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

Sanat Gupta; Kaushik Nayak; Saikiran Pendem

doi:10.12688/f1000research.141148.1

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Research Article

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

[version 1; peer review: 1 approved with reservations]

Sanat Gupta¹, Kaushik Nayak¹, Saikiran Pendem ¹

PUBLISHED 12 Oct 2023

Author details Author details

¹ Manipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

Sanat Gupta
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Kaushik Nayak
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Saikiran Pendem
Roles: Conceptualization, Formal Analysis, Methodology, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Manipal Academy of Higher Education gateway.

Abstract

Background: Radiomics, a field of research, relies on the theory that quantified characteristics from radiographic images would reflect underlying pathophysiology. Lung cancer continues to stand as one of the prevalent and well-known forms of cancer, causing mortality. The slice thickness (ST) of computed tomography (CT) images would be key concern regarding generalizability of radiomic features (RF) results in oncology. There is scarcity of research that has delved into how ST affects variability of RF in lung tumors. 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 ST, and was reconstructed retrospectively. RF were extracted from the CECT thorax images of 5-mm and 2-mm 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 extracted, 66 (61.6%) exhibited a statistically significant distinction (p<0.05) when comparing two slice thicknesses 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 slice thickness 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.

Keywords

Lung Cancer, Radiomics, Computed Tomography, Slice Thickness, CT Parameters

Corresponding author: Saikiran Pendem

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Gupta S et al. This is an open access article 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.

How to cite: Gupta S, Nayak K and Pendem S. Impact of slice thickness on reproducibility of CT radiomic features of lung tumors [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:1319 (https://doi.org/10.12688/f1000research.141148.1) First published: 12 Oct 2023, 12:1319 (https://doi.org/10.12688/f1000research.141148.1) Latest published: 01 Dec 2023, 12:1319 (https://doi.org/10.12688/f1000research.141148.2)

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. Radiomics is a rapidly evolving field particularly in oncology to improve patient care, aid in treatment decision making, characterization, response to therapy and prognosis.¹^–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^–⁷ Radiomics and Machine learning methods have been used for classification of histological subtypes of LC, prediction of LC staging and outcome, response to treatment, prognosis of lung cancer.⁸^–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. Experts have recommended that for training predictive models using radiomics based machine learning models, only reproducible RF should be considered.¹²^–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. 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. 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 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).

Table 1. Demographic characteristics of study population.

Characteristics	Data
Age (Mean ± SD)	53.16 ± 10.25
Gender (%)
Male (M)	18 (56.25 %)
Female (F)	14 (43.75%)
Tumor size, mm (Mean ± SD)	17.20 ± 15.77
Pathology (%)	Ca Lung/Pulmonary tumors • NSCLC-71.8% • SCLC-28.1%
Location (Lung)
Right	10 (31.2%)
Left	7 (21.8 %)
Bilateral	15 (46.8%)

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).

Table 2. Technical parameters of CECT Thorax Protocol.

Protocol	Chest helical
Patient position	Supine - feet first
Scanogram	PA – 180 degree
Area coverage	Apex of lungs to the domes of diaphragm
Scan orientation	Craniocaudal
Acquiring Slice thickness	5mm
Slice increment	5mm
Kilovoltage (kVp)	120
Milliampere second (mAs)	360
Collimation	64 x 0.625
Rotation time	0.75 seconds
Field of view (FOV)	350 mm
Matrix size	512 x 512
Pitch	1.08
Contrast	Omnipaque – Iohexol (300 mgI/ml) Volume (60 ml)

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.

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.

Statistical analysis

Statistical analysis was done using SPSS version 20.0. A Paired t-test was performed to identify the significant difference in RF between the two slice thickness (2 mm and 5 mm) groups. Lin’s Concordance Correlation Coefficient (CCC) was calculated to assess the reproducibility of RF between two groups (2 and 5 mm). Concordance Correlation Coefficient of > 0.99 suggests excellent reproducibility, > 0.95 to 0.99 suggests good reproducibility, >0.90 to 0.95 suggests moderate reproducibility, ≤ 0.90 suggests weak reproducibility. p-value (<0.05) was considered.

Results

A total of 32 cases (18 males and 14 females) with LC [Non-Small Cell Lung Cancer (NSCLC) – 71.8%, Small Cell Lung Cancer (SCLC) – 28.1%)] with mean age of were included 53.16 ± 10.25.

