Spectral computed tomography multi-parameter images for differentiating between tumor tissue and inflammatory tissue in pneumonic-type lung carcinoma lesions

Introduction

Lung cancer is the second most common cancer and the leading cause of cancerrelated death worldwide (1). Lung cancer typically appears as nodules or masses on imaging; however, a subset presents with a pneumonia-like imaging features and is termed pneumonic-type lung carcinoma (PTLC). PTLC is frequently misdiagnosed in clinical practice, and even when it is correctly diagnosed, determining the extent of tumor tissue is difficult because of its high heterogeneity (2,3). This heterogeneity increases the difficulty of accurate needle placement in the viable tumor region during biopsy. Therefore, determining the extent of tumor tissue in PTLC lesions before needle biopsy is essential. Several studies have investigated PTLC using contrast-enhanced computed tomography (CT), magnetic resonance imaging, and radiomics; however, most studies have focused on differentiating PTLC from pneumonia (4-7). Further, no studies have explored methods for differentiating between tumor tissue and inflammatory tissue in PTLC lesions to guide precise needle biopsy.

In recent years, with advancements in imaging technology, dual-layer spectral detector computed tomography (SDCT) has been increasingly used in clinical practice (8,9). Compared with conventional CT, SDCT provides quantitative parameters, including iodine density (ID) and Z-effective (Zeff) values, which have shown advantages in identifying effective tumor tissue components in lung cancer and guiding biopsy procedures (10-12). Ma et al. (13) used SDCT‑derived ID to select biopsy targets in patients with large-volume lung cancer and found that spectral CT parameters can identify regions with at least 20% tumor cells. Curti et al. (14) reported that SDCT-derived Zeff can be used to guide percutaneous lung biopsy, resulting in the acquisition of more diagnostically valuable diagnostic samples and biomarker information. Therefore, spectral CT has the potential to differentiate between tumor tissue and inflammatory tissue in PTLC lesions to guide precise needle biopsy.

This study aimed to use SDCT parameters to differentiate tumor tissue from inflammatory tissue in PTLC lesions, and to provide a novel method for determining the puncture target before biopsy. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0215/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by Research Ethics Committee of Gansu Provincial Hospital (No. 2023-768), and informed consent was obtained from all individual participants.

The clinical and imaging data of 198 patients (102 patients with lung cancer and 96 patients with pneumonia) who underwent chest contrast-enhanced SDCT and transthoracic lung biopsy (TTLB) at Gansu Provincial Hospital between September 2020 and April 2024 were reviewed. The chest contrast-enhanced SDCT scans were typically performed within 3 days before the TTLB. The inclusion criteria for patients with PTLC were as follows: (I) pathological confirmation of primary lung cancer; (II) no history of any anti-tumor treatment; and (III) CT findings of patchy areas of increased density resembling pneumonia. The inclusion criteria for patients with pneumonia were as follows: (I) chest CT findings of lobar or segmental consolidation with unclear boundaries; (II) pathological confirmation of inflammation by TTLB; and (III) follow-up CT evidence of lesion absorption after anti-infective treatment. Patients with incomplete clinical or imaging data were excluded from the study. The study population selection flowchart is presented in Figure 1.

Figure 1 Flowchart of patient enrollment and selection process. CT, computed tomography; PTLC, pneumonic-type lung carcinoma; TTLB, transthoracic lung biopsy.

In addition, 30 patients with a high suspicion of PTLC were prospectively enrolled in the study. All patients underwent chest contrast-enhanced SDCT examinations for lesion assessment. Following the contrast-enhanced scans, CT-guided TTLB was performed within 3 days. The inclusion criteria for patients with suspected PTLC were as follows: (I) pneumonia-like imaging findings without lesion absorption after anti-infective treatment; (II) significantly elevated tumor marker levels, with no notable increases in white blood cell count or C-reactive protein levels, raising a strong clinical suspicion of malignant pulmonary neoplastic lesions; (III) an indication for CT-guided TTLB; and (IV) no contraindications to TTLB. The exclusion criterion was a pathological diagnosis other than lung carcinoma.

