The diagnostic value of vascular architecture in solid solitary pulmonary nodules quantified by dynamic contrast enhanced MRI

Introduction

Lung cancer is an important public health problem worldwide, the leading cause of cancer-related deaths globally, and the malignant tumor with the highest morbidity and mortality rate in China (1,2). A study investigating more than 37.5 million cancer patients from 71 countries across a 15-year period [2000–2014] found that survival rates for lung cancer patients in some developed countries have improved by nearly 5% (3); however, the survival rate of Chinese lung cancer patients is still at a low level, and there are large differences between regions. The registry of the National Cancer Center, through large-scale screening and follow-up, found that the 5-year overall survival rate of lung cancer is 19.7%, but the 5-year survival rate of early-stage lung cancer can reach 90% (4). This may be due to the high malignancy of advanced lung cancer, the rich blood supply of the lesion and microvascular invasion increasing the risk of distant metastasis and the complexity of prognosis (5). Survival is significantly shorter in patients presenting with early metastases, and a study published in the Lancet as early as 1992 has shown that microvessel counts and vascular maturation significantly correlate with the progression and prognosis of distant metastases from lung cancer (6).

Microvessel density (MVD) is known to be a common marker for quantifying angiogenesis within tumors (7). In this study on lung adenocarcinoma, MVD expression has been found to correlate with tumor prognosis and can be an important predictor of overall survival and progression-free survival (8). Using immunohistochemical staining, the vascular endothelial cell marker CD31 and the perivascular cell and smooth muscle cell positive reactant smooth muscle actin (SMA) antibodies together quantitatively assess MVD. CD31-MVD is expressed in both mature and immature microvascular endothelial cells, while SMA-MVD is a marker of mature endothelial cells only (9). For a comprehensive assessment of the vascular architecture of solid solitary pulmonary nodules (SSPNs), the clinical basis also includes an assessment of the “vascular normalization” index: microvascular perfusion index (MPI), which represents the proportion of mature blood vessels to vascular endothelial cells (MPI = SMA-MVD/CD31-MVD) (10). Vascular endothelial growth factor (VEGF) contributes to tumor cell infiltration, proliferation and metastasis by promoting tumor neovascularization and altering vascular permeability (11).

Currently, MVD, VEGF, and MPI are quantitative indicators for clinically assessing the vascular architecture of lung nodules, which are closely related to the prognosis and survival of lung cancer patients. However, the detection of the expression of the above indicators relies on postoperative immunohistochemistry, and the method of pathological microangiometry is not only cumbersome, but also does not allow for dynamic and reproducible observation. Preoperative quantification of SSPNs vascular architecture using a method that is rapid, noninvasive, reproducible, and reflective of the full extent of the lesion is clinically important.

Computed tomography (CT) is currently the most commonly used imaging tool for the examination of SSPNs, and differential diagnosis of SSPNs is based on CT morphologic features; however, plain scanning does not provide information on physiologic function, and the smaller the diameter of the lesion, the greater the diagnostic challenge; and the radiation dose of enhancement scanning restricts the wide range of clinical applications (12-14). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-ionizing radiation, non-invasive examination method, capable of multi-parameter, multi-sequence high soft-tissue resolution imaging. Malignant tumors are usually characterized by greater blood flow and more irregular vascular structures (15). DCE-MRI scanning captures the dynamic changes of vasculature in the tumor region, and its quantitative parameters reflect the tumor vascular characteristics and tissue microenvironment by evaluating the vascular permeability and the distribution of contrast, which is of large scope of application for comprehensive and precise assessment of lung cancer. For example, a two-dimensional imaging histology model based on T2-weighted imaging (T2WI) images has achieved good results in the differential diagnosis of lung nodules (16), with high diagnostic efficacy in the pathologic subtyping of patients with non-small cell carcinoma (17), and can also be used to assess the efficacy of non-surgical therapeutic means in patients with lung cancer (18-20) and to predict the gene mutation status of patients with lung adenocarcinoma (21).

The aim of this study was to investigate DCE-MRI quantitative perfusion parameters including volume transfer constant (K^trans), rate constant (K_ep), and extravascular extracellular volume fraction (V_e), and to assess their value for application to vascularization of SSPNs. We present this article in accordance with the STARD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1467/rc).

