The value of dual-layer detector spectral CT multiparameter imaging in distinguishing between benign lesions and carcinomas of the larynx and hypopharynx

Introduction

Common benign lesions of the larynx and hypopharynx include polyps, papillomas, polypoidal degeneration, and cysts (1), while the most prevalent malignancy is squamous cell carcinoma. These conditions share similar clinical presentations, with hoarseness being the most frequently reported symptom. The main treatment options for laryngeal and hypopharyngeal benign lesions (LHBLs) include medication, voice therapy, and surgical resection (2,3). Conversely, laryngeal and hypopharyngeal carcinomas (LHCs) require more radical treatment, such as partial or total laryngectomy, often in combination with other treatment modalities. Therefore, accurately distinguishing between benign and malignant lesions in the larynx and hypopharynx is crucial for developing individualized and precise treatment plans.

Computed tomography (CT) is widely used in the diagnosis of laryngeal and hypopharyngeal lesions, and plays an important role in the formulation of clinical treatment plans (4-6). However, due to the low soft-tissue resolution of conventional CT, the subjectivity of diagnosis by radiologists, and the complexity of the anatomical structure of the larynx and hypopharynx, its diagnostic accuracy for laryngeal and hypopharyngeal lesions is not satisfactory in clinical practice. Dual-energy CT enables the acquisition of high- and low-energy X-ray attenuation data, enabling the reconstruction of single-energy images. It also facilitates the differentiation of tissues with similar attenuation at a single energy level, enabling material decomposition and the reconstruction of water equivalent density and contrast agent concentration images (7,8). Three main approaches are used to achieve energy separation in CT: the use of a single X-ray tube to generate different X-ray spectra; the use of two X-ray tubes operating at different voltages; and the use of different detectors to acquire information about the incident X-ray spectrum (8).

Dual-layer detector spectral computed tomography (DLSCT) enables the acquisition of conventional images (CIs) and spectral images in a single scan, including virtual monochromatic images (VMIs), virtual non-contrast (VNC) images, iodine no water (INW) images, effective atomic number (Zeff) maps, and iodine density (ID) maps (9). According to previous reports (10-12), low-energy VMIs can improve image quality, reduce artifacts, and increase the contrast between the lesion and surrounding tissues, or between different lesions. Owing to the advantages mentioned above, the clinical application of DLSCT in disease diagnosis, treatment, and follow-up has gradually increased (13-17).

Notably, the diagnostic value of DLSCT in distinguishing between LHBLs and LHCs is unclear. Therefore, this study aimed to evaluate the utility of quantitative DLSCT multiparameters for distinguishing between LHBLs and LHCs and to identify the optimal DLSCT parameter by comparing the diagnostic performance of different parameters. We hypothesized that the quantitative analysis of DLSCT multiparameters could improve diagnostic accuracy and facilitate the selection of clinical treatment plans for patients with laryngeal and hypopharyngeal disease. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2621/rc).

Methods

Patients

This retrospective study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Ethics Committee of The Affiliated Hospital of Southwest Medical University, and the requirement for informed consent was waived. Patients at The Affiliated Hospital of Southwest Medical University with pathologically confirmed (gold standard) laryngeal and hypopharyngeal lesions, who underwent preoperative DLSCT, were consecutively enrolled in the study between October 2020 and June 2023. The inclusion criteria were as follows: (I) contrast-enhanced DLSCT of the neck or throat, including arterial phase (AP) and venous phase (VP) imaging, performed within 2 weeks before surgery or biopsy; (II) pathologically confirmed LHBL or LHC; and (III) no history of anti-tumor treatments in the head and neck region before CT scanning, including surgery, radiotherapy or chemotherapy. The exclusion criteria were as follows: (I) poor-quality or incomplete images; (II) regions of interest (ROIs) that could not be delineated, such as lesions that were too small or affected by vascular structures or imaging artifacts; and/or (III) incomplete measurement data. Figure 1 provides a detailed flow diagram of the patient recruitment and exclusion process. According to the International Classification of Diseases for Oncology, laryngeal and hypopharyngeal diseases were categorized as LHBLs and LHCs (18).