Clinical data were collected from medical records including age, sex, smoking history, fever, and cough. Inflammatory markers, including elevated white blood cell count (>9.5×10⁹/L) and C-reactive protein level (>10 mg/L), as well as tumor markers, including gastrin-releasing peptide precursor (>63.7 pg/mL), squamous cell carcinoma antigen (>1.5 ng/mL), carcinoembryonic antigen (>5 ng/mL), cytokeratin 19 fragment (>2.08 ng/mL), and neuron specific enolase (>20 ng/mL) were also recorded. All biomarker cut-off values were based on the reference intervals established by the clinical laboratory of Gansu Provincial Hospital using chemiluminescence immunoassay kits (Roche, Basel, Switzerland; Abbott, Abbott Park, IL, USA), which have been validated in the local Chinese population.

SDCT examination

All study patients underwent SDCT examinations using an IQon Spectral CT scanner (Philips, Best, The Netherlands). The patients were positioned supine on the examination bed with their arms raised above their heads. Scanning was performed using the following acquisition parameters: tube voltage, 120 kVp; automated tube current modulation (DoseRight system, Philips Healthcare) with a DoseRight Index of 20; helical pitch, 0.953; detector collimation, 64×0.625; and rotation time, 0.5 s. Two reconstruction kernels were used: the Standard (B) kernel for mediastinal window images and the Y-Detail (YB) kernel for lung window images.

During contrast-enhanced scanning, the patients were injected with a nonionic iodinated contrast medium (320 mg/mL iodixanol [Visipaque, GE Healthcare, Waukesha, USA]) at a dose of 1.0 mL/kg body weight (total volume, 50–80 mL) via the antecubital vein using a dual-head high-pressure injector at a flow rate of 3.5 mL/s, followed by a 20-mL saline flush. Intelligent contrast agent tracking threshold triggering technology was used to collect arterial-phase (AP) images. The trigger point was located at the descending aorta 2 cm below the tracheal bifurcation, with a trigger threshold of 150 Hounsfield unit (HU). AP scanning was initiated automatically after the threshold was reached, and venous-phase (VP) images were acquired 25 s after the completion of the AP scan. The scanning ranges for both the AP and VP covered the entire chest and were identical in scope. The observation window parameters were as follows: lung window width, 1,500 HU, window position, −600 HU; mediastinum window width, 400 HU; and window level, 40 HU.

Imaging analysis

Visual identification

After scanning, hybrid iterative reconstruction (iDose4, level 4) and projection spatial spectral reconstruction (Spectral, level 4) were applied to the acquired data. The reconstructed images had a slice thickness and spacing of 1 mm. Standard kernels were used for data reconstruction, and spectral base image packets were generated. AP and VP images were processed and analyzed using a post-processing workstation (IntelliSpace Portal, version 9, Philips Healthcare, Best, The Netherlands).

Two radiologists (Group 1, with 5 and 10 years of experience in radiological diagnosis, respectively) independently reviewed and analyzed the images, and recorded the lesion location, margin, presence or absence of the air bronchogram sign, and presence or absence of the angiographic sign. Both radiologists were blinded to the biopsy target areas before image evaluation. In cases of disagreement, consensus was reached through discussion.

In the 102 PTLC lesions, suspicious tumor areas (STAs) and suspicious inflammatory areas (SIAs) were visually identified on conventional CT images, 40-kV virtual monoenergetic (40-keV MonoE) images, ID images, and Zeff images. “Differentiable” was defined as the presence of a clearly distinguishable boundary between the STA and the SIA that could be manually delineated. “Non-differentiable” was defined as a small density difference between the STA and the SIA, or the presence of a vague transition zone, preventing clear delineation of the boundary between the two regions (Figure 2).

