Impact of slice thickness on reproducibility of CT radiomic features of lung tumors

Background Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology. There is scarcity of research that how ST affects variability of RF in LC. The present study helps in identifying the specific RF categories affected by variations in ST and provides valuable insights for researchers and clinicians working with RF in the field of LC.Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors. Methods This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm STwas reconstructed retrospectively. RF were extracted from the CECT thorax images of both ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin’s Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses. Results Out of 107 RF, 66 (61.6%) exhibited a statistically significant distinction (p<0.05) when comparing two ST and while 41 (38.3%) RF did not show significant distinction (p>0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility. Conclusions The ST had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.


Introduction
Radiomics is a new field that seeks to improve the physician's visual perception of medical images with addition of more quantitative objectivity.The quantitative attributes from radiographic images are utilized to characterize spatial and textural patterns of lesions which can provide information about the heterogeneity associated with biological processes.[3][4][5] Lung cancer/carcinoma (LC) remains one among the most prevalent and familiar types of cancer that results in mortality notwithstanding recent improvements in healthcare.As, most detected LC are in the middle to late phase of the disease progression and have few management options left, hence, people with lung cancer have a 10-20% survival rate at 5 years following the diagnosis in most of the developed nations. 6,710][11] Radiomics, a rapidly evolving field, employs quantitative attributes from medical images to enhance physician's interpretation, particularly in oncology.Radiomics and machine learning models developed based on radiomic features play crucial roles in classifying histological subtypes lung cancer.Evaluating the variability of radiomic features (RF) is important as diagnosis and treatment decision made using these quantitative should be precise and reproducible.Recent studies have shown that the CT technical parameters such as exposure factors, slice thickness (ST) and image reconstruction algorithms (IRA) can significantly affect the values of RF.3][14] The reproducibility of texture analysis of lung tumors is unclear and there is scarcity of research that has delved into how ST affects variability of RF in lung tumors.The research gap of the study centers on the application of Radiomics in LC, emphasizing the need for a deeper understanding of the reproducibility of RF in the context of lung tumors.Hence, aim of the study is to evaluate the influence of ST on reproducibility of CT-RF for lung tumors.

Methods
This is a prospective study.The study was commenced upon approval from the Institutional Ethical committee of Kasturba Medical College and Hospital, Manipal, India on 12 th August 2022 (IEC:193/2022) followed by the enrolment of the first subject after registration in the Clinical Trial Registry -India (CTRI) registration (CTRI/2022/09/045554) on 15 th September 2022, and continued till 30 th April 2023.

Eligibility criteria
Patients with histopathological diagnosis of lung cancer types such as Non-Small Cell Lung Carcinoma (NSCLC) and Small Cell Lung Carcinoma (SCLC) were included.We excluded patients with ground glass nodules (GGN), lesions measuring less than 4mm, scans with motion artifacts and patients that did not consent to take part in the study.Ground glass nodules (GGN) often represent a distinct category of pulmonary nodules that may differ from solid lesions.Hence they were excluded to focus on a more homogenous sample.Limiting the inclusion criteria to lesions measuring at least 4mm ensures a more consistent and reliable measurement.Smaller lesions might present challenges in terms of accurate radiomic feature extraction and may be subject to greater variability due to partial volume effects, potentially influencing the study's reproducibility findings.Written informed consent to participate was obtained from each patient.

CT scanning procedure
The study was conducted at the Department of Radiodiagnosis, Kasturba Medical College and Hospital, Manipal, India.Both Kasturba Medical College and Hospital (KMC) and Manipal College of Health Professions (MCHP) are constituent

