West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. Asian Pacific Journal of Cancer Prevention 1513-7368 24 6 2023 06 01 Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study 2061 2072 90687 10.31557/APJCP.2023.24.6.2061 EN Dharmendran Palani Research and Development Centre, Bharathiar University, Coimbatore, India. 0000-0003-3326-3766 Kadirampatti M. Ganesh Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, India. Lavanya Karunagaran Department of Oral and Maxillofacial Pathology,Asan Memorial Dental College and Hospital, Chennai, India. 0000-0001-5458-3617 Kesavan Govindaraj Department of Radiotherapy, Vadamalayan Hospitals Integrated Cancer Centre, Madurai, India. Senthilkumar Shanmugam Department of Radiotherapy Government Rajaji Hospital & Madurai Medical College, Madurai, India. Journal Article 2023 02 22 Aim: To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures. Materials and Methods: Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness. Results: The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image’s radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion  for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features. Conclusion: We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition. https://journal.waocp.org/article_90687_988223196569b69acdd8d9a0e7c5cc51.pdf