Diagnostic accuracy of low-dose double-input perfusion computed tomography in the differential diagnosis of pulmonary benign and malignant ground-glass nodules

Diagnostic accuracy of low-dose double-input perfusion computed tomography in the differential diagnosis of pulmonary benign and malignant ground-glass nodules | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic accuracy of low-dose double-input perfusion computed tomography in the differential diagnosis of pulmonary benign and malignant ground-glass nodules Xiaoyan Hu, Jie Gou, Lishan Wang, Wei Lin, Wenbo Li, Fan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4072464/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study aimed to evaluate the value of low-dose dual-input targeted perfusion computed tomography (CT) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGOs).A prospective study was conducted of patients with GGOs who underwent CT perfusion imaging from January 2022 to October 2023. All nodules were confirmed by pathological analysis or disappeared during follow-up. The dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software was used for postprocessing analysis to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CT perfusion scan was 348 mGy∙cm, which was < 75% of the diagnostic reference level of the chest CT plain scan (470 mGy∙cm). The effective radiation dose was 4.872 mSV. Blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) were higher in the malignant nodules than in the benign nodules, with statistically significant differences ( p < 0.05). The FEP had the highest accuracy for diagnosis of malignant nodules [area under the curve (AUC) = 0.821, 95% confidence interval (CI): 0.735–0.908], followed by BV (AUV 0.713, 95% CI: 0.608–0.819), BF (AUC 0.688, 95% CI: 0.587–0.797), and MTT (AUC 0.616, 95% CI: 0.506–0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. For distinguishing between benign and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI: 0.728–0.903), and the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI: 0.729–0.900).Low-dose dual-input perfusion CT was very good at distinguishing between benign and malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability. Biological sciences/Cancer Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction In recent years, the detection rate of pulmonary ground-glass opacity nodules (GGOs) has increased significantly with the application of low-dose spiral computed tomography (CT). Some long-standing pulmonary GGOs may have malignant characteristics or tend to be come malignant. In February 2022, the National Cancer Center of China released the latest National Cancer statistics, which [ 1 ] showed that lung cancer has the highest incidence and mortality in China. By 2016, there were 828 000 new lung cancer cases (incidence: 20.37%) and 657,000 deaths (mortality: 27.22%) in China. The morbidity and mortality rates are very close, mainly because most clinically diagnosed patients are already in advanced stages and so have missed the opportunity for surgery. Studies have shown that the 5-year survival rate of lung cancer is approximately 19.75% [ 2 ]. However, for early diagnosed and completely resected cases of adenocarcinoma in situ and minimally invasive adenocarcinoma, the 10-year disease-specific survival rates are 100% and 100%, respectively, and the overall survival rates are 95.3% and 97.8%, respectively [ 3 ]. Therefore, early detection, early diagnosis, and early treatment can reduce the mortality of malignant GGOs and avoid unnecessary surgical treatment for benign GGOs. Due to the complexity of medical history, physical examination, and laboratory tests or the absence of symptoms, the diagnosis of pulmonary GGOs increasingly relies on various noninvasive imaging examinations, including CT, magnetic resonance imaging, and positron-emission tomography (PET)/CT along with 18 F-fluorodeoxyglucose [ 4 – 6 ], and invasive procedures, such as needle biopsy [ 7 ]. Although conventional CT is considered the standard technique for clinically assessing nodule characteristics, there are many similarities between the benign and malignant morphological features. Differentiation based on morphology and density findings between benign and malignant GGOs is only helpful when a nodule has typical features [ 8 ]. PET/CT is not recommended for pure GGOs (pGGOs), mixed GGOs (mGGOs) with solid components < 8 mm, and small nodules near the diaphragm [ 9 , 10 ]. CT-guided needle aspiration biopsy is the gold standard for diagnosing benign and malignant pulmonary nodules. However, needle aspiration is invasive and is influenced by various factors, such as the operator’s skill level, sample size, and lesion location. The formation of new blood vessels within tumors is necessary for tumor growth and metastasis. Formation of new blood vessels leads to a series of pathophysiological changes, particularly increases in perfusion, blood volume (BV), and capillary permeability [ 11 ]. Due to the immature or disrupted basement membrane of the tumor vasculature, the exchange of contrast agent between the inside and outside of the blood vessels is increased. Perfusion CT imaging can not only provide morphological and density information of lesions but also quantitatively analyze the blood supply of lesions by measuring blood flow (BF), BV, mean transit time (MTT), flow extraction product (FEP), and other lesion parameters [ 12 ]. These parameters reflect the functional activity of the lesion and are considered substitutes for the physiological and molecular processes of tumor angiogenesis [ 13 – 14 ]. Therefore, perfusion CT imaging can provide more valuable quantitative parameters for noninvasive evaluation of pulmonary GGOs. It was originally thought that the blood supply to most lung cancers came from the bronchial arterial system [ 15 – 16 ]. The lung has two blood supply systems: pulmonary circulation and systemic circulation. Under physiological conditions, the pulmonary circulation system provides the main blood flow to the lung parenchyma, accounting for approximately 95% of the total lung BF, and the pulmonary artery is the main functional blood vessel of the lung. The bronchial circulation system consists of nutritional vessels that mainly nourish the interstitial support structure of the bronchi and lungs at all levels. Although bronchial circulation accounts for a small portion of the total lung BF, it has a crucial role in maintaining airway and lung function. Therefore, tumors also can theoretically take blood from the pulmonary artery, which has been confirmed by postmortem stereoscopic microangiography and in vivo experiments [ 17 – 18 ]. In most previous studies on lung CT perfusion, the aorta has been considered to be the sole input artery in a single-input perfusion model [ 19 , 20 ]. This model reflects the dominant blood supply to lung tumors but often ignores the blood perfusion of the lesser blood supply parts. Therefore, it cannot accurately and clearly reflect the proportion of dual blood supply to the lung and cannot quantitatively analyze the proportion of systemic and pulmonary circulation in lung lesions. Moreover, previous CT perfusion studies have mainly focused on solid pulmonary nodules [ 21 , 22 ]. However, few studies have investigated dual-input perfusion CT imaging for identification of benign and malignant GGOs. This study aimed to investigate the value of dual-input perfusion CT imaging in the differential diagnosis of benign and malignant pulmonary GGOs. 2. Materials and methods 2.1 Patients This study was approved by the Medical Ethics Committee of our hospital (2022 KT 003) and was conducted following the guidelines of the Declaration of Helsinki. Signed informed consent was obtained from the patients before performing CT. The inclusion criteria were as follows: 1) patients capable of cooperating with perfusion CT; 2) pGGOs or mGGOs with diameters ≥ 0.6 cm and < 3 cm; 3) dynamic images with acceptable respiratory motion artifacts according to the observer and the CT perfusion software. 4) The conditions of all included patients need to be confirmed by surgical or nonsurgical pathological biopsy. The interval between the time of pathological sampling and perfusion CT examination should be less than 3 months. Alternatively, confirmation of lesion disappearance through follow-up (indicating a diagnosable benign GGO) can also be considered. The following exclusion criteria were used: 1) common contraindications of iodinated contrast injection; 2) patients with nephrotic syndrome, diabetic nephropathy, hypertensive nephropathy, chronic renal insufficiency, and other basic nephropathy; 3) patients who could not hold their breath during CT scanning, resulting in incorrect perfusion values and parameter map artifacts; and 4) the nodule was located too close to the heart border or diaphragm, resulting in excessive motion artifacts on the images and making analysis difficult. 2.2 Perfusion CT imaging protocols A SOMATOM Definition Flash scanner (Siemens, Germany) was used for the CT examinations. First, we performed low-dose spiral CT using automatic exposure control (Care KV) and automatic tube current modulation (Care Dose 4D) with a reference tube voltage of 100 kV and tube current of 40 mAs. The rotation time was 280 ms, and the pitch was 1.5. The slice thickness and interval were 1 mm. The I50f Medium Sharp ASA with SAFIRE algorithm was used to perform iterative reconstruction. The central slice of plain CT imaging containing GGOs was selected. Using this slice as the reference, the scanning range covered 15 cm along the Z-axis that included the entire nodule for subsequent perfusion imaging. After the plain scan was completed, 30 mL of the contrast agent, iodomyren (400 mg/mL, at a rate of 4.5 mL/s), was injected through a high-pressure syringe into the cubital vein. Immediately after injection of the contrast agent, the tube was flushed with 30 mL of normal saline to enhance perfusion. Perfusion scanning parameters were as follows: tube voltage: 70 kV, tube current: 100 mAs, and 4D Range: 150 mm. The rotation time was 280 ms, and the total exposure time was 13 times. The first acquisition began 6 s after injecting the contrast agent, the 1–10 acquisition intervals were 3 s, the 11–13 acquisition intervals were 4.5 s, and the total scan time was 43.65 s. The scan slice thickness was 3 mm, the reconstruction slice thickness was 3 mm, and the reconstruction matrix was 512 × 512. The reconstruction algorithm was B20f smooth. 