REVISED Amendments from Version 1
There are no changes in title, authors list, figures, and data.In abstract, the reviewer asked to explain the study's original contribution, the same is added and concise to <300 words.In Introduction, the reviewer suggested to explain the research gap and the same is added.In methods, for eligibility criteria the reviewer suggested to include rationale for excluding patients for ground glass nodule and lesions less than 4 mm and the same is added in methods section.In Table 2, the reviewer suggested to include more details about image acquisition parameters, hence the same is added.In results, the reviewer suggested to include the abbreviation for radiomic features (RF).In discussion, the reviewer suggested to address the limitations of the study, as well as potential implications for clinical practice.Hence, the same is added in discussion.In the conclusion, the reviewer suggested to add the future directions and original contribution of the study to the field, hence the same is added in the conclusion.
Any further responses from the reviewers can be found at the end of the article colleges of Manipal Academy of Higher Education (MAHE).A total of thirty-two (32) patients with confirmed histopathological diagnosis of lung cancer (NSCLC-71.8%& SCLC-28.1%) between September 2022 to April 2023 were included and all patients consented.The study population's demographic characteristics are outlined in Table 1.
All patients underwent Contrast Enhanced CT (CECT) Thorax examination using 128 Slice Incisive CT (Philips Medical Systems).The protocol used for the CECT Thorax examination of the study population is detailed in Table 2. Retrospective reconstruction of the CT images was carried out utilizing CECT images from a standard protocol of 5mm to produce a ST of 2 mm.Contrast scans were performed using Iohexol 300 mgI/ml (General Electric Health care, Wisconsin, USA) as the contrast agent.The contrast media was administered using Dual Head CT Pressure injector, OptiVantage (Guerbet, France, UK).

Segmentation
The Digital Imaging and Communication in Medicine (DICOM) CECT sections of two different slice thickness (2 mm and 5 mm) of the same patient were loaded into 3D slicer (version 4.10.2) and a radiologist (Bharath J L) with over 10 years of experience manually delineated the tumours (see Figure 1 for an example).The segmentation was performed using lung window (Window Width (WW): 1500 HU and Window Level (WL): -600 HU).All pulmonary nodules/ lesions present in the right, left, and bilateral lungs were segmented rather than solely selecting the largest or most prominent nodule/lesion.The segmentation of the nodule was performed while excluding the airways, blood vessels, or bronchi.We extracted RF from the segmented regions of the lung nodules using both 2-mm and 5-mm ST.A total of 3424 Radiomic Features (RF) measurements (107 RF per study) were extracted.Among them 66 (61.6%)RF exhibited significant difference between two the slice thickness measurements, while 41 (38.3%)RF did not show significant difference between the two slice thickness measurements (Figure 2; Table 3).

Reproducibility of RF
It was found that out of 14 shape-based features 8 (57.1%), out of 14 Gray Level Dependence Matrix (GLDM) RF 5 (35.71%), out of 24 for Gray Level Co-occurrence Matrix (GLCM) RF 3 (12.5%),out of 18 first order RF 8 (44.4%), out of 16 Gray level run length matrix (GLRLM) RF 4 (25%), out of 16 Gray level size zone matrix (GLSZM) RF 1 (6.25%) were found to be reproducible.All 5 neighboring gray tone difference matrix (NGTDM) RF were found to be not reproducible.Among the seven features categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM based features showed negligible reproducibility (Table 4).The mean CCC of RF categories were shown in Figure 3.

GLDM category
In

GLCM category
In GLCM category, features such as Idm (0.930), Id (0.922) and Sum squares (0.908) showed moderate reproducibility and rest of the twenty-one features showed poor reproducibility between 2-and 5-mm slice thickness.

GLRLM category
In GLRLM category, features such as Gray level non uniformity normalized (0.952) showed good reproducibility, Short run emphasis (0.949), Run percentage (0.936) and Run length non uniformity normalized (0.943) showed moderate reproducibility and rest of the twelve features showed poor reproducibility between 2-and 5-mm slice thickness.

GLSZM category
In GLSZM category, feature such as Zone percentage (0.906) showed moderate reproducibility and rest of the fifteen features showed poor reproducibility between two slice thicknesses.

NGTDM category
In the NGTDM category, all the five features showed poor reproducibility between 2-and 5-mm slice thickness.

