Feasibility of simulated realistic textured XCAT phantoms for assessment of radiomic feature stability
Bibliographic record
Abstract
Radiomics studies, using features extracted from medical images, are often used for outcome prediction in oncology. Studies frequently use physical phantoms to assess radiomic feature reliability, however, few studies have utilized computer-generated phantoms to assess the impact of image acquisition parameters. Additionally, studies have introduced deep learning approaches to generate CT-realistic textures on computer-generated phantoms. Therefore, we aimed to assess the feasibility of using 4D extended cardiac-torso (XCAT) phantoms with generated realistic textures using a deep learning network adapted from a previous study to analyze the impact of slice thickness on radiomic features. Our dataset consisted of 70 organ maps (training: n=50, validation: n=20) generated from CT images of lung cancer patients. These were used as input for a dual-discriminator conditional-generative adversarial network to synthesize realistic textures in the organ maps. The validated network was used to generate realistic-textured XCAT phantoms. The phantoms were reconstructed using three different slice thicknesses. Pyradiomics was used to extract radiomics features from the tumor of each XCAT phantom. The intraclass correlation coefficient was used to assess the feature reliability for each acquisition protocol. Qualitatively, the generated XCAT phantoms had similar textures to that of the real CT images. The features demonstrated excellent reliability between each acquisition protocol for most feature types with GLCM texture features only showing moderate reliability, however, this may be due to the small sample size of the study. This study showed the feasibility of using generated realistic-textured XCAT phantoms to study the impact of acquisition protocols on radiomic features.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".