Yoshiharu Ohno1,2, Masao Yui3, Daisuke Takenaka4, Yoshimori Kassai3, Kazuhiro Murayama1, and Takeshi Yoshikawa2
1Radiology, Fujita Health University School of Medicine, Toyoake, Japan, 2Radiology, Kobe University Graduate School of Medicine, Kobe, Japan, 3Canon Medical Systems Corporation, Otawara, Japan, 4Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan
Synopsis
No report has been found to compare the
capability of pulmonary MR imaging with UTE for nodule detection and Lung-RADS
classification as compared with low-dose CT (LDCT) and standard-dose CT (SDCT). We hypothesized that pulmonary MR imaging
with UTE has a similar potential to
detect pulmonary nodules and evaluate Lung-RADS classification as well as LDCT
and SDCT. The purpose of this study was
to compare the capability of pulmonary MR imaging with UTE for lung nodule detection
and evaluation of Lung-RADS classification with LDCT and SDCT.
Introduction
National
Lung Cancer Screening Trail (NLST) reported 20% reduction in mortality with the
use of low-dose computed tomography (LDCT) scan to screen high risk
individuals. Therefore, major organizations including US Preventive Services
Task Force has adopted LDCT for lung cancer screening in high risk populations. In addition, American College of Radiology
has designed Lung-RADS as a quality assurance tool to standardize lung cancer
screening CT reporting and management recommendations, reduce confusion in lung
cancer screening CT interpretations, and facilitate outcome monitoring (1). Since 2016 (2-4), pulmonary MR imaging with
ultrashort TE (UTE) less than 200ms has been suggested as having a
potential to detect nodule, evaluate lung parenchymal abnormality and play as
substitution to LDCT as well as standard-dose CT (SDCT). However, no report has been found to compare
the capability of pulmonary MR imaging with UTE for nodule detection and
Lung-RADS classification as compared with LDCT and SDCT. We hypothesized that pulmonary MR imaging
with UTE has a similar potential to
detect pulmonary nodules and evaluate Lung-RADS classification as well as LDCT
and SDCT. The purpose of this study was to
compare the capability of pulmonary MR imaging with UTE for lung nodule detection
and evaluation of Lung-RADS classification with LDCT and SDCT. Materials and Methods
110 consecutive
patients (64 males: mean age, 67 years and 46 females: mean age, 65 years) with
suspected pulmonary nodules at near-by hospital were examined with pulmonary MR
imaging with UTE at a 3T system (Vantage Titan 3T, Canon Medical Systems
Corporation, Otawara, Japan) by respiratory-gated 3D radial UTE pulse sequence (TR 4.0ms/ TE 110-192μs,
flip angle 5 degree, 1×1×1 mm3 voxel size), SDCT (270 mA) and LDCT (60 mA) at
64-detector row CTs (Aquilion 64, Canon Medical) and 320-detector row CTs
(Aquilion ONE, Canon Medical). According
to SDCT findings, all nodules were divided into solid and part-solid nodules
and ground glass nodules. In each
patient, probability of presence at each pulmonary nodule was assessed on all
three methods by means of 5-point visual scoring system by two board certified
chest radiologists. In addition, all
nodules were classified based on Lung-RADS on each method by same radiologists. To compare nodule detection capability,
Jackknife alternative free-response receiver operating characteristic (JAFROC)
analysis were performed among all methods.
Then, detection rates were also compared among three methods by
McNemar’s test. To evaluate Lung-RADS
classification capability, inter-observer agreement of each method was
evaluated by kappa statistics with χ2 test. In addition, inter-method agreements were
also assessed by kappa statistics with χ2 test were performed. A p value less than 0.05 was
considered as significant in this study. Results
Representative
case is shown in Figure 1, 2 and 3. Results
of JAFROC analysis are shown in Figure 4.
Figure of merit (FOM) of all methods based on consensus reading (MRI
with UTE: FOM=0.89, LDCT: FOM=0.86, SDCT: FOM=0.89) had no significant
differences (F=0.13, p=0.89). In
addition, sensitivity (SE) and false-positive rate per case (FPR) of all
methods (MR imaging with UTE: SE, 0.93, FPR, 0.55; LDCT; SE, 0.93, FPR, 0.68;
SDCT: SE, 0.93, FPR, 0.62) had no significant difference (p>0.05). On Lung-RADS classification, interobserver
agreement of each method was as follows: MR imaging with UTE: κ=0.92, p<0.0001;
LDCT: κ=0.93, p<0.0001; and SDCT: κ=0.96, p<0.0001. When assessed inter-method agreements among
all methods, inter-method agreements were also almost perfect (MR imaging with
UTE vs. LDCT: κ=0.86, p<0.0001; MR imaging with UTE vs. SDCT: κ=0.87,
p<0.0001; LDCT vs. SDCT: κ=0.95, p<0.0001). Conclusion
Pulmonary MR imaging with UTE is considered
at least as valuable as LDCT and SDCT for lung nodule detection and Lung-RADS
classification. Acknowledgements
Authors wish to thank Mr. Katsusuke Kyotani and
Prof. Takamichi Murakami in Kobe University Hospital for their valuable
contributions to this study. References
- American College of Radiology. Lung CT Screening Reporting
and Data System (Lung-RADS). Accessed at
www.acr.org/Quality-Safety/Resources/LungRADS on 12 October, 2019.
- Ohno Y, Koyama H, Yoshikawa T, et al. J Magn Reson Imaging.
2016; 43(2): 512-532.
- Ohno Y, Koyama H, Yoshikawa T, et al. Radiology. 2017; 284(2):
562-573.
- Wielpütz MO, Lee HY, Koyama H, et al. AJR Am J Roentgenol.
2018; 210(6): 1216-1225.