2.3 Image postprocessing After scanning, all images were transferred to a postprocessing workstation (syngo.via VB20A) for data analysis using body perfusion software. First, we selected the liver as the target organ. The liver mode of the software can analyze the dual blood supply of the liver tissue or a lesion, whereas the lung mode can only analyze the single blood supply. Therefore, this study used a liver model to analyze the dual vascular supply (pulmonary artery and bronchial artery) of pulmonary nodules. Second, the phase with the best image quality was selected for motion correction and denoising. The maximum slope method was used to calculate the perfusion parameters. The HU value threshold was used to remove air and bone pixels, resulting in a selection range from 50 to 150 HU. Third, vessel selection was performed as follows. The attenuation peak of the tumor-free lung tissue was used to divide the time–density curve (TDC) of the pulmonary nodules. The TDC before the attenuation peak of lung tissue was defined as the pulmonary artery phase, during which bronchial artery perfusion was considered negligible because the contrast agent in the aorta was assumed to be negligible. The TDC after the peak was defined as the bronchial artery phase (Fig. 1 ). The procedure was as follows: 1) The region of interest (ROI) was placed in the pulmonary artery to identify it as the input artery. The ROI was placed as far as possible in the main pulmonary artery. If the main pulmonary artery was not included in the scanning slice, the ROI was placed in the largest pulmonary artery segment in the slice. 2) The ROI was placed in the descending aorta to identify the bronchus as the input artery. 3) The ROI was placed within tumor-free lung tissue, and blood vessels were avoided as much as possible. Fourth, while avoiding the cavity and necrotic component, 50–70% of the ROI encompassing the lesion size was manually delineated on the coronal, sagittal, and axial images to measure the perfusion parameters, including the BF, BV, MTT, and FEP, and the TDC of the nodules was obtained. All of the above procedures were performed by two radiologists with > 5 years of work experience who used a blinded method, and the data measured by the two radiologists were checked for consistency to evaluate interobserver agreement. Finally, the average value of the two radiologists was used as the perfusion parameter value. The quality of the perfusion image was evaluated by the TDC of the lesion [ 13 ]. The TDC is mainly affected by motion calibration and beam hardening. A 3-point scale was used with 1 = excellent (accurate TDCs), 2 = appropriate (small-bundle sclerosis or misregistration, acceptable TDCs) and 3 = poor (misregistration or extensive beam hardening not acceptable for TDCs). 2.4 Radiation dose analysis After scanning, the volume CT dose index (CTDIvol, mGy) and dose length product (DLP, mGy∙cm) were collected and recorded. The effective radiation dose (ED, mSv) was calculated according to the following formula: ED = DLP × k, where k = 0.014 mSv/mGy∙cm. The results were compared with the Chinese diagnostic reference levels (DRLs) [ 23 ] and the updated 2017 American College of Radiology (ACR) DRLS [ 24 ]. 2.5 Statistical analyses The Statistical Package for the Social Sciences (version 22; IBM Statistics for Windows, IBM Corp., Armonk, NY) was used to perform all data analyses. The Kolmogorov–Smirnov normality test and Levene’s test for homogeneity of variance were performed for all data. For the data that satisfied normal distribution and homogeneity of variance, the mean ± SD was used to represent the data, and an independent-samples t -test was used to compare the differences between the two groups. Nonparametric tests were performed on non-normally distributed data. Values of P < 0.05 were accepted as indicating statistical significance. Given that the BF, BV, MTT, and FEP datasets were not normally distributed in both groups, the Mann–Whitney U test was performed to compare the perfusion parameters between the two groups. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the CT perfusion parameters to differentiate between benign and malignant nodules, and the Youden index was used to determine the best cutoff value. The intraclass correlation coefficient (ICC) was used to check the consistency between the two doctors. 3. Results 3.1 Basic patient information One hundred and one patients were included in this study (50 males, 51 females), with an average age of 58.92 ± 12.87 years. There were 43 benign nodules with an average diameter of 0.9 ± 0.75 cm and 58 malignant nodules with an average diameter of 1.0 ± 0.74 cm. The specific type, density, and size of the nodules are shown in Table 1 . A flow chart of the patients selection is shown in Fig. 2 . Table 1 The basic information of patients and the types, densities, and sizes of nodules Ago(y) n sex density size(cm,M ± IQR) male female pGGO mGGO Benign nodules: 56.53 ± 15.01 43 28 15 7 36 1.0 ± 0.74 Malignant nodules: 60.69 ± 10.81 58 22 36 34 24 0.9 ± 0.75 MIA - 31 11 20 26 5 0.7 ± 0.4 IA - 27 11 16 8 19 1.4 ± 0.8 t/z* 1.618 a 2.037 b P* 0.109 a 0.042 b Note: MIA stand for minimally invasive adenocarcinoma;IA stand for invasive adenocarcinoma. * Comparison between benign nodule group and malignant nodule group; a t test; b Mann–Whitney U test 3.2 Image quality assessment Of the 101 pulmonary nodules included, 78 showed excellent TDC and 33 showed appropriate TDC. 3.3 Intraclass correlation coefficient Two chest radiologists with > 5 years of work experience independently measured the BF, BV, MTT, and FEP values of the GGOs by drawing ROIs on coronary, sagittal, and axial images. The average values were calculated, and the ICC was used to check the consistency of the two. The results are shown in Table 2 . Table 2 Consistency test of the two radiologists BF BV MTT FED ICC 0.99(95% CI: 0.985–0.994) 0.987(95% CI: 0.981–0.991) 0.984(95% CI: 0.976–0.989) 0.99(95% CI: 0.986–0.993) p < 0.001 < 0.001 < 0.001 < 0.001 3.4 Comparison of CT perfusion parameters The values of BF, BV, MTT, and FEP were significantly higher in the malignant nodules than in the benign nodules ( P < 0.05). The results are presented in Table 3 . Figure 3 shows the benign nodules, and Fig. 4 shows the malignant nodules. Table 3 Comparison of perfusion parameters between benign and malignant nodules Benign nodules Malignant nodules z p BF(mL/100mL/min) 89.37 ± 110.14 140.51 ± 94.96 −2.75 0.007 BV(mL/100mL) 6.01 ± 8.49 9.76 ± 6.43 3.65 < 0.001 MTT(/s) 3.88 ± 1.63 4.56 ± 2.63 1.985 0.047 FED(mL/100mL/min) 12.34 ± 26.39 37.82 ± 37.68 5.501 < 0.001 3.5 Diagnostic efficacy of CT perfusion parameters for distinguishing between benign and malignant nodules The area under the ROC curve (AUC) for BF in diagnosing benign and malignant nodules was 0.688, with a 95% confidence interval [CI] of 0.587–0.797. When the threshold of BF was set at 98.38 mL/100 mL/min for diagnosing malignant nodules, the sensitivity was 81.4% and the specificity was 55.8%. The AUC for BV in diagnosing benign and malignant nodules was 0.713, with a 95% CI of 0.608–0.819. When the threshold of BV was set at 6.87 mL/100 mL for diagnosing malignant nodules, the sensitivity was 86.4% and the specificity was 53.5%. The AUC for MTT in diagnosing benign and malignant nodules was 0.616, with a 95% CI of 0.506–0.726. When the threshold of MTT was ≥ 5.17/s for diagnosing malignant nodules, the sensitivity was 47.5% and the specificity was 76.7%. The AUC for FEP in diagnosing benign and malignant nodules was 0.821, with a 95% CI of 0.735–0.908. When the threshold of FEP was set at 19.12 mL/100 mL/min for diagnosing malignant nodules, the sensitivity was 91.5% and the specificity was 62.8%. The AUC of the combination of BV and FEP for diagnosis of benign and malignant nodules was 0.816, with a 95% CI of 0.728–0.903. When the BV was ≥ 7.17 mL/100 mL and the FEP was ≥ 16.56 mL/100 mL/min were used as the threshold for the diagnosis of malignant nodules, the sensitivity was 60.5% and the sensitivity was 93.1%. The AUC of the combination of BF, BV, MTT, and FEP in the diagnosis of benign and malignant nodules was 0.814, with a 95% CI of 0.729–0.900. When BF was ≥ 114.71 mL/100 mL/min, a BV ≥ 12.85 mL/100 mL, an MTT ≥ 7.42/s, and an FEP ≥ 16.56 mL/100 mL/min were used as the threshold for the diagnosis of malignant nodules, and the sensitivity was 60.5% and the sensitivity was 91.4%. 3.6 Radiation dose The same scanning protocol was used to scan all included patients. The DLP of CT perfusion was 348 mGy∙cm and CTDIvol was 22.61 mGy. The DLP was < 75% of the Chinese DRL [ 23 ] of chest CT plain scans (DLP: 470 mGy∙cm) and < 50% of the updated 2017 ACR DRL [ 24 ] of enhanced chest CT (DLP: 374 mGy∙cm). The effective radiation dose was 4.87 mSV. 4. Discussion In this study, the differences in perfusion parameters between benign and malignant GGOs were analyzed by performing dual-input perfusion CT. The results showed that the BF, BV, MTT, and FEP were larger for malignant nodules than for benign nodules. Both BV and FEP had high accuracy for diagnosis of benign and malignant GGOs. When a BV ≥ 6.87 mL/100 mL or an FEP ≥ 19.12 mL/100 mL/min was used as the threshold for the diagnosis of malignant nodules, the sensitivity was high. These findings indicate that dual-input perfusion CT can help clinicians improve the diagnostic accuracy of malignant GGOs, thus enabling the development of appropriate diagnosis and treatment plans for patients and ultimately improving patient outcomes. The results of our study showed that the BF and BV of malignant nodules were higher than those of benign nodules. This finding is consistent with the results of previous studies [ 25 – 28 ]. The number and maturity of tumor blood vessels have an important effect on their biological behavior and a key role in tumor growth [ 29 ]. As the tumor grows and its invasiveness increases, the tumor will develop new blood vessels to facilitate its growth to meet its increasing demands. One study [ 25 ] showed that the microvessel density, vascular lumen area, number of vessels, and vascular perimeter were higher for malignant nodules than for benign nodules. BF is the blood flow per unit time through the vascular structures (including arteries, capillaries, veins, and venous sinuses). BV is the vascular bed volume per unit volume within an ROI, including capillaries and large vessels, which is related to the diameter and number of blood vessels. Both BF and BV have significant positive correlations with microvessel density, vascular lumen area, number of vessels, and vascular perimeter [ 25 , 30 ]. Therefore, the BF and BV were higher for the malignant nodules than for the benign nodules. Our study results showed that the FEP was higher for