Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib
Bibliographic record
Abstract
Objectives To evaluate the feasibility of 4-dimensional perfusion computed tomography (CT) as an imaging biomarker for patients with hepatocellular carcinoma and metastatic liver disease. Methods and materials Patients underwent volumetric dynamic contrast-enhanced CT on a 320-slice scanner before and during stereotactic body radiation therapy and sorafenib, and at 1 and 3 months after treatment. Quiet free breathing was used in the CT acquisition and multiple techniques (rigid or deformable registration as well as outlier removal) were applied to account for residual liver motion. Kinetic modeling was performed on a voxel-by-voxel basis in the gross tumor volume and normal liver resulting in 3-dimensional parameter maps of blood perfusion, capillary permeability, blood volume, and mean transit time. Perfusion characteristics in the tumor and adjacent liver were correlated with radiation dose distributions to evaluate dose-response. Paired t tests assessed change in spatial and histogram parameters from baseline to different time points during and after treatment. Technique reproducibility as well as the impact of arterial and portal vein input functions was also investigated using intra- and inter-subject variance and Bland-Altman analysis. Results Quantitative perfusion parameters were reproducible (±5.7%; range, 2%-10%) depending on tumor/normal liver type and kinetic parameter. Statistically significant reductions in tumor perfusion were measurable over the course of treatment and as early as 1 week after sorafenib administration ( P < .05). Marked liver parenchyma perfusion reduction was seen with a strong dose-response effect (R 2 = 0.95) that increased significantly over the course treatment. Conclusions The proposed methodology demonstrated feasibility of evaluating spatiotemporal changes in liver tumor perfusion and normal liver function following antiangiogenic therapy and radiation treatment warranting further evaluation of biomarker prognostication.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".