Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent

Introduction

Since the concept of radiomics was first introduced in 2012 (1,2), it has captured the interest of scholars from a variety of disciplines. Radiomics is an innovative method for converting imaging examination data into more complex quantitative information using automated algorithms (3-5). Coupled with the advancement of tools for pattern identification, and the expansion of data set sizes, radiomics could improve the predictive accuracy of individualized treatment selection and monitoring in oncology (6,7). Recent studies have shown that radiomics can aid in the differentiation of benign and malignant lung nodules, and in the prediction of lung cancer prognosis (8-15). For example, Lin et al. analyzed 180 cases and established a radiomic model for differentiating between benign and malignant sub-centimeter pulmonary nodules (12). Further, in a number of research studies, radiomics has been shown to be effective in predicting gene mutations, such as the epidermal growth factor receptor mutation (16), and Kirsten rat sarcoma viral oncogene homolog mutation (17).

However, despite numerous radiomic studies, radiomics has not yet been established as a clinically useful tool (18). The reproducibility of the results obtained by radiomics is a major research challenge. Several phantom studies have analyzed the effects of computed tomography (CT) acquisition parameters on radiomics using non-uniform CT image acquisition protocols between scanners, and shown that different CT scanners or protocols affect radiomic results (19-22). However, due to the inherent significant differences between phantom material and human tissue, phantom studies cannot truly simulate the radiomic features (RFs) that can be obtained from actual lesions. Few studies have examined the effects of CT reconstruction algorithms, radiation doses, and contrast agents on the RFs of pulmonary nodules using patient images.

Therefore, we conducted a prospective study on individual CT scan acquisition parameters to compare the effects of different reconstruction algorithms, radiation doses, and contrast agents on the reproducibility of RFs in pulmonary nodules. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2026/rc).

Methods

Patient selection

This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013), and was approved by the Medical Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (No. NCC19-018/1840). Informed consent was obtained from all patients. Patients who underwent chest CT scans with pulmonary nodules at the Cancer Hospital of the Chinese Academy of Medical Sciences between July 2018 and March 2019 were included in this study (Figure 1). To be eligible for inclusion in the study, the patients had to meet the following inclusion criteria: (I) have undergone both non-contrast enhanced (NE) and contrast-enhanced (CE) chest CT examinations; and (II) be known to have or suspected of having pulmonary nodules based on the results of the previous multidetector CT scan. Patients were excluded from the study if they met any of the following exclusion criteria: (I) had CT images that showed no nodules in the lungs; (II) had a lesion size <5 or >30 mm; and/or (III) had a lung nodule boundary that was difficult to segment accurately.

Figure 1 Flow diagram showing patient selection. CT, computed tomography.

Data reconstruction

All collected CT images were obtained using a 256-Multidetector Computed Tomography (MDCT) (Revolution CT, GE Healthcare, Milwaukee, WI, USA). The scanning range was from the top to the bottom of the lungs. The following scanning parameters were employed: 120 kVp and auto milliampere (pitch: 0.992 and slice thickness: 1.25 mm). The CE scan was performed using a pressure syringe with a peripheral intravenous injection of 85 mL of a non-ionic iodine contrast agent (300 mg/mL) at a rate of 3 mL/s. Scanning began 35 s after the intravenous injection. The three pairs of different scanning parameters were filtered back projection (FBP) and 50% adaptive statistical iterative reconstruction-V (ASiR-V); low dose [noise index (NI) =40] and standard dose (NI =10); and NE CT and CE CT. By altering only one of the three parameters and holding the other two constant, the following three pairs of comparison sequences were obtained: sequence pair 1—the reconstruction algorithm was modified, and the radiation dose and contrast agent remained constant; sequence pair 2—the radiation dose was modified, and the reconstruction algorithm and contrast agent remained constant; sequence pair 3—the contrast agent was modified, and the reconstruction algorithm and radiation dose remained constant. Figure 2 shows the three pairs of comparison sequences for three different lung nodules.

Figure 2 Three pairs of comparison sequences for a pGGN, PSN, and SN. FBP, filtered back projection; ASiR-V, adaptive statistical iterative reconstruction-V; NE, non-contrast enhanced CT; CE, contrast-enhanced CT; pGGN, pure ground-glass nodule; PSN, part-solid nodule; SN, solid nodule; CT, computed tomography.