Discussion
In the present study, we assessed the impact of slice thickness on the reproducibility of CT radiomic features (RF) for lung tumors.6][17][18] Variability of acquisition parameters could affect the diagnostic performance of radiomic signatures in oncologic patients. 18,19Limited studies had investigated the impact of ST on reproducibility of CT-RF in lung tumors.
In this study, the category of shape-based RF (57.1%) exhibited the highest reproducibility compared to other RF categories.These shape based features demonstrated robustness due to presence of low-frequency components and the reliance on segmented boundaries resulting in consistent reproducibility across changes in ST.Findings by Erdal et al. 20 & Lu et al. 21supported this, revealing that RF describing tumor dimension, shape of boundaries, low-order density frequencies, and rough features were less sensitive to image setting parameters, in contrast to features characterizing sharpness of boundaries, high-order density frequencies and smooth features.Both studies analyzed the combination of ST with IRA (lung and standard) and noted that shape-based features were less effected by change in slice thickness and reconstruction algorithm.They also observed that the thinner slices with sharper reconstructions had fewer reproducible features compared to thicker slices with smoother reconstructions.
The GLDM category features in our study, such as HGLE, DE, SDE, DNU, LDHGLE demonstrated moderate reproducibility.A study by Emaminejad et al. 22 in non-contrast chest CT (NCCT) identified that GLDM DE, DNU, GLNU were reproducible against the dose and kernel variations with varying slice thickness.Unlike our study, none of the previous research mentioned the reproducibility of GLDM features concerning slice thickness alone.
Within the GLCM category in our study, only two features showed reproducibility with variations in slice thickness.Similar results were documented by Erdal et al. 20 & Kim et al. 23 indicating that GLCM category (19.4 % & 25 %) had lower reproducibility compared to other RF categories.We observed that first-order features (44.4%) had the second highest reproducibility.Studies by Erdal et al., 20 Park s et al., 24 Choe J et al. 25 reported that first-order features exhibited the most reproducibility across various imaging parameters.Park s et al. 24 and Choe J et al. 25 reported that convolution network-based super resolution (SR) algorithms and kernel-converted images had reduced effects on the reproducibility of RF with variations in slice thickness and reconstruction kernels.Yang et al. 26 employed a resampling technique to standardize the voxel measurement of both thick and thin section CT images to 1x1x1 mm 3 using linear interpolation and observed that, following resampling of thicker images, 202 RF (66.2%, 202/305) exhibited a noteworthy reduction in variability of RF compared to the original non-resampled data (Table 5).
For the GLRLM and GLSZM categories, reproducibility rates were 25% and 6.25 %, respectively, in the current study.A Study by Emaminejad et al. 22 similarly found that GLRLM Run length non uniformity (1 of 9 features) and GLSZM (1 of 10 features) displayed very limited reproducibility against the dose and kernel variations with varying slice thickness.
Contrary to the study reported by Liu J et al. 27 which demonstrated that NGTDM exhibited good reproducibility, we did not observe any reproducible features in NGTDM.The reason for this disparity is attributed to differences in technical parameters, specifically in terms of dose variation, rather than slice thickness.The study has few limitations.Firstly, the sample size was relatively small, as it is time bound study with prospective data collection of patients who underwent CT scan with histopathological proven cases of lung cancer.A larger sample size is required to confirm the reproducibility of RF with slice thickness.This may limit the generalizability of the findings to a broader population.Secondly, we did not analyze whether a thinner slice thickness would result in better performance of radiomic models for predicting lung cancer.Thirdly, a single image acquisition variable such as slice thickness was examined to determine how it affects the reproducibility of radiomic features.Additionally, the study focused on a specific CT scanner model (128-Incisive CT by Philips Health care), and the results might be informed by scannerspecific characteristics.Generalizing the findings to other CT scanners would require further investigation.
In terms of potential implications for clinical practice, the study underscores the importance of considering the slice thickness when utilizing radiomics in the assessment of lung tumors.Clinicians should be aware that variations in slice thickness can introduce significant variability in RF.This finding emphasizes the need for standardizing imaging protocols, particularly in the context of lung cancer diagnosis and treatment planning.The results also highlight the critical role of shape-based features in radiomics, as they demonstrated the highest reproducibility in this study.Clinicians incorporating radiomic analysis into their practice should be attentive to the choice of features, giving preference to those with higher reproducibility for more reliable and consistent results.

