Korean J Radiol.  2020 Feb;21(2):159-171. 10.3348/kjr.2019.0630.

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

• Lee G
• Park H
• Bak SH
• Lee HY

• KMID: 2471633
• DOI: http://doi.org/10.3348/kjr.2019.0630

Abstract

Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.

Keyword

Lung cancer; Biomarkers; Image processing

MeSH Terms

Biomarkers
Diagnosis
Lung Neoplasms*
Lung*
Prognosis
Biomarkers

Figure

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  Fig. 1 Steps of radiomics: radiomics process starts with image acquisition and tumor segmentation, followed by feature extraction and selection, and ends with performance testing. AUC = area under curve, KM = Kaplan-Meier

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  Fig. 2 Generation of intratumor and peritumoral regions of lung adenocarcinoma in 57-year-old woman. Yellow arrows indicate region of interests. Lower table demonstrates decrease of median Hounsfield units and increase in standard deviation from intratumor to 5 mm peritumoral and 10 mm peritumoral regions, suggesting reflection of tumor microenvironment.

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  Fig. 3 Clustering approach achieved by combining FDG-PET and contrast-enhanced CT to identify intratumor heterogeneity in 63-year-old woman with lung adenocarcinoma. Pretreatment image exhibits heterogeneous tumor areas of various colors showing multiple patterns of tumor vascularity from contrast-enhanced CT and glucose metabolism from FDG-PET. One-year posttreatment with afatinib reveals tumor with central area of low vascularity and low metabolism (blue), suggesting effective treatment. Blue area represents low vascularity and low metabolism, yellow area represents low vascularity and high metabolism or high vascularity and low metabolism, and red area represents high vascularity and high metabolism. FDG = 18F-fluorodeoxyglucose, PET = positron-emission tomography

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  Fig. 4 Interval CT images of 70-year-old man with idiopathic pulmonary fibrosis. Compared to histogram of initial CT scan, histogram of CT scan obtained three years later demonstrates right-side shifting of Hounsfield unit pixels due to microscopic interstitial fibrosis, suggesting progression of idiopathic pulmonary fibrosis.

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  Fig. 5 Example of tumor volume segmentation by two different reviewers. Results show inter-reader variability.

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  Fig. 6 Effect of CT slice thickness on tumor visualization. Thick-section CT scan (right) demonstrates more partial volume artifacts compared to thin-section CT scan (left).

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  Fig. 7 Tracking cancer evolution from primary tumor to metastases using multi-region radiomics and inter-site modeling. A. Multi-region radiomics extraction from patient with primary lung cancer in LLL and multiple metastatic nodules in LLL, LUL, and RUL. (B) Inter-site similarity matrix and (C) phylogenetic tree (based on inter-site similarity matrix) demonstrate degrees of dissimilarity with each other, which indicates evolutional sequences through all lesions. Numbers indicate numeric codes for lesion locations. LLL = left lower lobe, LUL = left upper lobe, RUL = right upper lobe

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  Fig. 8 Example of deep learning architectures for lung cancer classification based on multi-layers.

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