Segmentation and RF extraction

All the nodules were segmented manually by a radiologist (S.X.Y.) with five years of diagnostic experience using ITK-SNAP software (http://www.itksnap.org/). The segmentation included the whole nodule, starting from the first layer and continuing to the last layer where the nodules disappeared. In addition, we tried to include the largest edges of the nodules during the segmentation of each layer. In total, 60 pulmonary nodules, including 20 pure ground-glass nodules (pGGNs), 20 part-solid nodules (PSNs), and 20 solid nodules (SNs), were segmented again by the same radiologist at an interval of 2 weeks to assess consistency. The extraction and analysis of the RFs were performed using the Artificial Intelligence Kit software provided by the GE Healthcare Life Sciences Company (Chicago, IL, USA). Ultimately, 391 RFs were extracted, including 42 histogram, 15 morphological, and 334 texture features (for further details on the 391 RFs, see Figure S1).

Statistical analysis

All the statistical analyses were performed using R software (R foundation for Statistical Computing, Vienna, Austria). The intraclass correlation coefficient (ICC) was used to evaluate the test-retest reliability of the RFs. An ICC of >0.75 indicated good reliability. A paired-sample t-test or Wilcoxon test was used to compare the reproducibility of the RFs between different sequences. The Chi-squared test was used to evaluate the difference in the number of RFs that varied with changes in the reconstruction parameters. Benjamini-Hochberg adjustment was applied to control the false discovery rates for multiple comparisons. A P value <0.05 was considered statistically significant.

Results

Patient demographics and RF selection

A total of 150 pulmonary nodules in 96 patients (mean age: 52±10 years; 62 females) were included in the study. The mean nodule size was 8.7±4.5 mm. Of the 150 pulmonary nodules, 52 were in the right upper lobe, 12 were in the right middle lobe, 27 were in the right lower lobe, 32 were in the left upper lobe, and 27 were in the left lower lobe. Among the 150 pulmonary nodules, 50 were pGGNs (mean age: 48.8±9.3 years; 25 females), 50 were PSNs (mean age: 52±10 years; 30 females), and 50 were SNs (mean age: 53.4±11.3 years; 18 females) (Table 1).

Of the 391 extracted features, 320 RFs with an ICC >0.75 were ultimately selected for analysis, including 40 histogram, 15 morphological, and 265 texture features. For further details on the 320 RFs, see Table S1.

Effects of reconstruction algorithms, radiation doses, and contrast agents on the RF reproducibility of pulmonary nodules

As Table 2 shows, when the reconstruction algorithm was modified, there was a significant difference in 38.4% (123/320) of the RFs. When the radiation dose was modified, there was a significant difference in 63.1% (202/320) of the RFs. When the contrast agent was modified, there was a significant difference in 54.1% (173/320) of the RFs. The alteration rates of the RFs due to the radiation dose and contrast agent were essentially the same (P=0.060). The alteration rates of the RFs due to radiation dose and contrast agent were found to be higher than that of the reconstruction algorithm (both P<0.001).

The proportion of histogram features affected by reconstruction algorithm, radiation dose, and contrast agent was 35.0% (14/40), 10.0% (4/40), and 92.5% (37/40) (P<0.001). The proportion of morphological features affected by reconstruction algorithm, radiation dose, and contrast agent was 20.0% (3/15), 26.7% (4/15), and 33.3% (5/15) (P=0.912). The proportion of texture features affected by reconstruction algorithm, radiation dose, and contrast agent was 40.0% (106/265), 73.2% (194/265), and 49.4% (131/265) (P<0.001).

Subgroup analysis on the effects of the reconstruction algorithm, radiation dose, and contrast agent on nodule consistency in RF reproducibility

As Table 3 and Figure 3 show, 37.5% (120/320), 12.8% (41/320), and 44.7% (143/320) of the RFs differed significantly between the CT images reconstructed with FBP and those reconstructed with 50% ASiR-V for the pGGNs, PSNs, and SNs, respectively (P<0.001). While 46.3% (148/320), 8.4% (27/320), and 42.2% (135/320) of the RFs differed significantly between the low-dose CT images and standard-dose CT images for the pGGNs, PSNs, and SNs, respectively (P<0.001). Additionally, 50.6% (162/320), 13.8% (44/320), and 41.3% (132/320) of the RFs differed significantly between the CE CT images and NE CT images for the pGGNs, PSNs, and SNs, respectively (P<0.001).