A total of 3424 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).

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.

Table 3. Percentage of significant and non-significant RF between 2-mm and 5-mm ST for each category.

RF Category	Features with p<0.05 [n(%)]	Features with p>0.05 [n(%)]
Shape (n=14)	4 (28.75%)	10 (71.42%)
GLDM (n=14)	9 (64.28%)	5 (35.71%)
GLCM (n=24)	20 (83.33%)	4 (16.66%)
First order (n=18)	12 (66.66%)	6 (33.33%)
GLRLM (n=16)	9 (56.25%)	7 (43.75%)
GLSZM (n=16)	9 (56.25%)	7 (43.75%)
NGTDM (n=5)	3 (60.00%)	2 (40.00%)

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).

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

RF Category	Excellent n(%)	Good n(%)	Moderate n(%)	Weak n(%)
Shape (n=14)	2 (14.2%)	3 (21.4%)	3 (21.4%)	6 (42.8%)
GLDM (n=14)	-	-	5 (35.7%)	9 (64.2 %)
GLCM (n=24)	-	-	3 (12.5%)	21 (87.5%)
First order (n=18)	-	1 (5.5%)	7 (38.8%)	10 (55.5%)
GLRLM (n=16)	-	1 (6.25 %)	3 (18.75 %)	12 (75 %)
GLSZM (n=16)	-	-	1 (6.25%)	15 (93.75%)
NGTDM (n=5)	-	-	-	5 (100%)

Figure 3. Mean concordance correlation coefficient (CCC) of each radiomic feature category between 2-mm and 5-mm slice thickness.

Shape-based category

In shape-based category, features such as Voxel volume (0.997) and Mesh volume (0.997) showed excellent reproducibility. Major (0.973) and minor axis length (0.959), maximum 2D-diameter (0.976) had good reproducibility. Maximum 3D-diameter (0.944), maximum 2D-diameter slice (0.926) and maximum 2D-diameter row (0.903) had moderate reproducibility and rest of the six features showed poor reproducibility between 2- and 5-mm slice thickness.

GLDM category

In GLDM category, features such as high gray level emphasis [HGLE] (0.918), dependence entropy [DE] (0.929), small dependence emphasis [SDE] (0.935), dependence non uniformity normalized [DNU] (0.935) and large dependence high gray level emphasis [LDHGLE] (0.903) showed moderate reproducibility and rest of the nine features showed poor reproducibility between 2- and 5-mm slice thickness.

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.

First order category

In first order category, features such as 10^th percentile (0.961) showed good reproducibility, Skewness (0.948), Uniformity (0.947), Median (0.921), Total energy (0.920), Root mean squared (0.945), Entropy (0.943) and Mean (0.943) showed moderate reproducibility and rest of the ten 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. Few previous studies had addressed the influence of exposure parameters such as tube voltage (kV_P), tube current (mA), image reconstruction algorithms (IRA), CT Scanner vendors on RF in CT for conditions like liver fibrosis, metastatic liver lesions, pancreatic neuroendocrine neoplasm.¹⁵^–¹⁸ Variability of acquisition parameters could affect the diagnostic performance of radiomic signatures in oncologic patients.¹⁸^–¹⁹ Limited 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.²¹ supported 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 mm³ 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).

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.

Author name (year)	Our study (2023)	Lu et al.²¹ (2016)	Erdal et al.²⁰ (2020)	Yang et al.²⁶ (2020)	Emaminejad et al.²² (2021)
Pathology studied	Lung tumors (SCLC, NSCLCL)	Lung cancer	Lung nodules	Solid pulmonary nodules	Lung cancer
Study Procedure	CECT	NCCT	NCCT	CECT	NCCT
Technical parameters	ST (2 and 5- mm)	IRA (Lung and standard) ST	Dose levels (4) Kernels (10) Thicknesses (8)	ST (1.25 mm and 5 mm)	Dose levels (100 %, 50%, 25% and 10 %) ST (0.6, 1 and 2 mm) Reconstruction kernel (smooth, medium, sharp)
RF extracted	107	89	28	396	226
Features extracted	Shape, GLDM, GLCM First order, GLRLM GLSZM, NGTDM	Tumor size, Shape, Boundary shape, Sharpness, Density distributions with and without spatial information	Histogram, GLCM, RLM, NGDLM,NGTDM	Histogram, Geometry Texture features	First-order, Wavelet Features, GLDM, GLRLM, GLCM, GLSZM, NGTDM
Reproducibility of RF	Shape based features (57.1%), First order (44.4%) features showed highest reproducibility compared to other RF categories	Eight of the feature groups associated with dimensions, form, and rough texture exhibited consistent reproducibility across all combinations	Density feature was robust against dose changes, Skewness was robust for kernel and ST, Deviation was weakest feature for all cases. GLCM category was least reproducible	In non-resampled data, 239 features were shown significant differences between thin and thick slice. 66 RF were reproducible. In resampled data, 202 features exhibited significant differences between two thicknesses. 103 features were reproducible.	Seventeen and Eighteen features were reproducible with respect to dose and kernel changes. Only one to five features were reproducible with changes in slice thickness

