Can spectral computed tomography (CT) replace perfusion CT to assess the histological classification of non-small cell lung cancer?

Introduction

Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two most common histological types of non-small cell lung cancer (NSCLC) (1,2). With the rapid development of precision medicine, the choice of NSCLC treatment is based on different pathological types and molecular phenotypes. For example, patients with LUAD and LUSC receive stereotactic external beam radiation simultaneously, with the former having a higher success rate than the latter (3). LUSC is less sensitive to pemetrexed than LUAD (4). Needle biopsy is a diagnostic method commonly used in clinical practice. However, the tumor tissue obtained by biopsy cannot fully reflect the biological characteristics of the lesion and may cause complications, such as pneumothorax, hemorrhage, and tumor metastasis (5). Therefore, it is necessary to explore a simple, noninvasive method to determine the pathological type of NSCLC before surgery.

Perfusion computed tomography (CT) can quantitatively evaluate tumor hemodynamics noninvasively (6) and has important clinical value in determining the pathological type of lung cancer and predicting the therapeutic efficacy and prognosis of lung cancer (7,8). However, perfusion CT has a high radiation dose owing to the need for multiple repeated acquisitions of lesions, which limits its wide application in clinical practice (9). Spectral CT is an imaging technique used to generate detailed images of the internal structures of the body using X-rays, which captures more extensive information about the composition of tissues and structures within the body by using multiple energy levels. The information can be visualized following the reconstruction process to obtain cross-sectional images of the internal structures of the body. One type of spectral CT is called dual-energy X-ray CT (DXCT) (10). Of note, early DXCT developments encountered issues with the basis material decomposition (BMD) concept, which required the mass attenuation coefficient of any material present in the body to be expressed as a linear combination of the mass attenuation coefficients of two basis materials (10). This resulted in energy-dependent systematic errors, particularly when radiographic contrast media was present (11). To address these issues, the basis material coefficients were numerically transformed into a more desirable set of basis materials, which is known as the BMD-DXCT method (12). This method has been shown to improve the accuracy of Single Photon Emission CT imaging by using DXCT images to provide object/energy-specific attenuation coefficient correction in SPECT imaging (13). Spectral CT has broad application prospects in the diagnosis and treatment of lung cancer by providing a variety of quantitative parameters to reflect the blood supply, functional and metabolic characteristics, and histopathological changes in tumors (14,15).

Some studies have shown that spectral and perfusion parameters are significantly correlated in liver, colorectal, and pancreatic cancers (16-18). However, no correlation study has confirmed their role in NSCLC and compared the accuracy of the two imaging modalities in identifying the histological type of NSCLC. We hypothesize that there is a correlation between spectral and perfusion parameters, with spectral CT exhibiting higher accuracy in identifying the histological type of NSCLC compared to perfusion CT, and that the clinical application of spectral CT in the histological classification of NSCLC will be more extensive than that of perfusion CT. Therefore, our study aimed to prospectively evaluate the relevance between spectral and perfusion parameters in NSCLC and to compare the differential diagnostic performance of the two CT modalities for the histological classification of NSCLC. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1206/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Medical Ethics Committee of Lanzhou University Second Hospital (No. 2021A-498) and informed consent was obtained from all the included patients. The study prospectively collected 221 patients with clinically suspected lung space-occupying lesions in our hospital who underwent “one-stop” spectral combined perfusion scan from September 2020 to December 2021 and with pathologically confirmed NSCLC. At the end, 62 patients were included (32 with LUAD and 30 with LUSC). The inclusion criteria were as follows: (I) CT showed solitary nodules or masses with lesions mearing ≥1 cm in diameter (n=194); (II) histopathologically confirmed LUAD or LUSC (n=153). The exclusion criteria were as follows: (I) treated with an anti-tumor or anti-inflammatory agent before undergoing CT (n=23); (II) incomplete clinical data (n=35); (III) period between the operation and spectral and perfusion CT scan ≥2 weeks (n=12); and (IV) poor quality of chest CT images (including respiratory motion artifacts or body surface metal artifact) (n=21). The inclusion and exclusion criteria are displayed in Figure 1.

Figure 1 Screening process of patient recruitment. CT, computed tomography; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.