Methods

Sample size estimation

In this study, the sample size was calculated using K^trans as the primary outcome indicator, and based on previous literature (22), the mean was expected to be 0.12 with a standard deviation of 0.07 for the benign group and 0.21 with a standard deviation of 0.15 for the malignant group. We utilized the “Two independent samples t-test-unequal variance” module in PASS version 15 for the sample size calculation, the ratio of the sample size of the two groups 1:2, take α=0.05, β=0.2, test efficacy of 1 − β =0.8, the use of two-sided test, the calculation of a total of 78 cases need to be to fulfill the requirement, 79 cases were included in this study, which fulfills the requirement.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the review board of The First Affiliated Hospital of Guangxi Medical University (No. 2024-E232-01) and informed consent was obtained from all individual participants.

Study cohort

The study cohort flow chart is shown in Figure 1. We prospectively included patients who underwent DCE-MRI of the chest and concurrent surgical treatment at The First Affiliated Hospital of Guangxi Medical University from November 2021 to December 2023, and whose pathological results were obtained. Patients were eligible for enrollment if they met the following criteria: (I) solid isolated pulmonary nodular lesions (≥1 and ≤3 cm) were diagnosed by chest CT at our hospital and underwent chest DCE-MRI enhancement scanning; (II) the lesions had homogeneous density, were generally free of calcification or cavitation, and were not surrounded by satellite foci or pulmonary atelectasis; and (III) they all were operated on within 1 week of the DCE-MRI examination, and the postoperative specimens of the pulmonary lesions were obtained and all imaging data were retained. Subjects were excluded from the study if they (I) had received prior radiotherapy or other therapies for the treatment of lung lesions; (II) had incomplete clinical data; and (III) had not undergone relevant immunohistochemistry. All patients included underwent serological tests using the 7600-120 automatic biochemical analyzer (Hitachi High-Tech, Tokyo, Japan) in our hospital’s clinical laboratory within 2 weeks prior to surgery. Postoperatively, clinical TNM staging and pathological grading were performed.

Figure 1 The flow chart of the study cohort. A total of 79 patients were enrolled in the study. SSPNs, solid solitary pulmonary nodules; CT, computed tomography; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging.

Magnetic resonance imaging (MRI) examination

All patients underwent routine chest MRI plain scanning and dynamic enhancement MRI scanning using a 3.0T MRI scanner (MAGNETOM Verio) equipped with an 8-channel body phase array coil and respiratory gating. All patients were placed in the supine position with the head moved forward, and their respiratory rate and amplitude were matched to the scan as instructed. Routine MRI plain scan included turbo spin echo (TSE) T2WI axial, coronal and dysplasia free breathing with fat suppression, T1-weighted imaging (T1WI) volumetric interpolated breath-hold examination (VIBE) peripheral fat suppression plain and enhanced scans. The DCE-MRI enhancement scan parameters were as follows: repetition time, 4.1 ms; echo time, 1.41 ms; band width, 400 kHz; slice thickness, 3.6 mm; interval between floors, 1.2 mm; field of view, 380 mm × 380 mm; resolution, 320 mm × 224 mm. Gadoteric acid glucosamine injection (15 mL/5.654 g/vial, 0.2 mmol/kg) administered by push-feeding at a venous rate of 2 mL/s. Thirty seconds after injection of the contrast agent, the pulmonary nodal area was scanned for 30 consecutive cycles with a total scan time of 04:58, during which the patient was instructed to maintain shallow and slow breathing.

Imaging analysis

All raw data were transferred to the image perfusion processing software Tissu 4D (Seimens Medical Systems) on the Seimens Syngo Magnetic Resonance Diagnostics post-processing workstation to measure quantitative parameter values. After artefact correction and image screening, the quantitative T1 baseline value before enhancement was automatically obtained by a computer using multi-reverse angle technology. At the same time, with reference to the conventional T1WI and T2WI images, three regions of interest (ROIs) were manually outlined in different areas of the largest horizontal solid portion of the lesion cross-section, avoiding necrosis, cystic degeneration, hemorrhage, etc. as much as possible (Figure 2). The Tofts Two compartment hemodynamic model of the software automatically calculateed quantitative pharmacokinetic parameters in the ROIs, including K^trans, K_ep, and V_e values, which are related to each other by the relationship: K_ep = K^trans/V_e, and the average of three measurements is taken as the final result. To ensure the accuracy of the results, both examiners had 6 years of experience in chest MRI (L.F. and H.D.) and independently measured the relevant parameters.

Figure 2 Magnetic resonance scanning sequence image of SSPNs. (A-D) Pre-enhancement T1WI and DCE-MRI images of benign SSPNs and a T-SIC of plateau type. (E-H) Pre-enhancement T1WI and DCE-MRI images of malignant SSPNs and a T-SIC of inflow type. (A,E) Pre-enhancement T1WI images. (B,F) T1WI-1 min images. (C,G) T1WI-3 min images. (D,H) T1WI-5 min images. The arrows indicate the lesion area. SSPNs, solid solitary pulmonary nodules; T1WI, T1-weighted imaging; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; T-SIC, time signal intensity curve.