Figure 1 The patient recruitment and exclusion process for this study. CT, computed tomography; DLSCT, dual-layer detector spectral computed tomography; LHBL, laryngeal and hypopharyngeal benign lesion; LHC, laryngeal and hypopharyngeal carcinoma.

Scan protocol and image postprocessing

All patients underwent AP and VP contrast-enhanced scans of the neck or throat using the same DLSCT scanner (IQon spectral CT; Philips Healthcare). The scanning parameters were set as follows: 120 kilovolt peak (kVp) tube voltage with automatic exposure control for tube current adjustment, a 0.6-second rotation time, 64×0.625 mm collimation, and a pitch of 0.953. Patients received 55–60 mL of iodinated contrast medium (Iopamidol, 370 mg iodine/mL) via intravenous injection at a rate of 3 mL/s. AP and VP scans were acquired at 28–30 and 55–60 seconds after contrast injection, respectively.

Following data acquisition, hybrid iterative reconstruction (iDose4, level 4) was used to generate 120-kVp CIs, and projection-space spectral reconstruction (Spectral 4) was used to generate spectral-based images. The image reconstruction layer thickness was 0.8 mm, with a layer spacing of 0.8 mm. All scanning data were transferred to a dedicated postprocessing workstation for further image analysis using the manufacturer’s image viewing and postprocessing software (IntelliSpace Portal 9.0, Philips Healthcare) with spectral reconstruction algorithms. This process generated VMIs from 40–100 kiloelectron volt (keV) (in 10 keV intervals), VNC images, INW images, ID maps, and Zeff maps.

Image analysis

The maximum layer of each lesion was selected by a radiologist with three years of experience, and ROIs were drawn within the homogeneous solid portion of the lesion, avoiding large blood vessels, necrotic areas, and bony structures. The quantitative value of the ROI area, ranging from 3–15 mm², was automatically calculated by the post-processing software. All measurements were repeated by the same radiologist one week after the initial assessment, and the average values were used for analysis. The measurements of the lesions were conducted on the reconstructed images described above. In addition, the ID and Zeff of the carotid artery in the same layer were also measured to calculate the standardized iodine density (sID) and standardized effective atomic number (sZeff). Representative ROI delineation diagrams for LHBLs and LHCs are shown in Figure 2. The maximum lesion diameter (MLD) was also measured at the maximum layer of each lesion. When uncertainty arose regarding the location of the ROI, a senior radiologist with more than 20 years of experience was consulted for guidance. Both the pathologist and the radiologist were blinded to the pathological outcomes prior to their evaluations. The spectral curve slope (λ), sID, and sZeff were calculated using the following formulas (19):

λ=(HU40keV−HU100keV)/60[1]

sID=IDlesion/IDcarotidartery[2]

sZeff=Zefflesion/Zeffcarotidartery[3]

Figure 2 Spectral CT images in the venous phase of a patient with LHBL (laryngeal papilloma) on the right vocal cord, and a patient with LHC on the left vocal cord, including CI, 40 keV VMIs, INW images, ID maps, and Zeff maps. CI, conventional image; CT, computed tomography; ID, iodine density; INW, iodine no water; keV, kiloelectron volt; LHBL, laryngeal and hypopharyngeal benign lesion; LHC, laryngeal and hypopharyngeal carcinoma; VMI, virtual monochromatic image; Zeff, effective atomic number.

Statistical analysis

Kolmogorov-Smirnov and Shapiro-Wilk tests were used to assess data normality, and homogeneity of variance was evaluated for each spectral data set. For comparisons of normally distributed data, the independent-samples t-test was used. For comparisons of non-normally distributed data, the Mann-Whitney U test was used. Interobserver repeatability was assessed using the intraclass correlation coefficient (ICC) for all quantitative CT parameters. The diagnostic performance of each spectral parameter was evaluated by receiver operating characteristic (ROC) curve analysis, and the DeLong test was then used to assess the statistical significance of differences in diagnostic performance. The parameters with the highest area under the curve (AUC) values in the AP and VP were integrated via logistic regression. Decision curve analysis (DCA) was performed to examine the clinical utility of each parameter. R software (version 4.2.1, http://www.R-project.org) and SPSS (version 26.0, IBM Corporation) were used for all tests. A P value <0.05 was considered statistically significant. For multiple comparisons in the univariate analysis, Bonferroni correction was applied, with a corrected threshold of P<0.002.

