## 1 Introduction  
There is growing evidence that standard-of-care medical images obtained from modalities such as CT, MRI, and PET contain more information than is visible to the human eye [1]. The high-throughput extraction and processing of the underlying information from radiological images is known as \"radiomics\". The quantitative data obtained (imaging biomarkers) could potentially be used alongside the current gold standard of tumor evaluation and staging tools, including TNM staging [2], to aid clinical decision making such as personalized treatment planning.  
The predictive power of radiomic features is dependent on having a large set of data. However, due to the nature of medical images, the size of the studies is often relatively small and based on a single dataset, restricting the impact of the results. To find candidates for reproducible biomarkers from the hundreds of features available from first, second, and higher-order statistical features of images, it is necessary for researchers to validate the results published by other groups [3]. This should be carried out using a separate dataset from the original study and considered a retrospective investigation. However, at least 50% of published studies have been described as poorly reported with incomplete methodologies and results for successful validation when an analysis of biomedical research was performed by [PERSON] and [PERSON] [3]. The precise cause of this serious lack of reproducibility in validation is unclear. The lack of standards for validating results, incomplete reporting of methodologies and results, and unrecognized confounding variables in the dataset used could all be to blame.  
A recent systematic review of full-text articles in PubMed published in 2018 primarily addressed non-small cell lung cancer (NSCLC) and oropharyngeal cancer [4] and found only 7 out of 41 studies reported every methodology used in image acquisition, pre-processing, and feature extraction in detail. Out of 21 studies on NSCLC, 4 studies using CT images [5; 6; 7; 8] and 1 study using PET (Positron Emission Tomography) images reported every methodologic aspect. The results identified the sensitivity of radiomic features in terms of repeatability and reproducibility to processing details such as the settings used in image acquisition, the image reconstruction algorithm, image preprocessing, and the software used to extract radiomic features. First-order features were reported to be more reproducible than shape metrics and texture features.  
Our previously published study [9] analyzed radiomic features extracted from the CT component of PET/CT scans of patients with NSCLC, treated at the Royal Surrey NHS Foundation Trust (RSFT). The radiomics features were calculated using the toolkit of [PERSON] et al. [10], Which is available in the MATLAB package. This study found that a set of radiomics features were stable to settings used in image acquisition and reconstruction algorithms used in different scanner models. Features were also stable to variations in tumor delineation. However, features were sensitive to intensity quantization parameters, including (i) the number of intensity levels, (ii) the method of quantization to select the intensity levels, and (iii) the use of an intensity threshold around the tumor or organ being analyzed. These results show that different parameter choices in different datasets may help explain the results in the mentioned review papers [4; 5; 11]. Therefore, a question is: would these features be successfully reproduced and validated under different conditions, such as with a different lung cancer dataset, a different disease site, or using a different implementation of radiomics feature extraction?