Figure 2 Visual identification of tumor tissue and inflammatory tissue in PTLC lesions. On the conventional CT image (A), no clear boundary could be identified between the STA and SIA, which was defined as “non-differentiable”. On the 40-keV MonoE image (B), ID image (C), and Zeff image (D), a clear boundary between the STA and the SIA could be distinctly delineated using a mouse cursor, which was defined as “differentiable”. The colored outline indicates that there is a clear boundary between STA and SIA. The part within the outline represents STA, and the part outside the outline represents SIA. CT, computed tomography; ID, iodine density; PTLC, pneumonic-type lung carcinoma; SIA, suspicious inflammatory area; STA, suspicious tumor area; Zeff, Z-effective.

Parameter measurements

The attenuation values measured on the conventional CT images were recorded as CT_conventional. The attenuation values on the 40-keV MonoE images were recorded as CT_40keV. The effective atomic values were recorded as Zeff. The iodine density was recorded as ID.

Regions of interest (ROIs) were placed in the STAs and SIAs in the lesions on images with clear visual delineation by the two radiologists (avoiding the blood vessels, bronchi, necrotic areas, and calcifications). Measurements were performed on the same slices according to the slice location identifiers. The copy and paste function was used to ensure consistency in ROI position, shape, and size on the selected images. The CT_conventional, CT_40keV, ID, and Zeff values were recorded.

The CT_conventional, CT_40keV, ID, and Zeff values of the punctured tumor area (TA) and the inflammatory area (IA) were measured by another radiologist based on the position of the puncture needle in the puncture images. The CT_conventional, CT_40keV, ID, and Zeff values of the AP and VP in the patients with pneumonia were measured using the same method. Each measurement was repeated three times, and the mean value was used for analysis.

After 2 weeks, 20 of the 102 PTLC patients were randomly selected, and the parameters were measured by two additional radiologists (Group 2, with 6 and 18 years of experience in lung needle biopsy, respectively). Agreement between measurements was evaluated using the intra-class correlation coefficient (ICC). The mean ROI area was 34.46±2.48 mm² (range, 29.09–40.53 mm²).

Parameter validation for tumor tissue

According to the lesion location, patients were positioned to minimize motion artifacts and maintain stable respiration. At the start of the procedure, TTLB localization images were obtained using a localization grid, and the corresponding parametric (ID) images with good discrimination were then rotated on the adjacent IntelliSpace Portal workstation to the same position. The two radiologists (Group 1) and the interventional radiologist reviewed the parametric (ID) images and TTLB localization images, and the parameter values were measured. After discussion, the STA was spatially matched with the TTLB localization images, the optimal puncture pathway (avoiding critical structures) was selected, and the final biopsy area was determined based on the predefined cut-off value. CT-guided TTLB was subsequently performed to obtain at least one tissue sample, which was stored in 10% formalin solution and sent for pathological examination.

Statistical analysis

The statistical analysis was performed using SPSS software (version 27.0). Continuous variables were described as mean ± standard deviation or median (interquartile range), as appropriate. Count data were described as the number of cases (n, %). The Pearson Chi-squared test or Student’s t-test was used to compare the characteristics of the PTLC and pneumonia groups. The Mann-Whitney U test was used to compare the parameters between the two groups. Cochran’s Q test was used to compare the discrimination of STA and SIA among different parameter images. Bonferroni correction was applied for post hoc pairwise comparisons to adjust for the risk of type I error due to multiple testing by dividing the significance level by the number of comparisons. The ICC was used to evaluate the consistency of the measurement results between the radiologists in Groups 1 and 2; an ICC of 0.75 or greater indicated good agreement. Univariate and multivariate logistic regression analyses were performed to identify the independent predictors for differentiating tumor tissue from inflammatory tissue. The receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the parameters in differentiating tumor tissue from inflammatory tissue. The Youden index (sensitivity + specificity − 1) was used to determine the optimal diagnostic threshold and select the cut-off value, with higher values indicating better performance (1= perfect discrimination, 0= no discrimination). Area under the curve (AUC) values were compared using the DeLong test. A P value <0.05 was considered statistically significant.