Results

Clinical data

A total of 141 patients (138 male and 3 female) were included in the study. The clinicopathological characteristics of all the patients are summarized in Table 1. The patients ages ranged from 34–87 years. There were 35 patients with LHBLs, including 8 patients with bilateral lesions, resulting in a total of 43 lesions, comprising 22 polyps and 21 hyperplastic nodules. Details of the specific types of LHBLs are provided in Table S1. There were 106 patients with LHCs, with a total of 106 lesions evaluated. There were no statistically significant differences in age and sex between the two groups (P>0.002).

Differences in the quantitative spectral CT parameters between the LHBLs and LHCs

With the exception of VNC-AP, sZeff-VP, and sZeff-AP, all the measured spectral CT parameters differed significantly between the two groups (P<0.002). Specifically, the parameter values of the LHCs were higher than those of the LHBLs in both the AP and VP (Table 2 and Figure 3). Among the 40–100 keV VMIs, CIs, and VNC images, the greatest difference in the CT values between the LHBLs and LHCs was observed on the 40 keV VMI (Figure 4), with differences of 59.79 HU and 55.35 HU in the AP and VP, respectively. In addition, all ICC values for the spectral CT parameters exceeded 0.8, demonstrating excellent intra-observer reliability.

Figure 3 Comparison of each spectral CT parameter between LHBLs and LHCs. *, P<0.05; **, P<0.01; ***, P<0.001. AP, arterial phase; CI, conventional image; CT, computed tomography; ID, iodine density; INW, iodine no water; keV, kiloelectron volt; LHBLs, laryngeal and hypopharyngeal benign lesions; LHCs, laryngeal and hypopharyngeal carcinomas; sID, standardized iodine density; sZeff, standardized effective atomic number; VNC, virtual non-contrast; VP, venous phase; Zeff, effective atomic number; λ, slope of the spectral curve.

Figure 4 Line chart of CT values for LHBLs and LHCs on CI, 40–100 keV VMIs, and VNC images. AP, arterial phase; CI, conventional image; CT, computed tomography; keV, kiloelectron volt; LHBLs, laryngeal and hypopharyngeal benign lesions; LHCs, laryngeal and hypopharyngeal carcinomas; VMI, virtual monochromatic image; VNC, virtual non-contrast; VP, venous phase.

Diagnostic performance of single parameters for distinguishing between LHBLs and LHCs

For the AP, the AUC values of the 40–70 keV VMIs, INW, Zeff, ID, and λ were greater than that of the CI, but the differences were not statistically significant (P>0.05). The 40 keV VMI achieved the highest AUC of 0.870, with a sensitivity, specificity, and accuracy of 0.774, 0.837, and 0.792, respectively. The AUC values of the 80–100 keV VMIs, VNC, sZeff, and sID were lower than that of the CI.

In the VP, the AUC values of the 40–70 keV VMIs, INW, Zeff, ID, and λ were greater than that of the CI, but only the AUC values of the 40–60 keV VMIs differed significantly from that of the CI (P<0.05). The 40 keV VMI achieved the highest AUC of 0.932, with a sensitivity, specificity, and accuracy of 0.774, 0.930, and 0.819, respectively, yielding a cut-off value of 160.25 HU. The AUC values of the 80–100 keV VMIs, VNC, sZeff, and sID were lower than that of the CI.

There was no significant difference in the AUC values between the 40 keV-VP and 40 keV-AP VMIs (P=0.058). The diagnostic performances of the spectral CT parameters for distinguishing between the LHBLs and LHCs are shown in Table 3 and Figure 5.

