Effect of Computed Tomography Slice Thickness on Calculated Coronary Artery Calcium Score in the Assessment of Possible Radiation Induced Cardiac Toxicity
Bibliographic record
Abstract
Abstract Studies have suggested that the occurrence of radiation induced cardiac toxicity in breast cancer patients is significantly higher in women with pre-existing cardiac risk factors. Therefore, it is important to quantify the relationship between radiation induced cardiac toxicity and a measurable level of pre-existing cardiac risk such as the patient’s coronary artery calcium Agatston score. To assess the extent of coronary artery calcium present in the walls of patients’ coronary arteries before they received treatment, Agatston scores may be calculated using thoracic CT scans acquired for external beam radiotherapy planning. However, these planning CT scans can vary in slice thickness and resolution, thus complicating the calculation of calcium scores using scans with slice thicknesses other than the 3mm routinely employed for traditional Agatston scoring. The objective of this project is to quantify the effect of varying CT scan slice thickness in the calculation of coronary artery calcium scores so that a method of standardization might be developed. This is accomplished through the design, fabrication, and scanning of an anthropomorphic phantom featuring calcium inserts of varying sizes. Analysis of how the scores change with increasing slice thickness is used to construct a simple linear scaling method of standardization which corrects for varying slice thickness and the resulting partial volume distortions. A linear scaling method was successfully validated for the calculation of coronary artery calcium Agatston scores across a range of slice thicknesses increasing from 0.625 mm to 5 mm. Scaling is applied by multiplying the score by the slice thickness and dividing by 3 mm. This method is potentially applicable in any clinical or research endeavour which calls for retroactive cardiac calcium quantification. With a simple linear scaling method, external beam radiation therapy planning CT scans with slice thicknesses ranging from 0.625 mm to 5 mm can be used to calculate coronary artery calcium Agatston scores and thereby measure a patient’s level of cardiac risk before treatment.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".