Figure 3 Comparison of the effects of the reconstruction algorithm, radiation dose, and contrast agent on the RFs of pGGNs, PSNs and SNs. pGGN, pure ground-glass nodule; PSN, part-solid nodule; SN, solid nodule; RF, radiomic feature.

In the GGNs, among the 120 RFs that showed significant differences between the CT images reconstructed with FBP and those reconstructed with 50% ASiR-V, the proportion of histogram, morphological, and texture features were 37.5% (15/40), 6.7% (1/15), and 39.2% (104/265), respectively, and the histogram and texture features showed more variability than the morphological features (P=0.168, 0.033). Among the 148 RFs that showed significant differences between the low-dose CT images and standard-dose CT images, the proportion of histogram, morphological and texture features were 10.0% (4/40), 13.3% (2/15), and 53.6% (142/265), respectively, and the texture features showed the greatest variability (P=0.006, <0.001). Among the 162 RFs that showed significant differences between the CE CT images and NE CT images, the proportion of histogram, morphological, and texture features were 92.5% (37/40), 6.7% (1/15), and 46.8% (124/265), respectively, and the histogram features showed the greatest variability (both P<0.001).

In the PSNs, among the 41 RFs that showed significant differences between the CT images reconstructed with FBP and those reconstructed with 50% ASiR-V, the proportion of histogram, morphological, and texture features were 10.0% (4/40), 6.7% (1/15), and 13.6% (36/265), respectively, and the texture features were found to be relatively more affected, but the difference was not statistically significant (P=0.628). Among the 27 RFs that showed significant differences between the low-dose CT images and standard-dose CT images, the proportion of histogram, morphological and, texture features were 2.5% (1/40), 20.0% (3/15), and 8.7% (23/265), respectively and the morphological features were found to be relatively more affected, but the difference was not statistically significant (P=0.095). Among the 44 RFs that showed significant differences between the CE CT images and NE CT images, the proportion of histogram, morphological, and texture features were 85.0% (34/40), 0.0% (0/15), and 3.8% (10/265), respectively, and the histogram features showed the greatest variability (both P<0.001).

In the SNs, among the 143 RFs that showed significant differences between the CT images reconstructed with FBP and those reconstructed with 50% ASiR-V, the proportion of histogram, morphological, and texture features were 20.0% (8/40), 6.7% (1/15), and 50.6% (134/265), respectively, and the texture features showed the greatest variability (P=0.006, <0.001). Among the 135 RFs that showed significant differences between the low-dose CT images and standard-dose CT images, the proportion of histogram, morphological, and texture features were 15.0% (6/40), 6.7% (1/15), and 48.3% (128/265), respectively, and the texture features showed the greatest variability (P=0.006, <0.001). Among the 132 RFs that showed significant differences between CE CT images and NE CT images, the proportion of histogram, morphological and texture features were 87.5% (35/40), 13.3% (2/15), and 35.8% (95/265), respectively, and the histogram features showed the greatest variability (both P<0.001).

Discussion

In this prospective study, 38.4% (123/320), 63.1% (202/320), and 54.1% (173/320) of the RFs showed significant difference between the FBP and 50% ASiR-V, low-dose and standard-dose, and NE CT and CE CT images, respectively. The radiation dose and contrast agent affected more RFs than the reconstruction algorithm (both P<0.001). In the subgroup analysis of nodule consistency, regardless of whichever the reconstruction algorithms, radiation doses, or contrast agents was changed, the proportions of RFs differed significantly among the pGGNs, PSNs, and SNs (all P<0.001).

The findings of our study are aligned with those of previous studies, which reported that the reconstruction algorithm and radiation dose had a significant effect on the RFs (20,23,24). Further, our study showed that the contrast agent also had a significant effect on the RFs of the lung nodules. All the above results provide further evidence that alterations to CT parameters have significant effects on the RFs. Additionally, our findings showed that the radiation dose and contrast agent had a more significant effect on the RFs of the pulmonary nodules than the reconstruction algorithm. Previous studies have also reported differences in the extent to which different parameters affect the RFs of pulmonary nodules. In a study of 89 solid pulmonary nodules >4 mm, Emaminejad et al. found that slice thickness had the greatest effect on the RFs, followed by the radiation dose, and reconstruction kernel (24). All these results suggest that more attention needs to be paid to the consistency of scanning parameters in subsequent radiomic studies.

