. 2021 Feb.

Abstract

Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

Keywords: Computer-aided lung nodule detection; deep learning for medical image analysis; radiomics.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr-20-708). The series “Implementation of CT-based screening of lung cancer” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare.

Figures

Figure 1
Figure 1
Left: Benign nodule with visible calcification in the right upper lobe (RUL). Right: Malignant, spiculated nodule with cavitation in the right lower lobe (RLL).
Figure 2
Figure 2
A standard radiomics workflow.
Figure 3
Figure 3
Number of papers with the keywords “lung cancer” and “radiomics” as identified in the PubMed database for the years 2012–2019 and from January to May 2020.
Figure 4
Figure 4
A schematic diagram of lung nodule detection (A) and classification (B).
See this image and copyright information in PMC

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