Figure 5 Comparison of the diagnostic performance of various quantitative parameters, including the AUC (A), ACC (B), SEN (C), and SPE (D). Heatmap of the P values of the AP (E) and VP (F) parameters according to the Delong test. *, P<0.05; **, P<0.01; ***, P<0.001. ACC, accuracy; AP, arterial phase; AUC, area under the curve; CI, conventional image; ID, iodine density; INW, iodine no water; keV, kiloelectron volt; SEN, sensitivity; sID, standardized iodine density; SPE, specificity; sZeff, standardized effective atomic number; VNC, virtual non-contrast; VP, venous phase; Zeff, effective atomic number; λ, slope of the spectral curve.

There was a significant difference (P<0.001) in the MLD between the LHBLs (0.620±0.292 cm) and the LHCs (1.598±1.045 cm). The AUC of the MLD for distinguishing between LHBLs and LHCs was 0.832, which was significantly lower than that of the 40 keV-VP VMI (P=0.001); however, there was no significant difference between the AUC of the MLD and that of the 40 keV-AP VMI (P=0.354).

Diagnostic performance of the combined models

Table 4 shows the diagnostic efficiency of the four combined models that included the MLD, 40 keV-AP VMI, and 40 keV-VP VMI. Among them, the model that combined the 40 keV-AP VMI, 40 keV-VP VMI, and MLD had the highest AUC of 0.970. This value was significantly greater than that of the model that combined the 40 keV-AP VMI and MLD (P=0.009), whereas there was no statistically significant difference between it and the model that combined the 40 keV-VP VMI and MLD (P=0.403). Notably, the AUCs of all the combined models were significantly greater than those of the CIs of the same phase (P<0.05), as depicted in Figure 6A. Moreover, the DCA demonstrated that the net clinical benefits of the combined models were greater than those of the CIs. Among them, the model that combined 40 keV-AP VMI, 40 keV-VP, VMI, and the MLD had the greatest net clinical benefit, but it was only superior to the model that combined the 40 keV-VP VMI and the MLD within certain risk threshold ranges, as shown in Figure 6B.

Figure 6 The ROC curves (A) and DCA curves (B) for the MLD, CI-AP, CI-VP, 40 keV-AP VMI, 40 keV-VP VMI, 40 keV-AP-MLD, 40 keV-VP MLD, and 40 keV-VP-AP MLD. AP, arterial phase; AUC, area under the curve; CI, conventional image; DCA, decision curve analysis; keV, kiloelectron volt; MLD, maximum lesion diameter; ROC, receiver operating characteristic; VMI, virtual monochromatic image; VP, venous phase.

Discussion

In this study, we conducted a quantitative analysis of DLSCT multiparametric images and compared their diagnostic performance in distinguishing between LHBLs and LHCs. The present study revealed that low-energy VMIs, especially the 40 keV VMI, exhibited better diagnostic performance than CIs, and were more suitable for detecting benign and malignant laryngeal and hypopharyngeal diseases. Further, the AUC increased to 0.970 when the 40 keV-VP VMI, 40 keV-AP VMI, and the MLD were combined in a model, which was significantly greater than that of the CI. The DCA revealed that this combined model also had great net clinical benefit.

The differential diagnosis of LHBLs and LHCs can be difficult in clinical practice before surgery. Currently, the main examination methods for larynx and hypopharyngeal diseases are laryngoscopy, CT, and magnetic resonance imaging (MRI) (4). Laryngoscopy is an invasive procedure that allows for the observation of vocal cord mobility and tissue biopsy; however, it cannot assess the tumor depth of disease infiltration or distant metastasis. Several studies have shown the advantages of MRI (20); however, it requires a longer time and is susceptible to artifacts. CT is a first-line imaging modality for diagnosing laryngeal and hypopharyngeal diseases; however, its accuracy in differentiating between benign and malignant lesions of the larynx and hypopharynx needs to be improved.

