Dual-energy computed tomography for pre-surgical identification of adenocarcinoma subtypes

Dual-energy computed tomography for pre-surgical identification of adenocarcinoma subtypes

Claudia I. Henschke

Department of Radiology, Mount Sinai Health System, New York, NY, USA

Correspondence to: Claudia I. Henschke. Department of Radiology, Mount Sinai Health System, New York, NY, USA. Email: claudia.henschke@mountsinai.org.

Provenance and Peer Review: This article was commissioned by the Editorial Office, Translational Lung Cancer Research. The article did not undergo external peer review.

Comment on: Li M, Zhang L, Tang W, et al. Quantitative features of dual-energy spectral computed tomography for solid lung adenocarcinoma with EGFR and KRAS mutations, and ALK rearrangement: a preliminary study. Transl Lung Cancer Res 2019;8:401-12.

Submitted Dec 19, 2019. Accepted for publication Dec 30, 2019.

doi: 10.21037/tlcr.2020.03.18

As new targeted treatments for lung cancer are being developed which potentially are used prior to treatment, pre-treatment diagnosis of lung cancer subtypes becomes increasingly important. Currently, the only reliable method is to perform a biopsy using percutaneous needle biopsy, bronchoscopy, or surgery. This paper suggests a new non-invasive alternative—dual energy computed tomography (DECT).

DECT is a new type CT imaging method recently, which uses two sets of different X-ray energy spectra to provide different attenuation values for the same tissue. DECT can generate multiple quantitative parameters, such as monochromatic CT number and the spectral curve, iodine concentration, water concentration, and effective atomic number (Effective-Z), based on virtual monochromatic and material-decomposition images.

In this article, the authors found that DECT quantitative parameters together with the clinical information (age, sex, and smoker or never smoker) allowed for differentiation of the three most common genetic (EGFR, ALK, KRAS) subtypes of solid lung adenocarcinoma in univariate analysis (1). In multivariate logistic analysis, smoking status (OR =2.9, P=0.019) and CT number at 70 keV (OR =1.036, P=0.006) were significant predictive factors for EGFR mutation. Age and Effective-Z were significant predictive factors for ALK rearrangement (P=0.008) and KRAS mutation (OR =1.047, P=0.032) (OR =0.933, P=0.008), respectively (1).

Although these results are obtained at a single institution on 1,010 patients, they are very encouraging and should stimulate further investigation of DECT as well as further development of other quantitative pre-treatment assessments (2-4). While tissue sampling is currently used for identification for sensitivity of lung cancer to targeted treatments, it is not being routinely performed prior to surgery and becomes questionable for small tumors or ones on which non-surgical biopsy is difficult because of location which are increasing being identified in programs of lung cancer screening. For large tumors, these non-invasive techniques may show tumor heterogeneity which is important in treatment planning (5).

Multi-disciplinary team is playing a critical role in the accurate diagnosis and treatment of lung cancer nowadays. By learning about driver gene mutations and studying their correlation with features on CT scan, radiologists may contribute to appropriate patient management by pointing out the possibility of a mutated status and increase the value of their participation in multi-disciplinary meetings. Besides the diagnosis, imaging is also the major tool in the response assessment of lung cancer. Classical tumor response criteria, such as Response Evaluation Criteria in Solid Tumors (RECIST) (6), are based on the morphological change of the tumor and maybe have limitations for new therapy.

Advanced imaging techniques, such as DECT, magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide various functional or molecular parameters. Active investigations of radiologists are needed to find identify potential imaging biomarkers and to assess the treatment response more timely and precisely.

Lung cancer was the leading cause of cancer death around the world (7,8). Adenocarcinoma is the most common histopathological pattern of lung cancer and continuing rise in incidence (9-11). Lung adenocarcinoma are highly heterogeneous as can be seen in this report. The identification of specific driver oncogene mutations can guide the selection of appropriate target drugs, which create a personalized therapy era for lung cancer.

In conclusion, the emerging development of molecular targeting therapy brings a big challenge to the radiologists. This report provides exciting new methods and should stimulate future research of the usefulness of emerging imaging modalities for pre- and post-treatment assessment of lung cancer.


Funding: None.


Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr.2020.03.18). DIH reports that she is a named inventor on a number of patents and patent applications relating to the evaluation of pulmonary nodules on CT scans of the chest which are owned by Cornell Research Foundation (CRF). Since 2009, DIH does not accept any financial benefit from these patents including royalties and any other proceeds related to the patents or patent applications owned by CRF. DIH is the President and serve on the board of the Early Diagnosis and Treatment Research Foundation. DIH receives no compensation from the Foundation. The Foundation is established to provide grants for projects, conferences, and public databases for research on early diagnosis and treatment of diseases. Recipients include, I-ELCAP, among others. The funding comes from a variety of sources including philanthropic donations, grants and contracts with agencies (federal and non-federal), imaging and pharmaceutical companies relating to image processing assessments. The various sources of funding exclude any funding from tobacco companies or tobacco-related sources.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.


  1. Li M, Zhang L, Tang W, et al. Quantitative features of dual-energy spectral computed tomography for solid lung adenocarcinoma with EGFR and KRAS mutations, and ALK rearrangement: a preliminary study. Transl Lung Cancer Res 2019;8:401-12. [Crossref] [PubMed]
  2. Bodalal Z, Trebeschi S, Nguyen-Kim TDL, et al. Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY) 2019;44:1960-84. [Crossref] [PubMed]
  3. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563-77. [Crossref] [PubMed]
  4. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749-62. [Crossref] [PubMed]
  5. Yap TA, Gerlinger M, Futreal PA, et al. Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med 2012;4:127ps10. [Crossref] [PubMed]
  6. Nishino M, Hatabu H, Johnson BE, et al. State of the art: Response assessment in lung cancer in the era of genomic medicine. Radiology 2014;271:6-27. [Crossref] [PubMed]
  7. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin 2015;65:87-108. [Crossref] [PubMed]
  8. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin 2016;66:115-32. [Crossref] [PubMed]
  9. Zhang L, Li M, Wu N, et al. Time Trends in Epidemiologic Characteristics and Imaging Features of Lung Adenocarcinoma: A Population Study of 21,113 Cases in China. PLoS One 2015;10:e0136727. [Crossref] [PubMed]
  10. Travis WD, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011;6:244-85. [Crossref] [PubMed]
  11. Houston KA, Henley SJ, Li J, et al. Patterns in lung cancer incidence rates and trends by histologic type in the United States, 2004-2009. Lung Cancer 2014;86:22-8. [Crossref] [PubMed]
Cite this article as: Henschke CI. Dual-energy computed tomography for pre-surgical identification of adenocarcinoma subtypes. Transl Lung Cancer Res 2020;9(3):432-433. doi: 10.21037/tlcr.2020.03.18