This study also showed that different types of features were affected differently by parameter changes. Compared with the histogram and texture features, the percentage of changes in the morphological features was relatively stable, which is consistent with the findings of previous studies (20,25). A previous study showed that morphological features can provide important clinically relevant information for predicting survival in lung cancer, and head and neck tumors (26). Histogram and texture features play essential roles in diagnosing benign and malignant pulmonary nodules, and predicting tumor sensitivity to immunotherapy (7,9). These results all show that models constructed based on histogram, morphological, and textural features are of great clinical importance. Therefore, stable morphological, histogram, and texture features are crucial in the clinical application of predictive models.

The present study showed that the percentages of RFs differed significantly among the pGGNs, PSNs, and SNs regardless of whichever the reconstruction algorithms, radiation doses, or contrast agents was changed (all P<0.001). For example, the RFs of pGGNs, PSNs, and SNs affected by contrast agents were 50.6% (162/320), 13.8% (44/320), and 41.3% (132/320), respectively. The underlying cause of this outcome may still be predominantly attributed to the notable distinction in the solid components within the pGGNs, PSNs, and SNs. Previous results from both phantom and human studies are consistent with our results (23,27). By analyzing different densities of phantoms, Kim et al. found that slice thickness had the greatest effect on the RFs of 100 Hu nodule phantoms, while radiation dose had the greatest effect on the RFs of −630 Hu nodule phantoms (23). Similarly, Gao et al. found a significant difference in the reproducibility of RFs between the SNs and GGNs due to varying doses (27). They suggested that the main reason for the difference was that the radiation dose affected the spatial and density resolution of the images of the GGNs. The results indicated that the effect of varying scanning parameters on RFs of lung nodules was uncontrollable, and that there was a significant difference in the RFs stability on lung nodules of different consistency. For radiomic models constructed by mixing different scanning parameters, it was difficult to identify whether the differences between the benign and malignant nodules were due to tumor heterogeneity, or the effect of different parameters on lung nodules of different consistency. Therefore, future radiomic studies should fully consider the effects of scanning parameters on lung nodules of different consistency, and ensure the stability of the scan parameters to establish highly accurate radiomic models. A previous study showed that applying deep-learning features provided an effective solution, and more diverse and convenient solutions should be explored in the future (28).

Our results showed that the reconstruction algorithm and radiation dose primarily affected the texture features in the pGGNs, PSNs, and SNs, while the contrast agent predominantly affected the histogram features. This discrepancy may be due to the effect of the contrast agent on the RFs within the nodal image. He et al. showed that NE scan images contained more diagnostic information than CE images (29). This may be because the contrast agent covers heterogeneity information within the tumor. Given the effect of the contrast agent on feature stability and tumor heterogeneity, further studies need to be conducted to examine how the adverse effects of contrast agents can be avoided in radiomic research.

It should be noted that this study had a number of limitations. First, this study was conducted at a single center with a relatively small sample size, which might have introduced some degree of selection bias. Second, manual segmentation was employed in this study, and the observer variability is likely to exceed that of semi- or fully automatic segmentation. Finally, due to the absence of definitive pathological evidence, we were unable to ascertain which features provide greater diagnostic value in the final analysis. A subsequent prospective study will be conducted to expand the sample size and incorporate pathological results.

Conclusions

Scanning parameters affect the reproducibility of RFs, and nodules of different consistency are affected differently. The effects of these parameters should be fully considered in radiomic analysis.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2026/rc

Funding: This work was supported by the CAMS Innovation Fund for Medical Sciences (Nos. 2022-I2M-C&T-B-076 and 2021-I2M-C&T-B-061), the Beijing Hope Run Special Fund of Cancer Foundation of China (No. LC2022A22), and the National Natural Science Foundation of China (No. 81771830).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2026/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. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Medical Ethics Committee of the National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (No. NCC19-018/1840). 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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