Results

Characteristics of patients with PTLC and pneumonia

A total of 228 patients were included in this study, of whom 147 were male and 81 were female. The mean age of the study population was 61 years (range, 20–80 years). Among the 132 patients with PTLC, 102 were included in the retrospective analysis [adenocarcinoma (n=72), squamous cell carcinoma (n=21), small cell lung cancer (n=9)], and 30 were enrolled in the prospective validation cohort [adenocarcinoma (n=23), squamous cell carcinoma (n=5), small cell lung cancer (n=2)]. The diagnosis of pneumonia in all 96 patients was confirmed by histopathological examination and follow-up CT imaging.

The clinical data and pneumonia-related signs are presented in Table 1. The patients with PTLC were significantly older than those with pneumonia (64.1±10.8 vs. 57.2±10.4 years; P<0.001). Fever (38.5% vs. 14.4%, P<0.001), elevated white blood cell counts (51.0% vs. 27.3%, P<0.001), and elevated C-reactive protein levels (81.3% vs. 62.1%, P=0.002) were more commonly observed in the patients with pneumonia than in those with PTLC. Conversely, elevated tumor marker levels were more commonly observed in the patients with PTLC than in those with pneumonia (all P<0.001). No significant differences were observed between the two groups in terms of the other characteristics (all P>0.05).

Visual identification of tumor and inflammatory tissues in PTLC lesions using different parameter images

In the AP, the number of cases in which conventional CT images, ID images, 40-keV MonoE images, and Zeff images could differentiate STA from SIA were 34, 90, 87, and 95, respectively, and the difference among the four image types was statistically significant (Q=157.48, P<0.001). In the VP, the corresponding numbers of cases were 27, 81, 71, and 84, respectively, and the difference among the four image types was statistically significant (Q=138.88, P<0.001). Post hoc pairwise comparisons with Bonferroni correction (adjusted α=0.05/6=0.0083) showed that in both the AP and VP, the ID images, 40-keV MonoE images, and Zeff images significantly outperformed the conventional CT images in differentiating STA from SIA, with all differences being statistically significant (P<0.001, after Bonferroni correction).

Comparison of parameters among STA, SIA, TA, and IA

The STA and SIA parameters in 20 PTLC cases were repeatedly measured by radiologists in Groups 1 and 2 on AP and VP images. The ICCs of all parameters were >0.80, indicating good agreement between the two groups of radiologists (Table S1).

The STAs and SIAs represented ROIs in the PTLC lesions, while the TAs represented biopsy areas in the PTLC lesions, and the IAs represented biopsy areas in the pneumonia lesions. In both the AP and VP, significant differences were observed between the STAs and SIAs, as well as between the STAs and IAs for the CT_conventional, ID, CT_40keV, and Zeff parameters (all P<0.001); however, no significant differences were observed between the STAs and TAs (all P>0.05). Additionally, no significant differences were observed between the SIAs and IAs (all P>0.05); however, significant differences were observed between the TAs and IAs (all P<0.001) (Table 2).

Efficacy of spectral parameters in differentiating TA from IA

In differentiating TA from IA, the AUC values of ID in the AP and VP were 0.925 and 0.872, respectively. The optimal cut-off value of ID in the AP was 1.47 mg/mL (specificity: 96.1%, sensitivity: 81.3%). This means that when an ID value of <1.47 mg/mL was used as a single quantitative criterion, 96.1% of the pathological findings were identified as tumor tissue. The efficacy, cut-off values, sensitivity, and specificity of the other parameters are shown in Table 3. The AUC values of the spectral parameters (ID, CT_40keV, and Zeff) in both the AP and VP were higher than those of conventional CT (Table 3, Figure 3). In the differentiation of TA from IA, all spectral parameters demonstrated superior performance compared with the CT_conventional parameter in both the AP and VP (all P<0.001) (Tables S2,S3). Multivariate logistic regression analysis showed that in the AP, CT_{conventional,} ID, and CT_40keV were independent predictors for distinguishing TA from IA in PTLC lesions, while in the VP, CT_conventional, ID, and Zeff were independent predictors (Table 4).

Figure 3 ROC curves of spectral CT parameters for differentiating the TA from the IA in PTLC lesions (A: arterial phase; B: venous phase). AUC, area under the curve; CI, confidence interval; CT, computed tomography; IA, inflammatory area; ID, iodine density; PTLC, pneumonic-type lung carcinoma; ROC, receiver operating characteristic; TA, tumor area; Zeff, Z-effective.