the malignant nodules than for the benign nodules, which is because the FEP primarily reflects the permeability of blood vessels. Malignant nodules have more neovascularization, immature blood vessel development, incomplete vessel walls, and increased capillary permeability due to vascular endothelial growth factor and other angiogenic factors. In contrast, benign nodules have relatively straighter vessel branches, relatively mature vessels, and lower vessel-wall permeability [ 29 , 31 ]. Therefore, the FEP was higher for the malignant nodules than for the benign nodules, which is consistent with the findings of previous studies [ 25 , 26 ]. This study showed that the MTT was lower for the benign nodules than for the malignant nodules. MTT primarily reflects the average time required for contrast agents to flow through vascular structures, including arteries, capillaries, venous sinuses, and veins. Stimulation by various factors causes neovascularization in tumors to supply sufficient nutrients for their growth and metastasis. However, the newly formed vascular network in tumors differs from that in normal vasculature. The new vasculature is often tortuous and irregularly shaped. Compared with normal blood vessels, these structural abnormalities of tumor blood vessels may lead to significant functional defects and uneven hemodynamics [ 32 ], which increases the transit time of a contrast agent through the blood vessels. However, the vessels of inflammatory nodules are mature and dilated, so the transit time of a contrast agent through these vessels is naturally shorter. Of course, some benign nodules, such as chronic inflammatory nodules, tuberculomas, and hamartomas, have fewer vascular components, and the transit time of a contrast agent through these nodules is also very short. Therefore, compared with the benign nodules, the malignant nodules had a longer MTT. The ROC analysis showed that the FEP had the highest accuracy in the differential diagnosis of benign and malignant nodules, followed by the BV, BF, and MTT. When the BF, BV, and FEP were used alone in the diagnosis of malignant nodules, the sensitivity was > 80%, but the specificity was low at only 55.8%, 53.5%, and 62.8%, respectively. One possible reason for this finding may be that the benign nodules included in this study encompassed active inflammatory nodules, chronic inflammatory nodules, tuberculosis, sclerosing hemangiomas, osteochondromatous hamartomas, and chronic granulomatous nodules. Inflammatory nodules during the active phase can lead to vascular dilation, congestion, and increased vascular permeability because inflammatory mediators are released, resulting in increased BF, BV, and FEP. Therefore, there is some overlap in the characteristics of the perfusion parameters between malignant and inflammatory nodules, leading to a lack of high specificity in the diagnosis. Subgroup analysis, such as further analysis of the differences in perfusion parameters between active and inactive inflammatory nodules and malignant nodules, may improve the sensitivity and specificity of perfusion CT in the identification of pulmonary nodules. This is a research direction that we plan to pursue in the future. Our results showed that a tube voltage of 70 kV for CT perfusion scanning successfully reduced the radiation dose with a DLP of 348 mGy∙cm. Since there are no DRLs for chest enhanced CT and perfusion CT in China, it is impossible to make a direct comparison, but the DLP of perfusion CT is < 75% of the Chinese DRL [ 23 ] of chest CT plain scans and < 50% of the updated 2017 ACR DRL for enhanced chest CT [ 24 ]. The effective radiation dose can be as low as 4.87 mSV. The evaluation of nodules by perfusion CT is mainly to generate perfusion-related parameters in the ROI within the nodules and to compare perfusion parameters without observing the morphological characteristics of the nodules on perfusion images. The morphological features of the nodules were mainly observed by low-dose CT plain scan. In our study, the evaluation of perfusion image quality was based on the TDC of the lesion. To ensure the reliability of perfusion images, several methods can be used to reduce image noise, such as choosing a thicker reconstruction slice. In our study, the thickness of the reconstructed slice was 3 mm, and motion correction and 4D noise-reduction techniques were also used. Although the diameter of the nodules in our study was 3 cm, the Z-axis coverage of our perfusion CT scan was 15 cm because the perfusion scan time was long, so the total scanning time was 43.65 s, and the patient was rarely able to hold his or her breath for such a long time. Therefore, to avoid the leakage of nodules during a perfusion scan due to the influence of respiratory movement, the coverage length of the Z-axis was extended under the condition of calm breathing of the patient. To ensure the acquisition of a complete perfusion curve while reducing the radiation dose, we also set the scanning interval time to 3–4.5 s and obtained 13 time nodes. The perfusion curve quality of the included pulmonary nodules was excellent and moderate. The consistency of the perfusion parameters measured by the two physicians was good. Therefore, this scanning protocol can achieve the goal of using low-dose perfusion scanning while ensuring the accuracy of data and achieving a given research purpose. This study had some limitations. First, the sample size was small, and subgroup analysis was not conducted for different pathological types of malignant nodules or benign nodules with active or inactive biological behavior. These aspects cause significant variations in the perfusion parameters between malignant and benign nodule groups and lower specificity of the perfusion parameters for distinguishing between them. However, this study is ongoing, and we will collect more samples for subgroup analysis to further improve the diagnostic value of perfusion CT for benign and malignant pulmonary GGOs. Second, this study only included patients with pGGos and mGGos and excluded solid nodules; therefore, the perfusion differences between nodules with different densities were not analyzed. Third, this study only performed a comparative analysis of perfusion parameters and did not analyze the specific conditions of bronchial and pulmonary artery blood supply in the nodules. We also plan to work on this task in the future. 5. Conclusion The values of BF, BV, MTT, and FEP were found to be higher for malignant GGOs than for benign GGOs. Low-dose dual-input perfusion CT examination was also found to have high diagnostic value for distinguishing between benign and malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability. The diagnostic abilities of the combination of FEP and BV and of the combination of BF, BV, FEP, and MTT were equivalent to that of FEP alone. However, the combined use of these four perfusion parameters improved the ability to differentiate benign from malignant nodules relative to the ability of BF, BV, and MTT alone. Declarations Author contributions : GJ and W.LS completed the research and measured the data. H.XY analyzed the data and wrote the main manuscript text. LW and L.WB measured the data and prepared figures. YF reviewed the findings.All authors reviewed the manuscript. Data availability statement : The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. Conflicts of Interest: The authors declare no conflict of interest. Funding: This work was fund ed by Chengdu Medical Research Project, Chengdu Municipal Health Commission. grant number:2021018. References Zheng RS, et al.Cancer incidence and mortality in China, 2016.Journal of the National Cancer Center 2022, 2: 1–9. Dio:https://doi.org/10.1016/j.jncc.2022.02.002 . Zeng H, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 populationbased cancer registries. Lancet Glob Health, 2018, 6(5): e555e567. DOI: 10.1016/S2214-109X(18 ) 30127X. Yotsukura M, et al. Longterm prognosis of patients with resected adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung. J Thorac Oncol, 2021, 16(8):1312–1320. DOI: 10.1016/ j.jtho.2021.04.007. Jiang W, et al. Diffusionweighted imaging diagnostic algorithm in patients with suspected pleural malignancy. Eur Radiol, 2021, 31(12):9038–9047. DOI: 10.1007/s00330-021-08013-6 . Shao X, Shao X, Niu R, Jiang Z, Xu M, Wang Y. Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma. Quant Imaging Med Surg. 2021,11(8):3506–3517. doi: 10.21037/qims-20-1189 . He Y, Xiong Z, Zhang J, Xie J, Zhu W, Zhao M, Li Z. Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol. 2023, 149(12):9937–9946. doi: 10.1007/s00432-023-04918-5 . An W, Zhang H, Wang B, Zhong F, Wang S, Liao M. Comparison of CT-Guided Core Needle Biopsy in Pulmonary Ground-Glass and Solid Nodules Based on Propensity Score Matching Analysis. Technol Cancer Res Treat. 2022, 21:15330338221085357. doi: 10.1177/15330338221085357 . Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006, 239(1):34–49. doi: 10.1148/radiol.2391050343 . Bai C, et al. Evaluation of pulmonary nodules: clinical practice consensus guidelines for Asia. Chest, 2016, 150(4): 877–893. DOI: 10.1016/j. chest.2016.02.650. Deppen SA, et al. Accuracy of FDGPET to diagnose lung cancer in areas with infectious lung disease: a metaanalysis. JAMA, 2014, 312(12): 1227–1236. DOI: 10.1001/jama.2014.11488 . Miles KA. Tumour angiogenesis and its relation to contrast enhancement on computed tomography: a review. Eur J Radiol. 1999,30(3):198–205. doi: 10.1016/s0720-048x(99)00012-1 . PMID: 10452718. Sun Y, Yang M, Mao D, Lv F, Yin Y, Li M, Hua Y. Low-dose volume perfusion computed tomography (VPCT) for diagnosis of solitary pulmonary nodules. Eur J Radiol. 2016, 85(6):1208–18. doi: 10.1016/j.ejrad.2016.03.026 . Miles KA, et al; Experimental Cancer Medicine Centre Imaging Network Group. Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography. Eur Radiol. 2012,22(7):1430–41. doi: 10.1007/s00330-012-2379-4 . Park MS,et al. Perfusion CT: noninvasive surrogate marker for stratification of pancreatic cancer response to concurrent chemo- and radiation therapy. Radiology. 2009, 250(1):110–7. doi: 10.1148/radiol.2493080226 . Littleton JT, Durizch ML, Moeller G, Herbert DE. Pulmonary masses: contrast enhancement. Radiology. 1990, 177(3):861–71. doi: 10.1148/radiology.177.3.2244002 . Han Mingjun, Feng Gansheng, Yang Jianyong, Su Hong-ying, Zhao Zhong-chun. The pulmonary artery doesn't participate in the blood supply of lung cancer:experimental and DSA study. Chin J Radiol 2000, 34:802–804. Milne EN. Circulation of primary and metastatic pulmonary neoplasms. A postmortem microarteriographic study. Am J Roentgenol Radium Ther Nucl Med. 1967, 100(3):603–19. doi: 10.2214/ajr.100.3.603 . Yuan X, Zhang J, Ao G, Quan C, Tian Y, Li H. Lung cancer perfusion: can we measure pulmonary and bronchial circulation simultaneously? Eur Radiol. 