CT scanning protocol

All patients were scanned using a Revolution CT scanner (GE Healthcare, Milwaukee, WI, USA). The patients were first scanned unenhanced, followed by “one-stop” spectral-perfusion scanning protocol for all patients. The spectral scanning parameters: tube voltage, 80–140 kVp instantaneously (0.5 ms); tube current, automatically modulated; tube rotation time, 0.5 s; pitch, 0.984:1; collimation width, 128×0.625 mm; and Asir-V, 40%. The CT perfusion scanning parameters: tube current, 100 mA, and tube voltage 80–100 kVp. The other parameters were the same as those of the spectral CT. A nonionic iodine contrast agent (50–80 mL, 300 mgI/mL) was injected using a high-pressure syringe at an injection rate of 4–5.5 mL/s. The injection volume was 1.2 mL/kg, and then 30–40 mL normal saline was injected. The perfusion scan was started 2 s after the injection of the contrast agent, the arterial phase (AP) and venous phase (VP) spectral scan was performed at 30.2 and 52.6 s, respectively. Perfusion images were reconstructed using raw perfusion and spectral data in the AP and VP.

CT image analysis

All spectral and perfusion images were sent to the Advantage Workstation (AW) 4.7 post-processing workstation for analysis. Spectral and perfusion analysis was performed by two different radiologists (DLN and JJY with 5 and 8 years of experience in chest radiology, respectively). These radiologists were not aware of the study design and pathological results. Perfusion imaging analysis was performed first. The thoracic aorta and pulmonary artery at the same level as the lesion was defined as the input and output artery to put region of interest, and a pseudo-color map of each perfusion parameter was automatically generated. The layer with uniform lesion density and its adjacent higher and lower layers were selected to delineate three regions of interest (ROIs), and the final average was calculated. The perfusion parameters of the lesions included blood flow (BF), blood volume (BV), surface permeability (PS), and mean transit time (MTT). The spectral images used gemstone spectral imaging (GSI) Viewer to analyze. The measurement method was the same as that used for the perfusion. When measuring, we attempted to ensure that the size and position of the ROI in the perfusion and dual-phase spectra were consistent. The spectral parameters included iodine concentration (IC), effective atomic number (Zeff), CT_40keV, and the formula used to calculate the slope was K_70keV =(CT_40keV – CT_70keV)/(70 – 40).

Statistical analysis

The statistical analyses used SPSS 23.0 software (IBM, USA) and MedCalc (Version 19.2.3). The intraclass correlation coefficient (ICC) was used to analyze the consistency of the average spectral and perfusion parameters obtained by two radiologists. A value of P<0.05 was considered statistically significant. Categorical and continuous variables were expressed as frequencies or percentages and mean ± standard deviation or median and interquartile range, respectively. The Fisher’s exact test or chi-squared was used to assess categorical variables. The independent samples t-test or Mann-Whitney U test was used to compare the differences of continuous variables between the two groups. Correlations between the dual-phase spectral and perfusion parameters were assessed using Pearson or Spearman correlation analyses. A logistic regression model was constructed to evaluate different pathological types of NSCLC. The receiver operating characteristic (ROC) curve was drawn to assess the differential diagnostic efficiency of each parameter and model. The DeLong test was used to compare the diagnostic performance of different models.

Results

Clinical characteristics of the patients

There were 66.70% male and 34.40% female in the LUSC group, and 34.40% male and 65.60% female in LUAD group (P=0.01). Elevated SCCA values accounted for 46.70% and 18.80% in LUSC and LUAD (P=0.02), respectively. Elevated Cyfra21-1 levels accounted for 56.70% and 31.30% in LUSC and LUAD (P=0.04), respectively. However, there were no significant differences in age, smoking history, carcinoembryonic antigen, neuron-specific enolase, gastrin release peptide precursor levels, pathological grades, tumor size and tumor stage (P>0.05) (Table 1).

Correlation between spectral and perfusion parameters

In NSCLC, BV and BF were strongly correlated with dual-phase CT_40keV, IC, Zeff, and K_70keV (0.6<r<0.8, P<0.001). MTT was moderately correlated with dual-phase CT_40keV, IC, Zeff, and K_70keV (0.4<r<0.6, P<0.001). PS was not significantly correlated with CT_40keV, IC, Zeff, and K_70keV in the AP (P>0.05), but it was weakly correlated with CT_40keV, IC, Zeff, and K_70keV in the VP (0.2<r<0.4, P<0.05) (Table 2).