DLSCT can generate multiparametric images through post-processing technology, providing richer information. In the present study, we quantitatively analyzed CI and spectral CT images acquired from the same DLSCT and found that the LHC measurements were significantly higher than the LHBL measurements across all types of images. This may be due to the richer blood supply of malignant lesions compared with that of benign lesions, leading to increased iodine uptake after enhancement. This finding is consistent with previous studies on the differentiation of benign and malignant diseases in the head and neck region (19,21,22). Further, a notable difference in CT values was observed on the 40 keV VMI, which may be due to the enhanced attenuation contrast and improved contrast-to-noise ratio at lower energy levels (23-25). Previous studies have shown that the CIs reflect only the relative values of X-rays after attenuation through human tissues, where different tissues may have similar CT values at the same tube voltage. DLSCT provides additional information on the energy-dependent changes in material attenuation during the process of tissue attenuation, improving the contrast between tissues through energy conversion (26,27).

This study revealed that the diagnostic performance of the 40–60 keV VMIs in identifying LHBLs and LHCs was significantly superior to that of the CIs in the VP, with the 40 keV VMI exhibiting the highest diagnostic performance (AUC =0.932). Although there was no significant difference in the diagnostic performance of the spectral CT parameters in the AP compared with that of the CI, low-energy VMIs, especially the 40 keV VMI, showed a tendency toward improved diagnostic performance, with an AUC of 0.870, which was higher than that of the CI. These results indicated that low-energy VMIs with high signal-to-noise ratios were more advantageous than CIs for the differential diagnosis of LHBLs and LHCs. These findings are consistent with those of previous studies on the differential diagnosis of benign and malignant lesions in other organs, such as the bladder and orbit (28,29), which have suggested that low-energy VMIs exhibit the best diagnostic performance among these spectral CT parameters.

DLSCT also has high diagnostic value in the differentiation of benign and malignant lesions in the lung, thyroid, and breast (30-32). The relatively lower performance of the sID and sZeff may be attributed to their distinct sensitivities to tissue composition and the relatively low spatial resolution of the corresponding images (33). The sID reflects the absolute iodine concentration. However, its accuracy in identifying laryngeal lesions may be limited by partial volume effects and motion artifacts, particularly in the small anatomical structures of the larynx. The sZeff reflects the mean atomic number of tissues and is highly sensitive to calcium, air, and fibrotic components. Benign laryngeal lesions frequently contain calcifications or dense fibrous tissue, leading to sZeff values that can overlap with those of malignant tumors. This overlap likely reduces the discriminative ability of the sZeff, which is consistent with previous studies reporting variable performance of atomic number-based parameters in head and neck applications (34).

The biphasic injection protocol used in our study reflects standard practice in head and neck oncology. In head and neck imaging practice, the AP is primarily used to assess vascular anatomy, arterial feeders, and vascular invasion, whereas the VP is considered optimal for tumor parenchymal characterization, soft-tissue delineation, and nodal assessment. Our acquisition parameters were consistent with institutional and literature-based protocols, ensuring clinical relevance and comparability with previous studies.

In the present study, VP imaging demonstrated superior diagnostic performance compared with AP imaging. These findings reflect the contrast kinetics of laryngeal and hypopharyngeal lesions. LHCs are characterized by immature, hyperpermeable neovasculature with disrupted basement membranes. Following intravenous contrast injection, the AP primarily reflects intravascular distribution, whereas the VP captures the phase of contrast extravasation and interstitial retention, during which the contrast medium accumulates within the tumor stroma due to increased vascular permeability and impaired lymphatic drainage. Simultaneously, normal surrounding tissues, such as muscles, exhibit progressive washout during this phase, further increasing lesion-to-background contrast. This hemodynamic profile is well documented in head and neck oncology, and our findings align with those of previous studies demonstrating that quantitative parameters derived from VP acquisitions provide higher diagnostic accuracy for lesion characterization (35).

