Diagnostic value of quantitative dual-source CT dual-energy iodine maps combined with morphological CT features in assessing histological subtypes of lung cancer
10.3760/cma.j.issn.1005-1201.2018.11.003
- VernacularTitle:双能量CT碘图定量参数联合CT征象建模在诊断肺癌病理亚型中的价值
- Author:
Xiaoli XU
1
;
Xin SUI
;
Wei ZHONG
;
Yan XU
;
Zixing WANG
;
Lan SONG
;
Yao HUANG
;
Xiao WANG
;
Zhengyu JIN
;
Wei SONG
Author Information
1. 100730,中国医学科学院北京协和医学院北京协和医院放射科
- Keywords:
Lung neoplasms;
Tomography,X-ray computed;
Diagnosis
- From:
Chinese Journal of Radiology
2018;52(11):823-828
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the clinical usefulness of quantitative dual-source dual-energy CT (DECT) iodine enhancement metrics combined with morphological CT features in distinguishing different lung cancer subtypes. Methods One hundred and sixty-two consecutive patients suspected with lung cancer were prospectively enrolled and underwent DECT in arterial phase prior to biopsy or surgery.Tumor histological subtypes were determined in 110 patients. Two radiologists interpreted CT morphologic features of 110 lesions in a consensual manner. In addition, two radiologists independently contoured lesions and placed regions of interest in descending aorta or subclavian artery on the same section for normalization , from which automated computer measurements were generated:iodine density and iodine ratio (the ratio of iodine density of lesion to that of artery on the same section). DECT metrics and morphological CT features were compared among different lung cancer subtypes. Chi-square was used to compare qualitative parameters. One way ANOVA was used to compare quantitative parameters satisfying normal distribution, while those parameters not satisfying normal distribution or ranked data were compared by Kruskal-Wallis rank sum test. Multinomial logistic regression models were used to differentiate the histological subtypes of lung cancer: adenocarcinoma, squamous cell carcinoma (SCC), small cell lung cancer (SCLC). Results There were 48 cases of adenocarcinomas, 36 cases of SCC and 26 cases of SCLC. In analysis of CT features, tumor diameter, distribution, spiculation, pleural retraction, vascular involvement, confluent mediastinal lymphadenopathy, encasement of mediastinal structures and enhancement heterogeneity showed statistical difference (all P<0.05). The diameter of SCC[(5.73 ± 3.67)cm] and SCLC [(6.08 ± 4.39)cm] were larger than adenocarcinoma [(3.75 ± 2.80 cm)] (H=13.806,P<0.05). Adenocarcinomas were mostly located in the periphery (31 cases), while SCC (26 cases) and SCLC (21 cases) were mainly centrally located. Spiculation was mostly found in adenocarcinoma (44 cases) rather than SCLC (13 cases). Pleural retraction was mostly observed in adenocarcinoma (36 cases) rather than SCC (10 cases) and SCLC (5 cases). Vascular involvement was mostly found in SCLC (19 cases) rather than adenocarcinoma (15 cases). Confluent mediastinal lymphadenopathy was more frequently found in SCLC (15 cases) compared with adenocarcinoma (3 cases) and SCC (4 cases). Encasement of mediastinal structures was mostly found in SCLC (13 cases) rather than adenocarcinoma (7 cases). Homogeneous enhancement was more frequently found in SCLC (10 cases) than SCC (6 cases). No significant differences were observed in other CT features between any other two groups. Iodine density and iodine ratio were statistically different among these three subtypes lung cancer (H=16.817,20.338,P<0.001). Iodine density of adenocarcinoma and SCC was (1.50±0.80) and (1.40± 0.40) mg/ml, respectively, higher than the (1.20±0.40) mg/ml for SCLC (P<0.01). Iodine ratio of adenocarcinoma and SCC was (16.10 ± 7.02)％and (15.05 ± 4.62)％, respectively, higher than the (11.55 ± 3.15)％ for SCLC (P<0.01). No significant difference was observed between adenocarcinoma and SCC. Accuracy of the model based on CT features was 69.1％, accuracy of the model based on CT features combined with DECT parameters was 80.9％. Conclusions Quantitative DECT metrics are different among adenocarcinoma, SCC and SCLC, when combined with morphological CT features, higher diagnostic accuracy can be achieved.