Quantitative analyses with spectral and perfusion CT

The ICC values for spectral and perfusion CT parameters ranged from 0.71–0.89. The spectral parameters of LUAD and LUSC are displayed in Table 3. In the AP and VP, the CT_40keV, IC, Zeff, and K_70keV of LUAD were higher than those of LUSC (P<0.01). The perfusion parameters of LUAD and LUSC are shown in Table 4. The BV, BF, PS, and MTT of LUAD were significantly lower than those of LUSC (P<0.05). Figure 2 and Figure 3 show examples of the spectral and perfusion parameters of LUAD and LUSC.

Figure 2 Lung adenocarcinoma. (A) Lung window; (B-E) blood flow, blood volume, permeability surface, and mean transit time values of the lesions were 69.89 mL/(100 g·min), 5.50 mL/100 g, 22.74 mL/(100 g·min), 15.86 s, respectively; (G-K) spectral CT images of AP, the CT_70keV (G), effective atomic numbers (H), iodine concentration (I), water concentration (J) and K_70keV (K) are 70.63 HU, 8.76, 19.95 100 μg/cm³, 1,016.74 mg/cm³ and 3.75, respectively; (L-P) spectral CT images of VP, the CT_70keV (L), effective atomic numbers (M), iodine concentration (N), water concentration (O) and K_70keV (P) are 47.76 HU, 8.48, 14.52 100 μg/cm³, 1,009.97 mg/cm³ and 2.73, respectively; (F) hematoxylin and eosin staining (×100). AP, arterial phase; CT, computed tomography; HU, Hounsfiled Unit; VP, venous phase.

Figure 3 Lung squamous cell carcinoma. (A) Lung window; (B-E) blood flow, blood volume, permeability surface, and mean transit time values of the lesions were 49.49 mL/(100 g·min), 4.63 mL/100 g, 18.77 mL/(100 g·min), 10.36 s, respectively; (G-K) spectral CT images of AP, the CT_70keV (G), effective atomic numbers (H), iodine concentration (I), water concentration (J) and K_70keV (K) are 69.23 HU, 8.52, 18.66 100 μg/cm³, 1,011.72 mg/cm³ and 2.90, respectively; (L-P) spectral CT images of VP, the CT_70keV (L), effective atomic numbers (M), iodine concentration (N), water concentration (O) and K_70keV (P) are 61.98 HU, 8.39, 15.68 100 μg/cm³, 1,010.77 mg/cm³ and 2.37, respectively; (F) hematoxylin and eosin staining (×200). CT, computed tomography; AP, arterial phase; VP, venous phase.

The differential diagnostic efficiency of spectral and perfusion CT for different pathological types of NSCLC are displayed in Table 5, Table 6 and Figure 4. The ROC curve analysis revealed that the AUC of the spectral parameter model was 0.93 (sensitivity =0.94, specificity =0.83), and the diagnostic efficiency was better than that of the perfusion parameter model (AUC =0.81, sensitivity =0.99, specificity =0.57). The DeLong test showed a significant difference between the AUC of the spectral and perfusion models (P<0.05).

Figure 4 The ROC curves of spectral and perfusion CT for differentiating the histological classification of NSCLC. CT, computed tomography; ROC, receiver operating characteristic; NSCLC, non-small cell lung cancer.

Discussion

Spectral CT has been considered an alternative to perfusion CT for hemodynamic analysis of lesions (17,19,20); however, no study has directly compared the two imaging modalities in patients with NSCLC. The study revealed that in patients with NSCLC, spectral CT parameters IC, Zeff, and K were significantly correlated with perfusion parameters BV, BF, and MTT. In addition, both spectral and perfusion CT have good diagnostic performance in differentiating different pathological types of NSCLC; however, the advantages of the former are more notable than those of the latter.

To the best of our knowledge, this study is the first to utilize “one-stop” spectral combined perfusion scanning to demonstrate the correlation between the two imaging modalities. Although previous studies have demonstrated significant correlations between energy spectrum and perfusion parameters in liver, pancreatic, and colorectal cancers (21-23), the advantage of the current study was in obtaining two quantitative parameters with only one contrast injection and one scan of the patient, which reduced the amount of contrast agent and shortened the examination time (24). In addition, we employed real-time iterative reconstruction Veo algorithm to reduce radiation dose while maintaining image quality through pre- and post-processing techniques (25). Importantly, our data had high consistency in both time and space, making our findings more accurate.