2012, 22(8):1665–71. doi: 10.1007/s00330-012-2414-5 . Zhu B, Zheng S, Jiang T, Hu B. Evaluation of dual-energy and perfusion CT parameters for diagnosing solitary pulmonary nodules. Thorac Cancer. 2021, 12(20):2691–2697. doi: 10.1111/1759-7714.14105 . Yan G, et al. Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study. Radiol Oncol. 2023, 17;57(1):20–34. doi: 10.2478/raon-2023-0008 . Bohlsen D, Talakic E, Fritz GA, Quehenberger F, Tillich M, Schoellnast H. First pass dual input volume CT-perfusion of lung lesions: The influence of the CT- value range settings on the perfusion values of benign and malignant entities. Eur J Radiol. 2016, 85(6):1109–14. doi: 10.1016/j.ejrad.2016.03.013 . Li Y, Yang ZG, Chen TW, Yu JQ, Sun JY, Chen HJ. First-pass perfusion imaging of solitary pulmonary nodules with 64-detector row CT: comparison of perfusion parameters of malignant and benign lesions. Br J Radiol. 2010, 83(993):785–90. doi: 10.1259/bjr/58020866 . Diagnostic reference levels for adults in X-ray computed tomography.WS/T 637–2018. Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL. U.S. Diagnostic Reference Levels and Achievable Doses for 10 Adult CT Examinations. Radiology. 2017, 284(1):120–133. doi: 10.1148/radiol.2017161911 . Wang M,et al. Correlation study between dual source CT perfusion imaging and the microvascular composition of solitary pulmonary nodules. Lung Cancer. 2019, 130:115–120. doi: 10.1016/j.lungcan.2019.02.013 . Shan F, et al. Differentiation between malignant and benign solitary pulmonary nodules: use of volume first-pass perfusion and combined with routine computed tomography. Eur J Radiol. 2012, 81(11):3598–605. doi: 10.1016/j.ejrad.2012.04.003 . Shu SJ, Liu BL, Jiang HJ. Optimization of the scanning technique and diagnosis of pulmonary nodules with first-pass 64-detector-row perfusion VCT. Clin Imaging. 2013, 37(2):256–64. doi: 10.1016/j.clinimag.2012.05.004 . Marin A, et al. Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules? Radiol Oncol. 2021, 31;55(3):259–267. doi: 10.2478/raon-2021-0024 . Spira D, et al. Assessment of tumor vascularity in lung cancer using volume perfusion CT (VPCT) with histopathologic comparison: a further step toward an individualized tumor characterization. J Comput Assist Tomogr. 2013, 37(1):15–21. doi: 10.1097/RCT.0b013e318277c84f . Li Y, Yang ZG, Chen TW, Chen HJ, Sun JY, Lu YR. Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer. 2008, 61(1):44–53. doi: 10.1016/j.lungcan.2007.10.021 . Ma SH, et al. Peripheral pulmonary nodules: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer. 2008, 30;8:186. doi: 10.1186/1471-2407-8-186 . Gullino PM. Angiogenesis and neoplasia. N Engl J Med 1981;305:884–5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Jun, 2024 Reviews received at journal 26 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers agreed at journal 19 May, 2024 Reviewers invited by journal 11 Apr, 2024 Editor assigned by journal 11 Apr, 2024 Editor invited by journal 28 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 First submitted to journal 11 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4072464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":282687456,"identity":"ca966dc6-3452-4516-b608-f4625ed3c0cd","order_by":0,"name":"Xiaoyan Hu","email":"","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Hu","suffix":""},{"id":282687457,"identity":"2acc0f41-a52b-43a1-8c94-08ae4f6ca0fa","order_by":1,"name":"Jie Gou","email":"","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Gou","suffix":""},{"id":282687458,"identity":"065b60fa-24a7-4c1d-a19f-7306e4ccf3ef","order_by":2,"name":"Lishan Wang","email":"","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lishan","middleName":"","lastName":"Wang","suffix":""},{"id":282687459,"identity":"dfcdd262-ebce-43e9-85b2-b912b1f1e1c6","order_by":3,"name":"Wei Lin","email":"","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lin","suffix":""},{"id":282687460,"identity":"adff5e7c-3ba5-41c8-8931-2263dacba3be","order_by":4,"name":"Wenbo Li","email":"","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Li","suffix":""},{"id":282687461,"identity":"e6ac91ef-ebc8-490b-9d72-25165172dc23","order_by":5,"name":"Fan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3POwrCQBCA4Q2BTbO67SxeYiqNkJvYjE0qrWwsggYsbBZSBy9hbhBYsPIAsUvIBSxT+qqssukE96/nY2YYc7l+MOnLtqYO9lmQWhJ1FBwbHXq5Li0JZoKpmm+9c0W2xB+VSAJ8lbdFxZJo0Utm/phqCoHLSbwJ2SVep31kfmD42iLUaTUFLzX9BA1DIA6At6s1EW+CWAlLog48xqUGUvr5C9n8IqUxTdftSAamqO5J1E++jgQaMv4hQ4XL5XL9Rw+CKDv6PTxzaQAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu First People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-03-11 09:36:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4072464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4072464/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68143-x","type":"published","date":"2024-07-24T16:15:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53582297,"identity":"fa396c17-3f4c-404f-9a94-bdc0b6b5bb20","added_by":"auto","created_at":"2024-03-27 17:40:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240323,"visible":true,"origin":"","legend":"\u003cp\u003eTDC of input arteries. The yellow arrow represents the attenuation peak of tumor–free \u0026nbsp;lung tissue. The TDC on the left of the yellow arrow is defined as the pulmonary artery phase, and the TDC on the right of the yellow arrow is defined as the bronchial artery phase.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4072464/v1/28eff3c48e00c930dcd6b2ed.jpeg"},{"id":53581188,"identity":"eb6d28dd-e1fa-42b2-972b-1b3f08c98c5d","added_by":"auto","created_at":"2024-03-27 17:32:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":195083,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the patients selection.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4072464/v1/985d605c63349d966c8519bb.jpeg"},{"id":53581189,"identity":"28125eb3-67f9-4cb9-ab2a-76f2d7771110","added_by":"auto","created_at":"2024-03-27 17:32:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1079307,"visible":true,"origin":"","legend":"\u003cp\u003eInflammatory GGO. A 56-year-old female patient presented with cough and sputum for 3 weeks. 3a: Plain CT on April 16, 2023 showed pGGO in the lower lobe of the right lung. 3b: TDC of perfusion CT of the pGGO on April 17, 2023. 3c: Follow-up CT on May 19,2023.The density of the pGGO was decreased.3d: Follow-up CT on December 6, 2023.The pGGO of the lower lobe of the right lung have disappeared.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4072464/v1/c6c4349081ba482cd108a7a1.jpeg"},{"id":53581187,"identity":"edda00e8-5c91-4b00-85b0-b3e0310305b5","added_by":"auto","created_at":"2024-03-27 17:32:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586166,"visible":true,"origin":"","legend":"\u003cp\u003eInvasive adenocarcinoma.56-year-old male, physical examination revealed pulmonary nodules for 3 months. 4a: Plain CT showed mGGO in the upper lobe of the right lung. 4b:TDC of perfusion CT of the mGGO.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4072464/v1/f26fd1d099757dcf139a4a81.jpeg"},{"id":61596935,"identity":"3c196fb1-ebb3-47d4-8b23-1f9d57e8a212","added_by":"auto","created_at":"2024-08-01 17:30:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2626678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4072464/v1/338751d1-1da1-4aa5-b7b0-6af6844b5671.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic accuracy of low-dose double-input perfusion computed tomography in the differential diagnosis of pulmonary benign and malignant ground-glass nodules","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, the detection rate of pulmonary ground-glass opacity nodules (GGOs) has increased significantly with the application of low-dose spiral computed tomography (CT). Some long-standing pulmonary GGOs may have malignant characteristics or tend to be come malignant. In February 2022, the National Cancer Center of China released the latest National Cancer statistics, which [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] showed that lung cancer has the highest incidence and mortality in China. By 2016, there were 828 000 new lung cancer cases (incidence: 20.37%) and 657,000 deaths (mortality: 27.22%) in China. The morbidity and mortality rates are very close, mainly because most clinically diagnosed patients are already in advanced stages and so have missed the opportunity for surgery. Studies have shown that the 5-year survival rate of lung cancer is approximately 19.75% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, for early diagnosed and completely resected cases of adenocarcinoma in situ and minimally invasive adenocarcinoma, the 10-year disease-specific survival rates are 100% and 100%, respectively, and the overall survival rates are 95.3% and 97.8%, respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, early detection, early diagnosis, and early treatment can reduce the mortality of malignant GGOs and avoid unnecessary surgical treatment for benign GGOs.\u003c/p\u003e \u003cp\u003eDue to the complexity of medical history, physical examination, and laboratory tests or the absence of symptoms, the diagnosis of pulmonary GGOs increasingly relies on various noninvasive imaging examinations, including CT, magnetic resonance imaging, and positron-emission tomography (PET)/CT along with \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and invasive procedures, such as needle biopsy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although conventional CT is considered the standard technique for clinically assessing nodule characteristics, there are many similarities between the benign and malignant morphological features. Differentiation based on morphology and density findings between benign and malignant GGOs is only helpful when a nodule has typical features [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. PET/CT is not recommended for pure GGOs (pGGOs), mixed GGOs (mGGOs) with solid components\u0026thinsp;\u0026lt;\u0026thinsp;8 mm, and small nodules near the diaphragm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CT-guided needle aspiration biopsy is the gold standard for diagnosing benign and malignant pulmonary nodules. However, needle aspiration is invasive and is influenced by various factors, such as the operator\u0026rsquo;s skill level, sample size, and lesion location. The formation of new blood vessels within tumors is necessary for tumor growth and metastasis. Formation of new blood vessels leads to a series of pathophysiological changes, particularly increases in perfusion, blood volume (BV), and capillary permeability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Due to the immature or disrupted basement membrane of the tumor vasculature, the exchange of contrast agent between the inside and outside of the blood vessels is increased. Perfusion CT imaging can not only provide morphological and density information of lesions but also quantitatively analyze the blood supply of lesions by measuring blood flow (BF), BV, mean transit time (MTT), flow extraction product (FEP), and other lesion parameters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These parameters reflect the functional activity of the lesion and are considered substitutes for the physiological and molecular processes of tumor angiogenesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, perfusion CT imaging can provide more valuable quantitative parameters for noninvasive evaluation of pulmonary GGOs.