Immunohistochemistry

After surgical excision of the lesions, tissues from nonnecrotic areas were taken as SSPNs pathology tissue specimens and fixed in 10% formalin solution for 24 h. Subsequently, routine dehydration and dewaxing, antigen repair, and other operations were performed, and all specimens were prepared as 4 µm sections. This was done as follows: paraffin embedding was done using a tissue processor, followed by deparaffinization and hydration of the tissue sections, which were fully dried and placed in 0.01 mol/L sodium citrate buffer at pH 6.0 for antigenic repair, after which the sections were washed with phosphate buffered saline (PBS) for 5 min and incubated with 3% H₂O₂ for 10 min at room temperature. Next, the sections were washed three times with PBS for 5 min each, and then primary antibodies generated against CD31, α-SMA, and VEGF, respectively, were added and incubated overnight in a 4 ℃ refrigerator. On the following day, dropwise addition of the secondary antibody was followed by incubation for 15 min at room temperature. After washing again with PBS dilution, 3,3' diaminobenzidine reagent was added for color development and counterstained with hematoxylin, then dehydrated and sealed. All antibodies and assay kits were provided by Fuzhou Maixin Biotechnology (China).

Immunohistochemistry data interpretation

The resulting sections were connected to a bright-field microscope (model*/BX53+DP80) and imaged at ×40 magnification to observe the homogeneity and consistency of the staining of the entire section. Three hot spots in the section with different fields of view were selected, and three images of each subject were imaged at ×200 magnification to capture the percentage of positive cell staining area and acquire the corresponding microvascular parameter values indirectly by using Image-J software, and the average value was taken as the final measurement. The average value was taken as the final measurement value. MVD was enumerated as reported in the literature by Weidner and Wang and co-labeled by CD31 and SMA antibodies (23,24). CD31-MVD stains the cytomembrane or cytoplasm of vascular endothelial cells with brown granules, which need to be clearly demarcated from adjacent microvessels, and are counted as one microvessel regardless of the presence or absence of erythrocytes, but microvessels with an internal diameter greater than 150 µm are excluded (Figure 3). The hotspot areas selected for CD31 were labeled, and images were acquired in the same field of view on serial SMA-stained sections, and SMA stained for round-like or slit vessels with luminal structures. VEGF-positive cells with staining area less than or equal to 5% were scored as 0, 5–25% were scored as 1, 26–50% were scored as 2, 51–75% were scored as 3, and greater than 75% were scored as 4. The intensity of microscopic staining was 0 for no staining, 1 for light yellow, 2 for brownish yellow, and 3 for tan color. The above two items are multiplied together for the final score, with 0–1 being negative (−), 2–3 being weakly positive (+), 4–6 being moderately positive (++), and 7–12 being strongly positive (+++) (25). All histologic analyses were determined by our hospital pathologist (with more than 10 years of experience).

Figure 3 Representative staining pattern of CD31-MVD, original magnification ×200. (A,C) Brown areas represent CD31-MVD positive expression by CD31 immunostaining, identifying high and low MVDs. (B,D) Positive areas were manually identified and colored using Image-J software. CD31-MVD, CD31 microvessel density.

Statistical analysis

Statistical analysis was performed using SPSS 24.0. All data were expressed as the mean ± standard deviation. The Shapiro-Wilk test was used to analyze whether the sample data conformed to normal distribution. The independent samples t-test was used to compare the DCE-MRI quantitative permeability parameters and vascular conformation parameters between subgroups of benign and malignant SSPNs. The diagnostic efficacy of the quantitative parameters for malignant SSPNs was analyzed by plotting the receiver operating characteristic (ROC) curves, and the optimal threshold values of quantitative parameters for predicting malignant SSPNs and the specificity and sensitivity at that threshold were determined. The correlation between DCE-MRI quantitative permeability parameters and angioarchitecture parameters was evaluated by Spearman correlation analysis. A two-sided P<0.05 was determined to be statistically significant.

Results

Patients and characteristics

A total of 79 patients with SSPNs were included in our study, the mean age of the total participants was 50.5±11.5 years, 51 (64.6%) of them were females, with a larger proportion of females (80.0%) in the benign group. The majority of patients never smoked, with 58 (73.4%), 35 (64.8%) and 23 (92.0%) in the three groups, respectively; weight in the normal range was more common, with a total of 30 cases (38.0%); more than half of the patients had lesions in the range of 1–2 cm, with a total of 44 cases (55.7%); the majority of patients with serum carcinoembryonic antigen (CEA) was normal, with a total of 63 (79.7%), of which there were no patients with abnormal CEA values in the benign group; the number of patients with abnormal serum ferritin values were 40 (50.6%), 31 (57.4%) and 9 (36.0%), respectively (Table 1).