Accuracy of ID ≤1.47 mg/mL in the AP for verifying tumor tissue in 30 PTLC patients

The optimal spectral parameter images (AP ID images) and TTLB localization images were reviewed by two radiologists (Group 1) and the interventional radiologist who performed the TTLB procedures. The area with an ID value ≤1.47 mg/mL in the AP was selected for puncture biopsy in 30 patients with PTLC. Pathological examination confirmed lung carcinoma in 29 patients, resulting in an accuracy of 96.7% for obtaining tumor tissue (Figure 4).

Figure 4 AP contrast-enhanced CT images in a 48-year-old woman with pneumonic-type invasive adenocarcinoma. (A) Lung window image shows irregular patchy shadows in the left lower lobe with ill-defined boundaries. (B-E) Conventional CT image, 40-keV MonoE image, ID image, and Zeff image, respectively. (F) The puncture image. The green outline represents the regions of interest (ROI) when STA and SIA measure parameters. On the ID image, the ID value of the STA in the lesion was 1.22 mg/mL, whereas that of the SIA was 1.93 mg/mL. CT, computed tomography; HU, Hounsfield unit; ID, iodine density; SIA, suspicious inflammatory area; STA, suspicious tumor area; Zeff, Z-effective.

Discussion

This study evaluated the use of SDCT parameters for differentiating between tumor tissue and inflammatory tissue in PTLC lesions. The findings revealed a significant difference between conventional CT images and spectral images in differentiating between tumor tissue and inflammatory tissue. In particular, ID demonstrated good performance in differentiating between tumor tissue and inflammatory tissue in PTLC lesions in the AP (AUC =0.925). The optimal cut-off value of ID in the AP was 1.47 mg/mL, the sensitivity was 81.3%, and the specificity was 96.1%. This means that using the ID value of <1.47 mg/mL as a single quantitative criterion, 96.1% of the pathological findings may be tumor tissue. The cut-off value of ID in the AP (1.47 mg/mL) was prospectively validated in 30 PTLC patients, achieving an accuracy of 96.7% for obtaining tumor tissue. These findings suggest that SDCT images can be used to differentiate between tumor tissue and inflammatory tissue in PTLC lesions, which could significantly enhance the precision of needle biopsies in PTLC lesions.

Based on the visual assessments of patients with PTLC lesions performed by two radiologists, the SDCT-derived ID, 40-keV MonoE, and Zeff images exhibited significantly superior performance to the conventional CT images for differentiating the STA from SIA in both the AP and VP (all P<0.05). Previous studies have demonstrated that 40-keV MonoE images can be used to effectively identify pulmonary vessels and lymph nodes in the hilar region and accurately delineate the boundaries between head and neck tumors and adjacent normal tissue (15,16). The present study showed that low-level monoenergetic images can be used to distinguish between two tissues that show similar densities on conventional contrast-enhanced CT images. Our findings also demonstrated that 40-keV MonoE images enabled effective differentiation between tumor tissue and inflammatory tissue in PTLC lesions, with successful visual discrimination achieved in 87 cases during the AP and 71 cases during the VP. Additionally, Zeff images enabled visual differentiation between the STA and SIA in 95 of 102 cases (93.1%) in the AP and 84 of 102 cases (82.4%) in the VP. It showed that Zeff images could distinguish materials with similar densities and CT values, comparable to low-energy MonoE images, and achieved the highest number of cases with successful visual differentiation. This may be attributed to Zeff’s use of atomic number color-coding within the tissue, enabling the effective characterization of material composition for each pixel. Additionally, Zeff generates pseudo-color images of tissues, facilitating the differentiation of various substances by human observers (17).

