Prospective study on the reproducibility of radiomic features in the setting of variable CT contrast timing: initial results
Bibliographic record
Abstract
Colorectal cancer is the third most commonly diagnosed cancer in the United States with roughly half of these patients developing liver metastases, of which less than 10% survive past 3 years. Although resection is possible in some cases, only 15–25% of these patients are considered cured at the 10 year mark. Research supported by single-institution data shows that quantitative information taken from contrast-enhanced computed tomography (CECT) scans has the potential to pre-operatively predict patients at high risk for recurrence. CT image acquisition, reconstruction, and contrast timing affect the generalizability of predictive radiomic models in CT. Identification of reproducible features, therefore, is a necessary prerequisite to clinical implementation. We prospectively varied CT acquisition, reconstruction, and contrast timing parameters to quantify variability of radiomic features. Reproducibility analysis was performed using Lin’s concordance correlation coefficient (CCC) with 135 patients from Memorial Sloan Kettering Cancer Center (n = 68) and University of Texas MD Anderson Cancer Center (n = 67). Each patient underwent an additional phase CECT scan within ± 15 seconds of the routine portal venous phase using a controlled protocol, with systematic variations in scan timing, image acquisition, and image reconstruction. Radiomic features were extracted from the liver parenchyma and largest metastasis separately. Features extracted from the liver parenchyma were found to be less reproducible than those extracted from the largest tumor,. Reproducibility of features extracted from the tumor showed a negative correlation with the magnitude of scan delay. When reconstructed with a 5 mm slice thickness, scans with higher levels of adaptive statistical iterative reconstruction showed less reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".