Conclusion
Radiomics has the potential to transform lung cancer diagnosis, follow-up, and therapy planning by enabling individualised management in a non-invasive and an economical manner.Our study found that ST is the main parameter impacting the reproducibility of CT-RF for lung tumours.The original contribution of this study lies in its systematic examination of the influence of ST on the reproducibility of CT-RF in lung tumors.By identifying specific categories of RF that are more or less affected by variations in ST, the study provides valuable insights for researchers and clinicians working with radiomics in the field of lung cancer.This information could contribute to the refinement of imaging protocols, the standardization of radiomic analyses, and the interpretation of radiomic data in oncology.The study also increases awareness regarding the significance of accurately configuring imaging acquisition parameters in the context of radiomic/radio genomic approaches.Standardization of technical parameters and protocols is necessary when conducting multicentre studies, as these factors can impact the diagnostic performance of Machine Learning (ML) models developed using radiomic features.

Tarun Gangil
The institute of cancer research, London, UK

Major comments
Authors should indicate the clinical validity of radiomics results.It is advised to authors to first specify the clinical relevance of variability of slice thickness, in the volumetric interpretation of the lung tumors.How the volumetric segmentation of 2mm slice thickness vary from the one with 5mm(without radiomics).Cite clinical research papers for the same.Further it can be added that radiomics features also supports the known clinical facts.

Minor Comments
Revision on Grammatical errors.

Suggestions to editor
It is an excellent research, where all the statistical process is well explained and can be reproduced, after addressing the major comment.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

Mubarak Taiwo Mustapha
Near East University, TRNC Mersin, Nicosia, Turkey I can confirm that all my comments and suggestions have been addressed.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of
expertise to confirm that it is of an acceptable scientific standard.

Version 1
Reviewer Report 26 October 2023 https://doi.org/10.5256/f1000research.154563.r214642 © 2023 Mustapha M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Major comments
The abstract could benefit from a more explicit mention of the study's original contribution to the field. 1.
Directly state the main research question that the study addresses.2.
Include more details about the image acquisition parameters (e.g., exposure factors, acquisition protocols) to ensure reproducibility of the study.

3.
Explicitly address the limitations of the study, as well as potential implications for clinical practice.

Minor comments
In the methods section, provide a brief rationale for excluding patients with ground glass nodules (GGN) and lesions measuring less than 4mm. 1.
In the Methods section, the date of approval from the Institutional Ethical Committee (12th August 2022) and the CTRI registration date (15th September 2022) are provided.It might be helpful to briefly explain the time gap between these dates, if relevant.

2.
In the Results section, please clarify what RF stands for upon first mention.While it's explained later in the text, an initial definition or expansion would be helpful for reader clarity.

3.
In the Discussion section, while comparing the study's findings with previous research, consider mentioning any discrepancies or agreement with other studies in terms of reproducibility outcomes.

4.
The Conclusion section succinctly summarizes the main findings and emphasizes the importance of standardizing technical parameters.It could further benefit from a statement about the study's potential impact on clinical practice or future research directions.

5.
In the Conclusion, it might be worth reiterating the study's original contribution to the field, especially in terms of its focus on slice thickness and reproducibility of radiomic features.
6. Response: The study has few limitations.Firstly, the sample size was relatively small, as it is time bound study with prospective data collection of patients who underwent CT scan with histopathological proven cases of lung cancer.A larger sample size is required to confirm the reproducibility of RF with slice thickness.This may limit the generalizability of the findings to a broader population.Secondly, we did not analyze whether a thinner slice thickness would result in better performance of radiomic models for predicting lung cancer.Thirdly, a single image acquisition variable such as slice thickness was examined to determine how it affects the reproducibility of radiomic features.Additionally, the study focused on a specific CT scanner model (128-Incisive CT by Philips Health care), and the results might be informed by scanner-specific characteristics.Generalizing the findings to other CT scanners would require further investigation.