\u003c/p\u003e \u003cp\u003eIt was originally thought that the blood supply to most lung cancers came from the bronchial arterial system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The lung has two blood supply systems: pulmonary circulation and systemic circulation. Under physiological conditions, the pulmonary circulation system provides the main blood flow to the lung parenchyma, accounting for approximately 95% of the total lung BF, and the pulmonary artery is the main functional blood vessel of the lung. The bronchial circulation system consists of nutritional vessels that mainly nourish the interstitial support structure of the bronchi and lungs at all levels. Although bronchial circulation accounts for a small portion of the total lung BF, it has a crucial role in maintaining airway and lung function. Therefore, tumors also can theoretically take blood from the pulmonary artery, which has been confirmed by postmortem stereoscopic microangiography and in vivo experiments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In most previous studies on lung CT perfusion, the aorta has been considered to be the sole input artery in a single-input perfusion model [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This model reflects the dominant blood supply to lung tumors but often ignores the blood perfusion of the lesser blood supply parts. Therefore, it cannot accurately and clearly reflect the proportion of dual blood supply to the lung and cannot quantitatively analyze the proportion of systemic and pulmonary circulation in lung lesions. Moreover, previous CT perfusion studies have mainly focused on solid pulmonary nodules [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, few studies have investigated dual-input perfusion CT imaging for identification of benign and malignant GGOs. This study aimed to investigate the value of dual-input perfusion CT imaging in the differential diagnosis of benign and malignant pulmonary GGOs.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003e This study was approved by the Medical Ethics Committee of our hospital (2022 KT 003) and was conducted following the guidelines of the Declaration of Helsinki. Signed informed consent was obtained from the patients before performing CT. The inclusion criteria were as follows: 1) patients capable of cooperating with perfusion CT; 2) pGGOs or mGGOs with diameters ≥ 0.6 cm and \u0026lt; 3 cm; 3) dynamic images with acceptable respiratory motion artifacts according to the observer and the CT perfusion software. 4) The conditions of all included patients need to be confirmed by surgical or nonsurgical pathological biopsy. The interval between the time of pathological sampling and perfusion CT examination should be less than 3 months. Alternatively, confirmation of lesion disappearance through follow-up (indicating a diagnosable benign GGO) can also be considered. The following exclusion criteria were used: 1) common contraindications of iodinated contrast injection; 2) patients with nephrotic syndrome, diabetic nephropathy, hypertensive nephropathy, chronic renal insufficiency, and other basic nephropathy; 3) patients who could not hold their breath during CT scanning, resulting in incorrect perfusion values and parameter map artifacts; and 4) the nodule was located too close to the heart border or diaphragm, resulting in excessive motion artifacts on the images and making analysis difficult.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Perfusion CT imaging protocols\u003c/h2\u003e \u003cp\u003eA SOMATOM Definition Flash scanner (Siemens, Germany) was used for the CT examinations. First, we performed low-dose spiral CT using automatic exposure control (Care KV) and automatic tube current modulation (Care Dose 4D) with a reference tube voltage of 100 kV and tube current of 40 mAs. The rotation time was 280 ms, and the pitch was 1.5. The slice thickness and interval were 1 mm. The I50f Medium Sharp ASA with SAFIRE algorithm was used to perform iterative reconstruction. The central slice of plain CT imaging containing GGOs was selected. Using this slice as the reference, the scanning range covered 15 cm along the Z-axis that included the entire nodule for subsequent perfusion imaging. After the plain scan was completed, 30 mL of the contrast agent, iodomyren (400 mg/mL, at a rate of 4.5 mL/s), was injected through a high-pressure syringe into the cubital vein. Immediately after injection of the contrast agent, the tube was flushed with 30 mL of normal saline to enhance perfusion. Perfusion scanning parameters were as follows: tube voltage: 70 kV, tube current: 100 mAs, and 4D Range: 150 mm. The rotation time was 280 ms, and the total exposure time was 13 times. The first acquisition began 6 s after injecting the contrast agent, the 1–10 acquisition intervals were 3 s, the 11–13 acquisition intervals were 4.5 s, and the total scan time was 43.65 s. The scan slice thickness was 3 mm, the reconstruction slice thickness was 3 mm, and the reconstruction matrix was 512 × 512. The reconstruction algorithm was B20f smooth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Image postprocessing\u003c/h2\u003e \u003cp\u003eAfter scanning, all images were transferred to a postprocessing workstation (syngo.via VB20A) for data analysis using body perfusion software. First, we selected the liver as the target organ. The liver mode of the software can analyze the dual blood supply of the liver tissue or a lesion, whereas the lung mode can only analyze the single blood supply. Therefore, this study used a liver model to analyze the dual vascular supply (pulmonary artery and bronchial artery) of pulmonary nodules. Second, the phase with the best image quality was selected for motion correction and denoising. The maximum slope method was used to calculate the perfusion parameters. The HU value threshold was used to remove air and bone pixels, resulting in a selection range from 50 to 150 HU. Third, vessel selection was performed as follows. The attenuation peak of the tumor-free lung tissue was used to divide the time–density curve (TDC) of the pulmonary nodules. The TDC before the attenuation peak of lung tissue was defined as the pulmonary artery phase, during which bronchial artery perfusion was considered negligible because the contrast agent in the aorta was assumed to be negligible. The TDC after the peak was defined as the bronchial artery phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The procedure was as follows: 1) The region of interest (ROI) was placed in the pulmonary artery to identify it as the input artery. The ROI was placed as far as possible in the main pulmonary artery. If the main pulmonary artery was not included in the scanning slice, the ROI was placed in the largest pulmonary artery segment in the slice. 2) The ROI was placed in the descending aorta to identify the bronchus as the input artery. 3) The ROI was placed within tumor-free lung tissue, and blood vessels were avoided as much as possible. Fourth, while avoiding the cavity and necrotic component, 50–70% of the ROI encompassing the lesion size was manually delineated on the coronal, sagittal, and axial images to measure the perfusion parameters, including the BF, BV, MTT, and FEP, and the TDC of the nodules was obtained. All of the above procedures were performed by two radiologists with \u0026gt; 5 years of work experience who used a blinded method, and the data measured by the two radiologists were checked for consistency to evaluate interobserver agreement. Finally, the average value of the two radiologists was used as the perfusion parameter value.\u003c/p\u003e \u003cp\u003eThe quality of the perfusion image was evaluated by the TDC of the lesion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The TDC is mainly affected by motion calibration and beam hardening. A 3-point scale was used with 1 = excellent (accurate TDCs), 2 = appropriate (small-bundle sclerosis or misregistration, acceptable TDCs) and 3 = poor (misregistration or extensive beam hardening not acceptable for TDCs).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Radiation dose analysis\u003c/h2\u003e \u003cp\u003eAfter scanning, the volume CT dose index (CTDIvol, mGy) and dose length product (DLP, mGy∙cm) were collected and recorded. The effective radiation dose (ED, mSv) was calculated according to the following formula:\u003c/p\u003e \u003cp\u003eED = DLP × k,\u003c/p\u003e \u003cp\u003ewhere k = 0.014 mSv/mGy∙cm.\u003c/p\u003e \u003cp\u003eThe results were compared with the Chinese diagnostic reference levels (DRLs) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the updated 2017 American College of Radiology (ACR) DRLS [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe Statistical Package for the Social Sciences (version 22; IBM Statistics for Windows, IBM Corp., Armonk, NY) was used to perform all data analyses. The Kolmogorov–Smirnov normality test and Levene’s test for homogeneity of variance were performed for all data. For the data that satisfied normal distribution and homogeneity of variance, the mean ± SD was used to represent the data, and an independent-samples \u003cem\u003et\u003c/em\u003e-test was used to compare the differences between the two groups. Nonparametric tests were performed on non-normally distributed data. Values of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 were accepted as indicating statistical significance. Given that the BF, BV, MTT, and FEP datasets were not normally distributed in both groups, the Mann–Whitney \u003cem\u003eU\u003c/em\u003e test was performed to compare the perfusion parameters between the two groups. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the CT perfusion parameters to differentiate between benign and malignant nodules, and the Youden index was used to determine the best cutoff value. The intraclass correlation coefficient (ICC) was used to check the consistency between the two doctors.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003c/p\u003e\u003ch2\u003e3.1 Basic patient information\u003c/h2\u003e\u003cp\u003eOne hundred and one patients were included in this study (50 males, 51 females), with an average age of 58.92 ± 12.87 years. There were 43 benign nodules with an average diameter of 0.9 ± 0.75 cm and 58 malignant nodules with an average diameter of 1.0 ± 0.74 cm. The specific type, density, and size of the nodules are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A flow chart of the patients selection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe basic information of patients and the types, densities, and sizes of nodules\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAgo(y)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003esex\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003edensity\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esize(cm,M ± IQR)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epGGO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003emGGO\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign nodules:\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.53 ± 15.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0 ± 0.74\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant nodules:\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.69 ± 10.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9 ± 0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4 ± 0.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et/z*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.618\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.037\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: MIA stand for minimally invasive adenocarcinoma;IA stand for invasive adenocarcinoma. * Comparison between benign nodule group and malignant nodule group; a t test; b Mann–Whitney U test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003e3.2 Image quality assessment\u003c/h2\u003e\u003cp\u003eOf the 101 pulmonary nodules included, 78 showed excellent TDC and 33 showed appropriate TDC.\u003c/p\u003e\u003ch2\u003e3.3 Intraclass correlation coefficient\u003c/h2\u003e\u003cp\u003eTwo chest radiologists with \u0026gt; 5 years of work experience independently measured the BF, BV, MTT, and FEP values of the GGOs by drawing ROIs on coronary, sagittal, and axial images. The average values were calculated, and the ICC was used to check the consistency of the two. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsistency test of the two radiologists\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBF\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBV\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMTT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFED\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99(95% CI: 0.985–0.994)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.987(95% CI: 0.981–0.991)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984(95% CI: 0.976–0.989)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(95% CI: 0.986–0.993)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e3.4 Comparison of CT perfusion parameters\u003c/h2\u003e\u003cp\u003eThe values of BF, BV, MTT, and FEP were significantly higher in the malignant nodules than in the benign nodules (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the benign nodules, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the malignant nodules.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of perfusion parameters between benign and malignant nodules\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign nodules\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant nodules\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBF(mL/100mL/min)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e89.37 ± 110.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e140.51 ± 94.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e−2.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBV(mL/100mL)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e6.01 ± 8.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e9.76 ± 6.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTT(/s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.88 ± 1.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.56 ± 2.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.985\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFED(mL/100mL/min)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e12.34 ± 26.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e37.82 ± 37.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.501\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003e3.5 Diagnostic efficacy of CT perfusion parameters for distinguishing between benign and malignant nodules\u003c/h2\u003e\u003cp\u003eThe area under the ROC curve (AUC) for BF in diagnosing benign and malignant nodules was 0.688, with a 95% confidence interval [CI] of 0.587–0.797. When the threshold of BF was set at 98.38 mL/100 mL/min for diagnosing malignant nodules, the sensitivity was 81.4% and the specificity was 55.8%. The AUC for BV in diagnosing benign and malignant nodules was 0.713, with a 95% CI of 0.608–0.819. When the threshold of BV was set at 6.87 mL/100 mL for diagnosing malignant nodules, the sensitivity was 86.4% and the specificity was 53.5%. The AUC for MTT in diagnosing benign and malignant nodules was 0.616, with a 95% CI of 0.506–0.726. When the threshold of MTT was ≥ 5.17/s for diagnosing malignant nodules, the sensitivity was 47.5% and the specificity was 76.7%. The AUC for FEP in diagnosing benign and malignant nodules was 0.821, with a 95% CI of 0.735–0.908. When the threshold of FEP was set at 19.12 mL/100 mL/min for diagnosing malignant nodules, the sensitivity was 91.5% and the specificity was 62.8%. The AUC of the combination of BV and FEP for diagnosis of benign and malignant nodules was 0.816, with a 95% CI of 0.728–0.903. When the BV was ≥ 7.17 mL/100 mL and the FEP was ≥ 16.56 mL/100 mL/min were used as the threshold for the diagnosis of malignant nodules, the sensitivity was 60.5% and the sensitivity was 93.1%. The AUC of the combination of BF, BV, MTT, and FEP in the diagnosis of benign and malignant nodules was 0.814, with a 95% CI of 0.729–0.900. When BF was ≥ 114.71 mL/100 mL/min, a BV ≥ 12.85 mL/100 mL, an MTT ≥ 7.42/s, and an FEP ≥ 16.56 mL/100 mL/min were used as the threshold for the diagnosis of malignant nodules, and the sensitivity was 60.5% and the sensitivity was 91.4%.\u003c/p\u003e\u003ch2\u003e3.6 Radiation dose\u003c/h2\u003e\u003cp\u003eThe same scanning protocol was used to scan all included patients. The DLP of CT perfusion was 348 mGy∙cm and CTDIvol was 22.61 mGy. The DLP was \u0026lt; 75% of the Chinese DRL [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] of chest CT plain scans (DLP: 470 mGy∙cm) and \u0026lt; 50% of the updated 2017 ACR DRL [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] of enhanced chest CT (DLP: 374 mGy∙cm). The effective radiation dose was 4.87 mSV.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, the differences in perfusion parameters between benign and malignant GGOs were analyzed by performing dual-input perfusion CT. The results showed that the BF, BV, MTT, and FEP were larger for malignant nodules than for benign nodules. Both BV and FEP had high accuracy for diagnosis of benign and malignant GGOs. When a BV ≥ 6.87 mL/100 mL or an FEP ≥ 19.12 mL/100 mL/min was used as the threshold for the diagnosis of malignant nodules, the sensitivity was high. These findings indicate that dual-input perfusion CT can help clinicians improve the diagnostic accuracy of malignant GGOs, thus enabling the development of appropriate diagnosis and treatment plans for patients and ultimately improving patient outcomes.\u003c/p\u003e\u003cp\u003eThe results of our study showed that the BF and BV of malignant nodules were higher than those of benign nodules. This finding is consistent with the results of previous studies [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The number and maturity of tumor blood vessels have an important effect on their biological behavior and a key role in tumor growth [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As the tumor grows and its invasiveness increases, the tumor will develop new blood vessels to facilitate its growth to meet its increasing demands. One study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] showed that the microvessel density, vascular lumen area, number of vessels, and vascular perimeter were higher for malignant nodules than for benign nodules. BF is the blood flow per unit time through the vascular structures (including arteries, capillaries, veins, and venous sinuses). BV is the vascular bed volume per unit volume within an ROI, including capillaries and large vessels, which is related to the diameter and number of blood vessels. Both BF and BV have significant positive correlations with microvessel density, vascular lumen area, number of vessels, and vascular perimeter [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, the BF and BV were higher for the malignant nodules than for the benign nodules.\u003c/p\u003e\u003cp\u003eOur study results showed that the FEP was higher for the malignant nodules than for the benign nodules, which is because the FEP primarily reflects the permeability of blood vessels. Malignant nodules have more neovascularization, immature blood vessel development, incomplete vessel walls, and increased capillary permeability due to vascular endothelial growth factor and other angiogenic factors. In contrast, benign nodules have relatively straighter vessel branches, relatively mature vessels, and lower vessel-wall permeability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, the FEP was higher for the malignant nodules than for the benign nodules, which is consistent with the findings of previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study showed that the MTT was lower for the benign nodules than for the malignant nodules. MTT primarily reflects the average time required for contrast agents to flow through vascular structures, including arteries, capillaries, venous sinuses, and veins. Stimulation by various factors causes neovascularization in tumors to supply sufficient nutrients for their growth and metastasis. However, the newly formed vascular network in tumors differs from that in normal vasculature. The new vasculature is often tortuous and irregularly shaped. Compared with normal blood vessels, these structural abnormalities of tumor blood vessels may lead to significant functional defects and uneven hemodynamics [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which increases the transit time of a contrast agent through the blood vessels. However, the vessels of inflammatory nodules are mature and dilated, so the transit time of a contrast agent through these vessels is naturally shorter. Of course, some benign nodules, such as chronic inflammatory nodules, tuberculomas, and hamartomas, have fewer vascular components, and the transit time of a contrast agent through these nodules is also very short. Therefore, compared with the benign nodules, the malignant nodules had a longer MTT.