In addition, we further conducted combined analyses of 40 keV-AP and 40 keV-VP VMIs, which had the highest AUC values in the AP and VP, respectively. The results revealed that the AUC of the combined analysis increased to 0.947, which was greater than that of the 40 keV-AP VMI, while not differing significantly from that of the 40 keV-VP VMI. Although there was no significant difference in the AUC values between the 40 keV-VP and 40 keV-AP VMIs, the DCA demonstrated that the overall net benefit of the 40 keV-VP VMI was greater than that of the 40 keV-AP VMI alone. When these two parameters were further combined with the MLD, the AUC of the combined model was further improved. In terms of clinical applicability, the 40 keV-VP VMI appears optimal for distinguishing between LHBLs and LHCs, as it meets the diagnostic requirements while also reducing the examination time and radiation dose. Notably, diagnostic efficacy was further improved when the VMI was combined with the MLD.

Notably, histopathological biopsy remains the gold standard for diagnosing laryngeal and hypopharyngeal lesions, and our model is not intended to replace it but rather to serve as a reliable non-invasive adjunct for preoperative assessment. Direct laryngoscopic biopsy is associated with procedural complications, and even minimally invasive biopsies may be limited by sampling failure or inconclusive results, underscoring the clinical need for complementary non-invasive diagnostic approaches (36). Our multiparametric quantitative analysis and combined model could provide clinical value in critical scenarios. Specifically, they may provide objective supplementary evidence to guide decisions regarding further invasive testing in patients with suspected lesions or indeterminate pathological findings, they may facilitate targeted biopsy site selection, thereby improving diagnostic yield and reducing blind sampling, and they may help identify subtle lesions indistinguishable from normal anatomical structures on conventional CT, thereby reducing misdiagnosis rates and the need for unnecessary follow-up or invasive interventions.

This study had several limitations. First, the relatively small sample size restricted the analyses to univariate methods, which may increase confounding bias. Thus, larger studies are needed to improve the reliability of the results. Second, the benign lesions included two types of diseases, which may have resulted in heterogeneity. Third, this was a single-center study that used only one type of dual-energy CT scanner, and the results may be affected by selection bias. Fourth, selection bias may have been introduced by the exclusion of very small lesions that could not be delineated, as well as cases with poor-quality or incomplete images, or incomplete measurement values, which may have led to the overestimation of diagnostic performance of CT in routine clinical practice. Fifth, while the combined model offered an incremental improvement over single spectral CT parameters, external validation in independent cohorts is needed to confirm its stability and generalizability. Subsequent investigations should seek to integrate automated lesion delineation or deep learning methods and explore the potential of various dual-energy CT devices to increase the generalizability of these findings. In addition, future studies should compare the performance of DLSCT with that of other imaging modalities, such as MRI and positron emission tomography.

Conclusions

Our study demonstrated that spectral CT images derived from DLSCT provide superior diagnostic performance in distinguishing between LHBLs and LHCs compared with conventional CT. Notably, the 40 keV-VP VMI showed favorable diagnostic value and clinical utility, and may assist in formulating individualized and precise treatment plans for patients with laryngeal and hypopharyngeal diseases.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2621/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2621/dss

Funding: This study was supported by the Sichuan Science and Technology Program of China (grant No. 2025ZNSFSC0646), the Sichuan University & Luzhou Collaborative Foundation (grant Nos. 2017CDLZ-G27 and 2018CDLZ-11), the Project of Administration of Traditional Chinese Medicine of Sichuan Province (grant No. 2023MS083), and the Luzhou Science & Technology Department (grant No. 2022-SYF-60).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2621/coif). G.C. reports funding from the Sichuan Science and Technology Program of China (grant No. 2025ZNSFSC0646), Sichuan University & Luzhou Collaborative Foundation (grant Nos. 2017CDLZ-G27 and 2018CDLZ-11), Project of Administration of Traditional Chinese Medicine of Sichuan Province (grant No. 2023MS083), and Luzhou Science & Technology Department (grant No. 2022-SYF-60). The other 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 and its subsequent amendments. This retrospective study was approved by the Ethics Committee of The Affiliated Hospital of Southwest Medical University, and the requirement for informed consent was waived.

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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