Comparison of patients’ quantitative permeability parameters and vascular conformation parameters based on different nature of SSPNs

Quantitative permeability parameters (K^trans, K_ep, and V_e values) were higher in the malignant group than in the benign group, with significant differences in K^trans and V_e values when comparing the two groups (P<0.001) and no statistically significant difference in K_ep values (P=0.28). The vascular construct parameter variables SMA-MVD (P=0.01) and MPI (P<0.001) were statistically significant for the comparison between the benign and malignant SSPNs, and CD31-MVD and VEGF were not significantly different between the two groups (Table 2).

Patient ROC curve analysis

In the ROC curves of DCE-MRI quantitative parameters for the diagnosis of malignant lung nodules, the area under the curve (AUC) for the K^trans value was 0.813, with a threshold at diagnosis >0.066/min, and the sensitivity and specificity were 96.3% and 56.0%, respectively; the AUC for the K_ep value was 0.510, with a threshold at diagnosis ≤0.292/min, and the sensitivity and specificity were 31.5% and 92.0%, respectively; the AUC for the V_e value was 0.759, with a threshold at diagnosis of >0.212/min, and the sensitivity and specificity were 68.0% and 81.5%, respectively (Table 3, Figure 4).

Figure 4 The ROC curves for K^trans, K_ep, and V_e values to discriminate malignant SSPNs; AUCs were 0.813, 0.510, and 0.759, respectively. K^trans, volume transfer constant; K_ep, rate constant; V_e, extravascular extracellular volume fraction; ROC, receiver operating characteristic; SSPNs, solid solitary pulmonary nodules; AUCs, area under the curves.

Relationship between quantitative DCE-MRI parameters and vascular architectural parameters

There was a strong positive correlation between K^trans values and CD31-MVD and VEGF, with correlation coefficients of 0.857 (P<0.001) and 0.851 (P<0.001), respectively; there was a strong negative correlation between K^trans values and MPI, with a correlation coefficient of −0.779 (P=0.001), which was not statistically different from SMA-MVD. There was also a strong positive correlation between K_ep values and CD31-MVD and VEGF, with correlation coefficients of 0.830 (P<0.001) and 0.859 (P<0.001), respectively; there was a significant negative correlation between K_ep values and MPI, with a correlation coefficient of −0.864 (P<0.001), which was not significantly different from SMA-MVD. V_e values were strongly positively correlated with CD31-MVD and VEGF, with correlation coefficients of 0.798 (P<0.001) and 0.764 (P<0.001), respectively, and were not statistically significant with either SMA-MVD or MPI (Table 4).

Discussion

Solid tumors rely on neovascularization for growth and metastasis, and the development of SSPNs is also closely related to neovascularization (15). Malignant SSPNs have a haphazard vascular distribution with a lack of muscularis propria resulting in increased permeability and an increased proportion of immature vessels; inflammatory nodules have increased blood flow, but the basement membrane is intact and the increase is in mature vessels; and benign SSPNs are slow-growing and usually have a lack of blood supply. Pathologists have demonstrated that SMA-MVD and MPI reflect the amount and percentage of mature blood vessels, and in our study, malignant SSPNs showed less expression of SMA-MVD and MPI than benign ones, with significant differences in intergroup comparisons, which correlates with the biological characteristics of the lesions, suggesting that cellular proliferation is more active in malignant nodules, and that the neovascularization network formed is predominantly composed of immature vessels. The principle of DCE-MRI imaging is based on the change of vascular permeability in the lesion area, and the change of the concentration of contrast agent in the blood vessels and tissues, which leads to the difference in the measured values of quantitative parameters, indirectly reflecting the angiogenesis, vascular permeability and vascular perfusion (26). K^trans reflects the vascular permeability of the tissue, K^trans value increases with the degree of damage to the vascular endothelial cells, V_e value reflects the degree of necrosis of the tissue, and K_ep is affected by both, and all three reflect the microcirculatory perfusion of the diseased tissue (27). Currently, DCE-MRI is mainly applied to differential diagnosis, staging and prognosis of breast, prostate and rectum in clinical practice. Ma et al. (28) scholars found that K^trans and K_ep values are significant in identifying the nature of breast occupancy using DCE-MRI, and can also provide a reference for grading invasive ductal carcinoma. Ocak et al (29) confirmed that benign prostate tumors have lower K^trans and K_ep values than malignant tumors. Bloch et al. (30) and Jager et al. (31) reached similar conclusions. Yao et al. (32) found that K^trans and K_ep were statistically significantly different between normal intestinal wall and rectal cancer, and that K^trans could be used as a prognostic indicator for rectal cancer. DCE-MRI is now also being progressively used in the diagnosis of benign and malignant lung nodules (33). Kumar et al. (34) studied the prediction of malignant SSPNs by DWI and DCE-MRI and found that the K^trans values had higher diagnostic accuracy and sensitivity. K^trans and K_ep values also have significant advantages over PET/CT parameters for determining the nature of lung lesions (35). Our results showed that K^trans, K_ep, and V_e values were higher in patients with malignant than benign SSPNs, with significant differences between the K^trans and V_e groups, and the sensitivity and specificity of the diagnosis were higher, which is consistent with the conclusions of most scholars. Although the K_ep value has a tendency to increase with the increase in the malignancy of the nodule, it is not statistically significant, the reason for this phenomenon may be related to the small sample size of the two groups, and the number of imbalance, need to increase the sample size for further analysis.

