Identification of robust and reproducible CT‐texture metrics using a customized 3D‐printed texture phantom

Abstract

Objective The objective of this study was to evaluate the robustness and reproducibility of computed tomography‐based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three‐dimensional (3D) printed progressively increasing textural heterogeneity. Materials and Methods A custom‐built 3D‐printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray‐level Co‐occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two‐way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. Results A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray‐level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. Conclusion The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

A customizable anthropomorphic phantom for dosimetric verification of 3D‐printed lung, tissue, and bone density materials

“…Different tissue type inserts can be made within an anthropomorphic setting to validate dual‐energy CT contrast resolution of the various tissue types, which is important for particle therapy dose calculation modeling as mapping the stopping power ratio for different tissue types is one of the key sources in beam range uncertainty. Other advanced applications also include molecular imaging, 43 ultrasound cardiac applications, 44 adaptive radiotherapy for Magnetic Resonance (MR)‐guided system, 45 and radiomics‐based texture metrics 46 …”

“…Other advanced applications also include molecular imaging, 43 ultrasound cardiac applications, 44 adaptive radiotherapy for Magnetic Resonance (MR)-guided system, 45 and radiomics-based texture metrics. 46…”

Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions

“…Radiomics features comparability is impacted by a broad range of factors at many points of analysis. Image acquisition settings such as the choice of the scanner, tube voltage, reconstruction kernel, choice of contrast agent, and a variety of other aspects can be a reason for diminished reproducibility [16][17][18] . Another component is the segmentation method (manual, semi-automatic, fully automated) which can produce different results 19 .…”

Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis

“…There are multiple methods of incorporating radiomics in multi-center studies that allow for variation of imaging acquisition. These include phantom-based studies [ 32 ] and software-based harmonization techniques [ 33 ]. Integration of these innovative liquid biopsy and radiomic tools to monitor therapy response and emergence of treatment resistance has great potential to dramatically improve outcomes of prostate cancer patients.…”