Assessment of 64-slice spiral computed tomography with perfusion weighted imaging in the early diagnosis of ground-glass opacity lung cancer.
OBJECTIVE
To evaluate the use of the 64-slice spiral computed tomography (CT) with perfusion-weighted imaging in the early diagnosis of ground-glass opacity lung cancers.


METHODS
412 patients with ground-glass opacities found by conventional CT scan in our hospital, and deemed highly suspected of being lung cancers, were enrolled in the study between February of 2012 and February of 2015. Sixty four-slice spiral CTs with perfusion-weighted imaging were carried out on all patients, and the latest nodular contrast analysis software, MPR2D, 3D reconstruction technology, MIP technology and perfusion scanning technology were used to analyze lesion types, density, blood supply, peripheral signals, doubling time and tissue perfusion characteristics. This was repeated after one month, and the final pathologic diagnosis was used as a reference.


RESULTS
There were 350 (84.95%) patients who were ultimately diagnosed with lung cancer. The main lesion type of lung cancer was quasi-circular, the average CT density was 56.7±5.4 HU, bronchial arterial enhancement increased in the nodule, peripheral signals were mainly irregular burrs, and the average doubling time was 1.2±0.3 s. Comparing these parameters with the ones in the non-cancer group, the differences found were with statistical significance (p<0.05). Importantly, tissue perfusion parameters of blood flow, blood volume, mean transit time and permeability surface in the lung cancer group were all significantly higher than those in non-cancer group (p<0.05).


CONCLUSIONS
From the findings in our study, we propose that 64-slice spiral CT with perfusion-weighted imaging can be used to diagnose ground-glass opacity lung cancer early, by taking into account variables such as lesion type, density, blood supply, peripheral signals, doubling time and tissue perfusion characteristics.
Medical Imaging Department, Affiliated Hospital of Hebei University of Engineering, Handan, Hebei, China.
Yinggang
Y
Lv
Yurong
Y
Jin
Dianguo
D
Xu
Qiaohuan
Q
Yan
Guiting
G
Liu
Hui
H
Zhang
Dingling
D
Yuan
Junhui
J
Bao
D000328:Adult; D000368:Aged; D003937:Diagnosis, Differential; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009389:Neovascularization, Pathologic / Q000175:diagnosis / Q000473:pathology; D055420:Perfusion Imaging / Q000379:methods*; D036542:Tomography, Spiral Computed / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*