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. 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.

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 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.

Data availability

Underlying data

Figshare: F1000 Data Radiomic Features for 2-mm and 5-mm Slice thickness. https://doi.org/10.6084/m9.figshare.23935491.²⁸

This project contains the following underlying data:

- RF of 2 mm and 5mm ST (Spread Sheet)
- CCC of RF (Spread Sheet)
- Demographic characteristics of each patient F1000 (Spread Sheet)
- CT images of all 32 patients (DICOM images)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

The authors would like to acknowledge Dr. J L Bharath (JLB), Faculty in Department of Radiodiagnosis and Imaging, Kasturba Medical College and Hospital, Manipal for manually delineating the tumours.

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Author details Author details

¹ Manipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

Sanat Gupta
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Kaushik Nayak
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Saikiran Pendem
Roles: Conceptualization, Formal Analysis, Methodology, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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© 2023 Gupta S et al. This is an open access article 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.

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Gupta S, Nayak K and Pendem S. Impact of slice thickness on reproducibility of CT radiomic features of lung tumors [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:1319 (https://doi.org/10.12688/f1000research.141148.1)

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Reviewer Report 26 Oct 2023

Mubarak Taiwo Mustapha, Near East University, TRNC Mersin, Nicosia, Turkey

Approved with Reservations

https://doi.org/10.5256/f1000research.154563.r214642

Major comments

The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Directly state the main research question that the study addresses.

Major comments

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

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?

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.

Reviewer Expertise: Medical imaging; Artificial intelligence;

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, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 01 Dec 2023

Saikiran Pendem, Manipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, 576104, India

01 Dec 2023

Author Response

Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature ... Continue reading Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature (RF) categories affected by variations in Slice thickness (ST) and provides valuable insights for researchers and clinicians working with RF in the field of Lung cancer (LC)

2. Directly state the main research question that the study addresses.
Response: As per the suggestion, the research gap is addressed in the introduction as below
The research gap of the study centers on the application of radiomics in lung cancer, emphasizing the need for a deeper understanding of the reproducibility of radiomic features (RF) in the context of lung tumors.

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 Table 2) were included for ensuring the reproducibility of the study as below
Reconstruction algorithm - iDose⁴, Level 3
Spatial resolution-0.33
Threshold (HU)- 150
Window Width (WW)-400
Window Level (WL)- 40

4 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.
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.

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.

7.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.

8.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.
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 5

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: 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)
Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature (RF) categories affected by variations in Slice thickness (ST) and provides valuable insights for researchers and clinicians working with RF in the field of Lung cancer (LC)

2. Directly state the main research question that the study addresses.
Response: As per the suggestion, the research gap is addressed in the introduction as below
The research gap of the study centers on the application of radiomics in lung cancer, emphasizing the need for a deeper understanding of the reproducibility of radiomic features (RF) in the context of lung tumors.

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 Table 2) were included for ensuring the reproducibility of the study as below
Reconstruction algorithm - iDose⁴, Level 3
Spatial resolution-0.33
Threshold (HU)- 150
Window Width (WW)-400
Window Level (WL)- 40

4 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.
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.

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.

7.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.

8.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.
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 5

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: 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)
Competing Interests: No competing interests were disclosed. Close
Report a concern
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COMMENTS ON THIS REPORT

Author Response 01 Dec 2023

Saikiran Pendem, Manipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, 576104, India

01 Dec 2023

Author Response

Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature ... Continue reading Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature (RF) categories affected by variations in Slice thickness (ST) and provides valuable insights for researchers and clinicians working with RF in the field of Lung cancer (LC)

2. Directly state the main research question that the study addresses.
Response: As per the suggestion, the research gap is addressed in the introduction as below
The research gap of the study centers on the application of radiomics in lung cancer, emphasizing the need for a deeper understanding of the reproducibility of radiomic features (RF) in the context of lung tumors.

