Objective To explore the feasibility of Artificial Intelligence(AI)assisted diagnostic system in evaluating pulmonary nodules detection in spectral CT virtual non-contrast images of the chest.Methods Image data were retrospec-tively collected from 55 patients who underwent chest conventional plain scan(TNC),and arterial and venous phase en-hancement scans,including 67 high-risk nodules.Images from the TNC,reconstructed virtual non-contrast in the arterial phase(VNC-A)and the venous phase(VNC-V)were imported into AI for calculations,documenting the number of pulmo-nary nodules detected by AI,diameters,and the identification morphological features of high-risk nodules.Radiologists dif-ferentiated true and false positives of nodules and blindly assessed the morphological features of the high-risk nodules.The sensitivity,positive predictive value,and false-positive rate of nodules detected by AI in the three groups of images were cal-culated and compared.The Bland-Altman plot was used to compare the mean difference in length diameter of the common nodules identified by AI in the VNC-A,VNC-V,and TNC,respectively.The concordance of morphological features of high-risk nodules based on AI detection and subjective assessment by radiologists was also evaluated.In addition,CT values,background noise,and signal-to-noise ratio of the thoracic aorta,pulmonary artery trunk,scapular muscles,and chest wall fat were measured and compared in the three sets of images at the AW workstation to objectively assess image quality.The radi-ation dose for each scan period was recorded.Results There was no significant difference in the sensitivity of AI in detec-ting nodules between the three sets of images(P=0.345),whereas the difference in positive predictive value and false-pos-itive rate was statistically significant(P=0.007、0.002),with VNC-A showing a significantly lower positive predictive value and a higher false-positive rate.The morphological features of high-risk nodules in VNC-A and VNC-V were evaluated based on AI or radiologists alone,which were comparable to TNC.However,when the radiologists'recognition results were taken as the gold standard,the number of Vascular convergence sign identified by AI was significantly less than that of radiologists(P＜0.001),and there was no statistical difference in the recognition performance of other morphological features between AI and radiologists(P＞0.05).The Bland-Altman plot analysis indicated that the mean difference of common nodules length diameter between TNC and VNC-A,VNC-V were 0.151 mm and 0.057 mm,respectively.In the objective evaluation of image quality,apart from fat,VNC images showed similar CT values to TNC images in evaluating different tissues of the chest(P＞0.05),with lower background noise and higher signal-to-noise ratio.Using VNC images in place of TNC resulted in a 31.65％reduction in the total effective radiation dose to the patient.Conclusion Based on AI-assisted diagnosis system combined with spectral CT virtual non-contrast technology,VNC in the venous phase is recommended.While signifi-cantly reducing radiation dose,it guarantees high-quality images,offers superior nodule detection performance,and essential-ly restores morphological features.
NETL
NSTL
万方数据