In recent years, numerous studies have shown the potential value of energy CT in the differential diagnosis, staging, and prognostic evaluation of lung cancer (18). The measurement of ID plays a crucial role in the differential diagnosis of lung cancer, inflammatory lesions, and tuberculosis (11,19). Consistent with the findings of the present study, Deng et al. (20) found that the CT_40keV, CT_70keV, ID, and Zeff values of peripheral lung cancer were lower than those of focal organizing pneumonia. In both the AP and VP, the STA exhibited lower ID, CT_40keV, and Zeff values than the SIA in PTLC lesions, while the TA also showed lower values than the IA.

The pathological mechanism of pneumonia primarily involves the stimulation of capillaries in the affected area by inflammatory factors, resulting in capillary dilation, increased vascular permeability, and enhanced blood flow (21,22). Although neovascularization occurs in lung cancer lesions, the tumor-associated blood vessels are immature, lacking a proper basement membrane, resulting in uneven blood supply, hypoxia, and necrosis; thus, tumor tissue is generally less hyperemic than inflammatory tissue (23-25). Because iodine is the main component of contrast agents, ID values reflect the blood supply of lesions. This suggests that the ID of inflammatory tissue is higher than that of tumor tissue.

The results of our study showed that ID, CT_40keV, and Zeff values were more effective than CT_conventional values in differentiating tumor tissue from inflammatory tissue in PTLC lesions. The cut-off value of ID in the AP was 1.47 mg/mL, with a sensitivity and specificity of 81.3% and 96.1%, respectively. After applying this ID cut-off value (AP, 1.47 mg/mL) for prospective validation in a cohort of 30 patients with PTLC lesions, a high accuracy rate of 96.7% was achieved. These findings indicate that this cut-off value can differentiate the SIA from the STA in PTLC lesions. Therefore, future studies should explore whether AP-only scanning could be performed to reduce examination costs and radiation exposure for patients.

Our study was based on dual-phase (AP and VP) CT images. Dual-phase CT is not standard at all institutions; many centers acquire only a single contrast-enhanced phase. We found that the diagnostic performance of the AP parameters was superior to that of the VP parameters. Therefore, our findings may be applicable to routine single-phase AP chest CT. However, direct validation in a single-phase cohort is required before widespread clinical adoption. Institutions that use VP-only imaging should interpret our findings with caution.

This study had several limitations. First, it was a retrospective single-center study with a relatively small sample size. Second, the distinction between the STA and SIA in this study was based on visual identification, which may be subject to interobserver variability. To assess this variability, two additional radiologists independently remeasured the parameters in 20 patients, yielding a moderate ICC of 0.80. Future prospective studies should incorporate independent dual measurements across the entire cohort to directly assess the impact of observer variability on diagnostic performance. Third, this study did not consider the impact of histological types of lung cancer on the results. Fourth, because biopsy was not performed for the SIA in the PTLC lesions, we included cases of pneumonia confirmed by biopsy and compared the spectral parameters of the inflammatory puncture areas with those of the tumor puncture areas in the PTLC lesions. This approach was consistent with the methodology of Baysal et al. (26). Fifth, we only evaluated 40-keV MonoE images. Higher keV levels may provide complementary information, and future studies should investigate a broader range of keV levels to determine the optimal energy setting for characterizing pneumonia-type lung cancer. Finally, contrast-enhanced CT has potential drawbacks such as contrast-induced nephropathy and allergic reactions. However, contrast-enhanced CT remains essential for accurate and safe TTLB. Therefore, the risk-benefit balance should be carefully considered based on renal function, allergy history, and diagnostic needs.

Conclusions

Spectral CT multi-parametric images can be used to differentiate between tumor tissue and inflammatory tissue in PTLC lesions, which may help determine the extent of tumor involvement and accurately guide the target area for biopsy.

Acknowledgments

The authors thank Professor Lu Zhang of Xi’an International Studies University.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0215/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0215/dss

Funding: This work was supported by the Natural Science Foundation of Gansu Province (No. 21JR7RA605); and the Gansu Provincial Department of Education (Nos. 2025B-194 and 2025B-190).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0215/coif). X.Y.Z. works as a full-time employee at Philips Healthcare China, a manufacturer of spectral CT systems. 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. The study was approved by Research Ethics Committee of Gansu Provincial Hospital (No. 2023-768) 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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