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 Table 2) were included for ensuring the reproducibility of the study as below
Reconstruction algorithm - iDose⁴, Level 3
Spatial resolution-0.33
Threshold (HU)- 150
Window Width (WW)-400
Window Level (WL)- 40

4 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.
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.

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.

7.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.

8.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.
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 5

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: 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)
Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature (RF) categories affected by variations in Slice thickness (ST) and provides valuable insights for researchers and clinicians working with RF in the field of Lung cancer (LC)

2. Directly state the main research question that the study addresses.
Response: As per the suggestion, the research gap is addressed in the introduction as below
The research gap of the study centers on the application of radiomics in lung cancer, emphasizing the need for a deeper understanding of the reproducibility of radiomic features (RF) in the context of lung tumors.

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 Table 2) were included for ensuring the reproducibility of the study as below
Reconstruction algorithm - iDose⁴, Level 3
Spatial resolution-0.33
Threshold (HU)- 150
Window Width (WW)-400
Window Level (WL)- 40

4 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.
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.

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.

7.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.

8.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.
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 5

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: 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)
Competing Interests: No competing interests were disclosed. Close
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Version 2

VERSION 2 PUBLISHED 12 Oct 2023

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Version 2 (revision) 01 Dec 23	read	read
Version 1 12 Oct 23	read

Mubarak Taiwo Mustapha, Near East University, TRNC Mersin, Nicosia, Turkey
Tarun Gangil, The institute of cancer research, London, UK

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Reviewer Report

8 Views

06 Mar 2024 | for Version 2

Tarun Gangil, The institute of cancer research, London, UK

8 Views Cite this report Responses(0)

Approved

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.

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
Are sufficient details of methods and analysis provided to allow replication by others?

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.

Reviewer 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.

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Reviewer Report

10 Views

11 Dec 2023 | for Version 2

Mubarak Taiwo Mustapha, Near East University, TRNC Mersin, Nicosia, Turkey

10 Views Cite this report Responses(0)

Approved

I can confirm that all my comments and suggestions have been addressed.

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.

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Reviewer Report

23 Views

26 Oct 2023 | for Version 1

Mubarak Taiwo Mustapha, Near East University, TRNC Mersin, Nicosia, Turkey

23 Views Cite this report Responses(1)

Approved With Reservations

Major comments

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

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?

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.

Reviewer Expertise

Medical imaging; Artificial intelligence;

Respond to this report

Responses (1)

Author Response

01 Dec 2023

Saikiran Pendem, Manipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, 576104, India

Major Comments
1. The abstract could benefit from a more explicit mention of the study's original contribution to the field.
Response: The present study helps in identifying the specific Radiomic Feature (RF) categories affected by variations in Slice thickness (ST) and provides valuable insights for researchers and clinicians working with RF in the field of Lung cancer (LC)

2. Directly state the main research question that the study addresses.
Response: As per the suggestion, the research gap is addressed in the introduction as below
The research gap of the study centers on the application of radiomics in lung cancer, emphasizing the need for a deeper understanding of the reproducibility of radiomic features (RF) in the context of lung tumors.

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 Table 2) were included for ensuring the reproducibility of the study as below
Reconstruction algorithm - iDose⁴, Level 3
Spatial resolution-0.33
Threshold (HU)- 150
Window Width (WW)-400
Window Level (WL)- 40

4 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.
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.

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.

7.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.

8.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.
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 5

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: 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)

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

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

Abstract

Keywords

Introduction

Methods

Eligibility criteria

CT scanning procedure

Table 1. Demographic characteristics of study population.

Table 2. Technical parameters of CECT Thorax Protocol.

Segmentation

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.

Statistical analysis

Results

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.

Table 3. Percentage of significant and non-significant RF between 2-mm and 5-mm ST for each category.

Reproducibility of RF

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

Figure 3. Mean concordance correlation coefficient (CCC) of each radiomic feature category between 2-mm and 5-mm slice thickness.

Shape-based category

GLDM category

GLCM category

First order category

GLRLM category

GLSZM category

NGTDM category

Discussion

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.

Conclusion

Data availability

Underlying data

Acknowledgements

References

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