In spectral CT, lower keV values can increase tumor visibility due to the enhanced photoelectric effect (26,27). As the energy level approaches the iodine k-edge of 33 keV, the decay of iodine becomes more pronounced, and tissues with different atomic numbers can be distinguished by different spectral characteristics (28,29). Luo et al. (30) showed that the maximum AUC for identifying orbital lesions was obtained at 40 keV on the spectral attenuation curve, and the identification efficiency gradually decreased at higher energies. Kraus et al. (31) found that subjectively reconstruction under 40 keV was considered to be superior in terms of lesion significance, contour, focal sharpness and diagnostic reliability with good image quality. This is consistent with the results of a series of studies on head and neck, thyroid, liver, pancreas, kidney and intestinal lesions (32-37).

People might wonder whether spectral parameters at a point in time can replace dynamically acquired perfusion parameters. However, our results showed a significant correlation between perfusion (BV, BF, and MTT) and spectral parameters (CT_40keV, IC, Zeff, and K_70keV), whereas PS was not significantly correlated with spectral parameters in AP but was weakly correlated with CT_40keV, IC, Zeff, and K_70keV in the VP. This may be due to the use of the same scanning protocol for all the patients or the small sample size of our study. In some patients, the contrast agent did not completely fill the blood vessels and intercellular spaces because of the slow blood circulation during AP. Previous studies have shown that the spectra are significantly correlated with perfusion parameters in colorectal cancer (17), liver cancer (16), and pig lung model (38). Therefore, we can cautiously consider using that spectral CT to replace perfusion CT in assessing hemodynamic changes in NSCLC.

The results showed that spectral (IC, Zeff, and K_70keV) and perfusion (BV, BF, MTT, and PS) parameters were significantly different between LUAD and LUSC, and that spectral CT showed better diagnostic performance than perfusion CT (AUC =0.93, sensitivity =0.94, specificity =0.83). Jia et al. compared the spectral parameters of LUAD and LUSC and found that the differences in IC, Zeff, and K between the two were statistically significant, and that the AUC of above three spectral parameters combined was 0.84 (39). Although some studies have shown that perfusion parameters have good differential diagnostic value in the histological classification of lung cancer, their diagnostic performance varies (40,41). This observation may be due to the software used by different vendors or differences in the study population. This study compared the diagnostic performance of two CT modalities based on the same study population using “one-stop” spectral combined perfusion scanning, and the results obtained were more objective and credible than those of previous studies.

Spectral CT has potential advantages in lung cancer, as it can simultaneously provide enhanced images and a variety of quantitative parameters, which can be used for both routine diagnosis and functional evaluation (16). Perfusion CT can evaluate hemodynamic changes in tumors quantitatively, especially in evaluating the efficacy of tumor anti-vascular therapy (42,43). Hemodynamic changes during treatment usually precede morphological changes. However, owing to the continuous dynamic scanning technique used in CT perfusion, limited scanning coverage and additional radiation doses are unavoidable. In addition, inaccurate perfusion parameters owing to respiratory motion artifacts limit their application in patients with suboptimal respiratory function (44). Therefore, because spectral CT can compensate for these deficiencies of perfusion CT, we propose it as an alternative to indirectly assess the tissue properties and hemodynamics of tumors in situations where low radiation is required and patients have suboptimal respiratory function.

The current had the following limitations: First, due to the low incidence of other pathological types of NSCLC, it was not included in this study, and other pathological types need to be collected and included in the future studied to infer broader and more accurate conclusions. Second, this study used the same scanning plan for all the patients and did not formulate a personalized scanning plan according to the patient’s heart and blood circulation status. Finally, because “one-stop” spectral combined perfusion scanning can only be achieved on higher-level CT, it cannot be widely implemented. However, with the rapid development of medical technology and the continuous exploration of scientific studies, this technology will certainly help in clinical diagnosis and treatment.

Conclusions

The results of our study showed a significant correlation between spectral and perfusion parameters in NSCLC, and spectral CT has a better diagnostic efficacy than perfusion CT in differentiating the histological classification of NSCLC. Therefore, in clinical practice, spectral CT may replace perfusion CT in assessing the histological properties and hemodynamics of tumors.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (No. 82071872).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-22-1206/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1206/coif). The authors report that this work was supported by the National Natural Science Foundation of China (No. 82071872). The authors have no other 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). Ethical approval was obtained from the medical ethics committee of Lanzhou University Second Hospital (No. 2021A-498) and informed consent was obtained from all patients.

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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