VEGF and CD31-MVD are frequently used to evaluate the level of microvascular development in tumor tissues. The degree of expression of VEGF levels not only reflects the malignancy of the tumor, but also the proliferation and metastasis of the tumor (36). The quantitative parameters K^trans, K_ep and V_e values in our study were closely and significantly correlated with CD31-MVD and VEGF expression. Preda et al. (37) scholars used DCE-MRI to assess quantitative parameter values before and after treatment in a rat soft tissue sarcoma model study, and concluded that K^trans values can reflect tumor microvascular changes. The study by Meyer et al. (38) similarly concluded that K^trans values can quantify changes in tumor tissue perfusion. There are a variety of factors in the vasculature that can cause tumor microangiogenesis, and although VEGF is one of the main factors, it is not the only reference index; secondly, after injecting contrast agent, magnetic resonance scanning acquisition of T1WI sequences can be affected by the physicochemical factors of the contrast agent. Although DCE-MRI quantitative permeability parameters do not fully reflect VEGF expression, they still reflect tumor vascular architecture to some extent. The results of this study showed that the MVD values of patients with malignant SSPNs with strongly positive VEGF expression were significantly higher than those with weakly positive or negative VEGF expression, and increased progressively with the increase of VEGF expression, which was the same as the results of the study by Tateishi et al. (39).

It has been well documented that MPI expression level is closely related to the maturity of blood vessels in tumors, and can be used as a direct reflection of the degree of maturity of microvessels, and when its value is larger, it represents a larger proportion of mature blood vessels (40-43). The results of this study confirmed a significant negative correlation between K^trans and K_ep values and tumor MPI expression, suggesting that the mature blood vessels of the lesion gradually decreased with the increase of K^trans and K_ep values. It has also been demonstrated (44,45) that irregular, tortuous, and leaky blood vessels within malignant tumors lead to reduced vascular maturation, in keeping with the views herein. V_e values did not correlate with the vascular maturity parameters SMA-MVD and MPI, and according to the results of the study by Tofts et al. (27), it was concluded that V_e measurements are unstable and may be altered by the effects of peri-lesion edema.

However, there are some limitations in this study. First, the number of patient cases collected was small, the malignant group was significantly larger than the benign group, and the unbalanced grouping may lead to biased results; second, we could not guarantee that the region of interest measured by MRI was exactly the same as the region examined by pathology; therefore, we outlined the ROIs as broadly as possible in order to ensure that the region examined by pathology was included.

Conclusions

Our results showed that there were significant differences in K^trans, V_e, SMA-MVD and MPI between benign and malignant SSPNs, and that K^trans and V_e values were highly specific and sensitive for the diagnosis of malignant SSPNs. In addition, some of the permeability parameters were closely correlated with the expression of vascular architectural parameters, providing an objective, noninvasive, and reproducible scientific basis for the assessment of the SSPNs microcirculation.

Acknowledgments

We thank Dr. Chenhui Li, a Siemens engineer, for developing and modifying the MRI scanning protocol for this study.

Funding: This study was supported by Guangxi Natural Science Foundation of China (No. 2016GXNSFAA380137) and the Innovation Project of Guangxi Graduate Education (No. YCBZ2023106).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1467/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1467/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1467/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the review board of The First Affiliated Hospital of Guangxi Medical University (No. 2024-E232-01) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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