\u003c/p\u003e\u003cp\u003eThe ROC analysis showed that the FEP had the highest accuracy in the differential diagnosis of benign and malignant nodules, followed by the BV, BF, and MTT. When the BF, BV, and FEP were used alone in the diagnosis of malignant nodules, the sensitivity was \u0026gt; 80%, but the specificity was low at only 55.8%, 53.5%, and 62.8%, respectively. One possible reason for this finding may be that the benign nodules included in this study encompassed active inflammatory nodules, chronic inflammatory nodules, tuberculosis, sclerosing hemangiomas, osteochondromatous hamartomas, and chronic granulomatous nodules. Inflammatory nodules during the active phase can lead to vascular dilation, congestion, and increased vascular permeability because inflammatory mediators are released, resulting in increased BF, BV, and FEP. Therefore, there is some overlap in the characteristics of the perfusion parameters between malignant and inflammatory nodules, leading to a lack of high specificity in the diagnosis. Subgroup analysis, such as further analysis of the differences in perfusion parameters between active and inactive inflammatory nodules and malignant nodules, may improve the sensitivity and specificity of perfusion CT in the identification of pulmonary nodules. This is a research direction that we plan to pursue in the future.\u003c/p\u003e\u003cp\u003eOur results showed that a tube voltage of 70 kV for CT perfusion scanning successfully reduced the radiation dose with a DLP of 348 mGy∙cm. Since there are no DRLs for chest enhanced CT and perfusion CT in China, it is impossible to make a direct comparison, but the DLP of perfusion CT is \u0026lt; 75% of the Chinese DRL [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] of chest CT plain scans and \u0026lt; 50% of the updated 2017 ACR DRL for enhanced chest CT [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The effective radiation dose can be as low as 4.87 mSV. The evaluation of nodules by perfusion CT is mainly to generate perfusion-related parameters in the ROI within the nodules and to compare perfusion parameters without observing the morphological characteristics of the nodules on perfusion images. The morphological features of the nodules were mainly observed by low-dose CT plain scan. In our study, the evaluation of perfusion image quality was based on the TDC of the lesion. To ensure the reliability of perfusion images, several methods can be used to reduce image noise, such as choosing a thicker reconstruction slice. In our study, the thickness of the reconstructed slice was 3 mm, and motion correction and 4D noise-reduction techniques were also used. Although the diameter of the nodules in our study was 3 cm, the Z-axis coverage of our perfusion CT scan was 15 cm because the perfusion scan time was long, so the total scanning time was 43.65 s, and the patient was rarely able to hold his or her breath for such a long time. Therefore, to avoid the leakage of nodules during a perfusion scan due to the influence of respiratory movement, the coverage length of the Z-axis was extended under the condition of calm breathing of the patient. To ensure the acquisition of a complete perfusion curve while reducing the radiation dose, we also set the scanning interval time to 3–4.5 s and obtained 13 time nodes. The perfusion curve quality of the included pulmonary nodules was excellent and moderate. The consistency of the perfusion parameters measured by the two physicians was good. Therefore, this scanning protocol can achieve the goal of using low-dose perfusion scanning while ensuring the accuracy of data and achieving a given research purpose.\u003c/p\u003e\u003cp\u003eThis study had some limitations. First, the sample size was small, and subgroup analysis was not conducted for different pathological types of malignant nodules or benign nodules with active or inactive biological behavior. These aspects cause significant variations in the perfusion parameters between malignant and benign nodule groups and lower specificity of the perfusion parameters for distinguishing between them. However, this study is ongoing, and we will collect more samples for subgroup analysis to further improve the diagnostic value of perfusion CT for benign and malignant pulmonary GGOs. Second, this study only included patients with pGGos and mGGos and excluded solid nodules; therefore, the perfusion differences between nodules with different densities were not analyzed. Third, this study only performed a comparative analysis of perfusion parameters and did not analyze the specific conditions of bronchial and pulmonary artery blood supply in the nodules. We also plan to work on this task in the future.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe values of BF, BV, MTT, and FEP were found to be higher for malignant GGOs than for benign GGOs. Low-dose dual-input perfusion CT examination was also found to have high diagnostic value for distinguishing between benign and malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability. The diagnostic abilities of the combination of FEP and BV and of the combination of BF, BV, FEP, and MTT were equivalent to that of FEP alone. However, the combined use of these four perfusion parameters improved the ability to differentiate benign from malignant nodules relative to the ability of BF, BV, and MTT alone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e GJ and W.LS completed the research and measured the data. H.XY analyzed the data and wrote the main\u0026nbsp;manuscript text. LW and L.WB measured the data and\u0026nbsp;\u0026nbsp;prepared figures. YF\u0026nbsp;reviewed the findings.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e \u003cem\u003eThis work was\u0026nbsp;\u003c/em\u003e\u003cem\u003efund\u003c/em\u003e\u003cem\u003eed by\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eChengdu Medical Research Project, Chengdu Municipal Health Commission. grant number:2021018.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZheng RS, et al.Cancer incidence and mortality in China, 2016.Journal of the National Cancer Center 2022, 2: 1\u0026ndash;9.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eDio:https://doi.org/10.1016/j.jncc.2022.02.002\u003c/span\u003e\u003cspan address=\"Dio:10.1016/j.jncc.2022.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng H, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 populationbased cancer registries. Lancet Glob Health, 2018, 6(5): e555e567. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2214-109X(18\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) 30127X.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYotsukura M, et al. Longterm prognosis of patients with resected adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung. J Thorac Oncol, 2021, 16(8):1312\u0026ndash;1320. DOI: 10.1016/ j.jtho.2021.04.007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang W, et al. Diffusionweighted imaging diagnostic algorithm in patients with suspected pleural malignancy. Eur Radiol, 2021, 31(12):9038\u0026ndash;9047. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-021-08013-6\u003c/span\u003e\u003cspan address=\"10.1007/s00330-021-08013-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao X, Shao X, Niu R, Jiang Z, Xu M, Wang Y. Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma. Quant Imaging Med Surg. 2021,11(8):3506\u0026ndash;3517. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/qims-20-1189\u003c/span\u003e\u003cspan address=\"10.21037/qims-20-1189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Xiong Z, Zhang J, Xie J, Zhu W, Zhao M, Li Z. Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol. 2023, 149(12):9937\u0026ndash;9946. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-023-04918-5\u003c/span\u003e\u003cspan address=\"10.1007/s00432-023-04918-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn W, Zhang H, Wang B, Zhong F, Wang S, Liao M. Comparison of CT-Guided Core Needle Biopsy in Pulmonary Ground-Glass and Solid Nodules Based on Propensity Score Matching Analysis. Technol Cancer Res Treat. 2022, 21:15330338221085357. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/15330338221085357\u003c/span\u003e\u003cspan address=\"10.1177/15330338221085357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiner-Muram HT. The solitary pulmonary nodule. Radiology. 2006, 239(1):34\u0026ndash;49. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2391050343\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2391050343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai C, et al. Evaluation of pulmonary nodules: clinical practice consensus guidelines for Asia. Chest, 2016, 150(4): 877\u0026ndash;893. DOI: 10.1016/j. chest.2016.02.650.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeppen SA, et al. Accuracy of FDGPET to diagnose lung cancer in areas with infectious lung disease: a metaanalysis. JAMA, 2014, 312(12): 1227\u0026ndash;1236. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2014.11488\u003c/span\u003e\u003cspan address=\"10.1001/jama.2014.11488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiles KA. Tumour angiogenesis and its relation to contrast enhancement on computed tomography: a review. Eur J Radiol. 1999,30(3):198\u0026ndash;205. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0720-048x(99)00012-1\u003c/span\u003e\u003cspan address=\"10.1016/s0720-048x(99)00012-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 10452718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Yang M, Mao D, Lv F, Yin Y, Li M, Hua Y. Low-dose volume perfusion computed tomography (VPCT) for diagnosis of solitary pulmonary nodules. Eur J Radiol. 2016, 85(6):1208\u0026ndash;18. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejrad.2016.03.026\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2016.03.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiles KA, et al; Experimental Cancer Medicine Centre Imaging Network Group. Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography. Eur Radiol. 2012,22(7):1430\u0026ndash;41. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-012-2379-4\u003c/span\u003e\u003cspan address=\"10.1007/s00330-012-2379-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark MS,et al. Perfusion CT: noninvasive surrogate marker for stratification of pancreatic cancer response to concurrent chemo- and radiation therapy. Radiology. 2009, 250(1):110\u0026ndash;7. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2493080226\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2493080226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittleton JT, Durizch ML, Moeller G, Herbert DE. Pulmonary masses: contrast enhancement. Radiology. 1990, 177(3):861\u0026ndash;71. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiology.177.3.2244002\u003c/span\u003e\u003cspan address=\"10.1148/radiology.177.3.2244002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Mingjun, Feng Gansheng, Yang Jianyong, Su Hong-ying, Zhao Zhong-chun. The pulmonary artery doesn't participate in the blood supply of lung cancer:experimental and DSA study. \u003cem\u003eChin J Radiol\u003c/em\u003e 2000, 34:802\u0026ndash;804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilne EN. Circulation of primary and metastatic pulmonary neoplasms. A postmortem microarteriographic study. Am J Roentgenol Radium Ther Nucl Med. 1967, 100(3):603\u0026ndash;19. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2214/ajr.100.3.603\u003c/span\u003e\u003cspan address=\"10.2214/ajr.100.3.603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan X, Zhang J, Ao G, Quan C, Tian Y, Li H. Lung cancer perfusion: can we measure pulmonary and bronchial circulation simultaneously? Eur Radiol. 2012, 22(8):1665\u0026ndash;71. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-012-2414-5\u003c/span\u003e\u003cspan address=\"10.1007/s00330-012-2414-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu B, Zheng S, Jiang T, Hu B. Evaluation of dual-energy and perfusion CT parameters for diagnosing solitary pulmonary nodules. Thorac Cancer. 2021, 12(20):2691\u0026ndash;2697. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1759-7714.14105\u003c/span\u003e\u003cspan address=\"10.1111/1759-7714.14105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan G, et al. Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study. Radiol Oncol. 2023, 17;57(1):20\u0026ndash;34. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2478/raon-2023-0008\u003c/span\u003e\u003cspan address=\"10.2478/raon-2023-0008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohlsen D, Talakic E, Fritz GA, Quehenberger F, Tillich M, Schoellnast H. First pass dual input volume CT-perfusion of lung lesions: The influence of the CT- value range settings on the perfusion values of benign and malignant entities. Eur J Radiol. 2016, 85(6):1109\u0026ndash;14. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejrad.2016.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2016.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Yang ZG, Chen TW, Yu JQ, Sun JY, Chen HJ. First-pass perfusion imaging of solitary pulmonary nodules with 64-detector row CT: comparison of perfusion parameters of malignant and benign lesions. Br J Radiol. 2010, 83(993):785\u0026ndash;90. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1259/bjr/58020866\u003c/span\u003e\u003cspan address=\"10.1259/bjr/58020866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiagnostic reference levels for adults in X-ray computed tomography.WS/T 637\u0026ndash;2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL. U.S. Diagnostic Reference Levels and Achievable Doses for 10 Adult CT Examinations. Radiology. 2017, 284(1):120\u0026ndash;133. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2017161911\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2017161911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M,et al. Correlation study between dual source CT perfusion imaging and the microvascular composition of solitary pulmonary nodules. Lung Cancer. 2019, 130:115\u0026ndash;120. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lungcan.2019.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.lungcan.2019.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan F, et al. Differentiation between malignant and benign solitary pulmonary nodules: use of volume first-pass perfusion and combined with routine computed tomography. Eur J Radiol. 2012, 81(11):3598\u0026ndash;605. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejrad.2012.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2012.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu SJ, Liu BL, Jiang HJ. Optimization of the scanning technique and diagnosis of pulmonary nodules with first-pass 64-detector-row perfusion VCT. Clin Imaging. 2013, 37(2):256\u0026ndash;64. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinimag.2012.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.clinimag.2012.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarin A, et al. Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules? Radiol Oncol. 2021, 31;55(3):259\u0026ndash;267. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2478/raon-2021-0024\u003c/span\u003e\u003cspan address=\"10.2478/raon-2021-0024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpira D, et al. Assessment of tumor vascularity in lung cancer using volume perfusion CT (VPCT) with histopathologic comparison: a further step toward an individualized tumor characterization. J Comput Assist Tomogr. 2013, 37(1):15\u0026ndash;21. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/RCT.0b013e318277c84f\u003c/span\u003e\u003cspan address=\"10.1097/RCT.0b013e318277c84f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Yang ZG, Chen TW, Chen HJ, Sun JY, Lu YR. Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer. 2008, 61(1):44\u0026ndash;53. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lungcan.2007.10.021\u003c/span\u003e\u003cspan address=\"10.1016/j.lungcan.2007.10.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa SH, et al. Peripheral pulmonary nodules: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer. 2008, 30;8:186. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2407-8-186\u003c/span\u003e\u003cspan address=\"10.1186/1471-2407-8-186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGullino PM. Angiogenesis and neoplasia. N Engl J Med 1981;305:884\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4072464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4072464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to evaluate the value of low-dose dual-input targeted perfusion computed tomography (CT) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGOs).A prospective study was conducted of patients with GGOs who underwent CT perfusion imaging from January 2022 to October 2023. All nodules were confirmed by pathological analysis or disappeared during follow-up. The dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software was used for postprocessing analysis to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CT perfusion scan was 348 mGy∙cm, which was \u0026lt;\u0026thinsp;75% of the diagnostic reference level of the chest CT plain scan (470 mGy∙cm). The effective radiation dose was 4.872 mSV. Blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) were higher in the malignant nodules than in the benign nodules, with statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The FEP had the highest accuracy for diagnosis of malignant nodules [area under the curve (AUC)\u0026thinsp;=\u0026thinsp;0.821, 95% confidence interval (CI): 0.735\u0026ndash;0.908], followed by BV (AUV 0.713, 95% CI: 0.608\u0026ndash;0.819), BF (AUC 0.688, 95% CI: 0.587\u0026ndash;0.797), and MTT (AUC 0.616, 95% CI: 0.506\u0026ndash;0.726). When the FEP was \u0026ge;\u0026thinsp;19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. For distinguishing between benign and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI: 0.728\u0026ndash;0.903), and the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI: 0.729\u0026ndash;0.900).Low-dose dual-input perfusion CT was very good at distinguishing between benign and malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability.\u003c/p\u003e","manuscriptTitle":"Diagnostic accuracy of low-dose double-input perfusion computed tomography in the differential diagnosis of pulmonary benign and malignant ground-glass nodules","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:32:04","doi":"10.21203/rs.3.rs-4072464/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-27T04:08:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T16:56:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274169038135769769746072492157796905091","date":"2024-06-22T15:11:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134749295335000051584288116992321509337","date":"2024-06-19T13:55:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T07:52:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149857411439231314377070347388851966803","date":"2024-06-16T19:16:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64800070012459749350280489613410354189","date":"2024-05-19T23:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-11T13:13:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-11T11:51:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-28T06:44:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-22T12:16:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-11T09:19:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"388eeb68-e628-41cc-82fb-46d95f3e861b","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29765384,"name":"Biological sciences/Cancer"},{"id":29765385,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2024-08-01T17:16:47+00:00","versionOfRecord":{"articleIdentity":"rs-4072464","link":"https://doi.org/10.1038/s41598-024-68143-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-24 16:15:09","publishedOnDateReadable":"July 24th, 2024"},"versionCreatedAt":"2024-03-27 17:32:04","video":"","vorDoi":"10.1038/s41598-024-68143-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-68143-x","workflowStages":[]},"version":"v1","identity":"rs-4072464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4072464","identity":"rs-4072464","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}