Extracellular volume fraction as a potential predictor to differentiate lung cancer from benign lung lesions with dual-layer detector spectral CT

Xin'ang Jiang; Qianyun Ma; Taohu Zhou; Qianqian Feng; Wen Yang; Xiuxiu Zhou; Wenjun Huang; Xiaoqing Lin; Jie Li; Xiaohui Zhang; Shiyuan Liu; Xiaoyan Xin; Li Fan

doi:10.21037/qims-23-736

Extracellular volume fraction as a potential predictor to differentiate lung cancer from benign lung lesions with dual-layer detector spectral CT

Quant Imaging Med Surg. 2023 Dec 1;13(12):8121-8131. doi: 10.21037/qims-23-736. Epub 2023 Oct 10.

Authors

Xin'ang Jiang^#¹, Qianyun Ma^#¹, Taohu Zhou^{1

2}, Qianqian Feng³, Wen Yang³, Xiuxiu Zhou¹, Wenjun Huang⁴, Xiaoqing Lin¹, Jie Li¹, Xiaohui Zhang⁵, Shiyuan Liu¹, Xiaoyan Xin³, Li Fan¹

Affiliations

¹ Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China.
² School of Medical Imaging, Weifang Medical University, Weifang, China.
³ Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
⁴ Department of Radiology, The Second People's Hospital of Deyang, Deyang, China.
⁵ Clinical and Technical Support, Philips Healthcare, Shanghai, China.

^# Contributed equally.

Abstract

Background: Extracellular volume (ECV) fraction has been used in cardiovascular diseases, pancreatic fibrosis, and hepatic fibrosis. The diagnostic value of ECV for focal lung lesions remains to be explored. The aim of this study was to evaluate the feasibility of ECV derived from a dual-layer detector computed tomography (DLCT) to differentiate lung cancer (LC) from benign lung lesions (BLLs).

Methods: Retrospectively, 128 consecutive patients with pathologically confirmed LC (n=86) or BLLs (n=42) were included. Conventional computed tomography (CT) characteristics and spectral CT parameters were assessed. All patients' hematocrits were measured to correct contrast volume distributions in blood while calculating ECV. After performing logistic regression analysis, a conventional CT-based model (Model A), DLCT-based model (Model B), combined diagnostic models (Model C), and an ECV-based model (Model D) were developed. The diagnostic effectiveness of each model was examined using the receiver operating characteristic (ROC) curve and their corresponding 95% confidence intervals (CIs). The area under the curve (AUC) of each model was compared using the DeLong test.

Results: Certain conventional CT features (such as lesion size, lobulation, spiculation, pleural indentation, and enlarged lymph nodes) differed significantly between the LC and BLL groups (all P<0.05). Statistical differences were found in the following DLCT parameters (all P<0.05): effective atomic number (Zeff) (non-enhancement), electron density (ED) (non-enhancement), ECV, iodine concentration (IC), and normalized iodine concentration (NIC). Models A, B, C, and D had AUCs of 0.801 [95% confidence interval (CI): 0.721-0.866], 0.805 (95% CI: 0.726-0.870), 0.925 (95% CI: 0.865-0.964), and 0.754 (95% CI: 0.671-0.826), respectively. The AUC of Model D (ECV) showed no significant difference from that of Models A and B (DeLong test, P>0.05).

Conclusions: The ECV derived from DLCT may be a potential new method to differentiate LC from BLLs, broadening the scope of ECV in clinical research.

Keywords: Extracellular volume fraction (ECV fraction); differential diagnosis; lung cancer (LC); spectral CT imaging.