Is the work clearly and accurately presented and does
In terms of potential implications for clinical practice, the study underscores the importance of considering the slice thickness when utilizing radiomics in the assessment of lung tumors.Clinicians should be aware that variations in slice thickness can introduce significant variability in RF.This finding emphasizes the need for standardizing imaging protocols, particularly in the context of lung cancer diagnosis and treatment planning.The results also highlight the critical role of shape-based features in radiomics, as they demonstrated the highest reproducibility in this study.Clinicians incorporating radiomic analysis into their practice should be attentive to the choice of features, giving preference to those with higher reproducibility for more reliable and consistent results.
Minor comments 5.In the methods section, provide a brief rationale for excluding patients with ground glass nodules (GGN) and lesions measuring less than 4mm.Response: Ground glass nodules (GGN) often represent a distinct category of pulmonary nodules that may differ from solid lesions.Hence they were excluded to focus on a more homogenous sample.Limiting the inclusion criteria to lesions measuring at least 4mm ensures a more consistent and reliable measurement.Smaller lesions might present challenges in terms of accurate radiomic feature extraction and may be subject to greater variability due to partial volume effects, potentially influencing the study's reproducibility findings.
6.In the Methods section, the date of approval from the Institutional Ethical Committee (12th August 2022) and the CTRI registration date (15th September 2022) are provided.It might be helpful to briefly explain the time gap between these dates, if relevant.

Response:
The CTRI registration will be done after receiving the Institutional ethical committee approval letter.Hence it took a month time for completing the CTRI registration.Response: The study findings with previous research were already discussed in discussion section and Table 5. Discrepancies and agreement with other studies in terms of reproducibility outcomes were also discussed and provided in Table  The original contribution of this study lies in its systematic examination of the influence of slice thickness on the reproducibility of CT-RF in lung tumors.By identifying specific categories of radiomic features that are more or less affected by variations in slice thickness, the study provides valuable insights for researchers and clinicians working with radiomics in the field of lung cancer.(Included the same in conclusion) The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.com

Figure 1 .
Figure 1.An axial CT image of a 52-year-old male with adenocarcinoma showing manual segmentation of tumour using 3D Slicer at (a) 2mm and (b) 5mm slice thickness.

Figure 2 .
Figure 2. Number of significant (p<0.05) and non-significant (p>0.05)features in each RF category between 2-mm and 5-mm slice thickness.
Expertise: Deep learning, Image Processing, Machine Learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.Reviewer Report 11 December 2023 https://doi.org/10.5256/f1000research.159376.r227714© 2023 Mustapha M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

7 . 8 .
In the Results section, please clarify what RF stands for upon first mention.While it's explained later in the text, an initial definition or expansion would be helpful for reader clarity.Response: As per the suggestions, the Radiomic features (RF) abbreviation is provided in the results section.In the Discussion section, while comparing the study's findings with previous research, consider mentioning any discrepancies or agreement with other studies in terms of reproducibility outcomes.

9 .
The Conclusion section succinctly summarizes the main findings and emphasizes the importance of standardizing technical parameters.It could further benefit from a statement about the study's potential impact on clinical practice or future research directions.Response: Our study information could contribute to the refinement of imaging protocols, the standardization of radiomic analyses, and the interpretation of radiomic data in oncology.(included the same in conclusion) 10.In the Conclusion, it might be worth reiterating the study's original contribution to the field, especially in terms of its focus on slice thickness and reproducibility of radiomic features Response:

Table 1 .
Demographic characteristics of study population.

Table 2 .
Technical parameters of CECT Thorax Protocol.

Table 4 .
Percentage of reproducibility of RF between 2-mm and 5-mm ST for each category.

Table 5 .
Comparison of reproducibility of radiomic features with CT technical parameters and Slice thickness combinations in lung cancer between current study and other recent studies.

it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests:
No competing interests were disclosed.

have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. 3. Include more details about the image acquisition parameters (e.g., exposure factors, acquisition protocols) to ensure reproducibility of the study Response:
As per the suggestion, additional image acquisition parameters ( esposure factors such as kVp and mAs were already mentioned in

Explicitly address the limitations of the study, as well as potential implications for clinical practice
As per the suggestion the limitations of the study, as well as potential implications for clinical practice were addressed in the discussion as below.