Radiological, dosimetric and mechanical properties of a deformable breast phantom for radiation therapy and surgical applications
Bibliographic record
Abstract
Abstract The displacement of tumor bed walls during oncoplastic breast surgery (OBS) decreases the accuracy of using surgical clips as the sole surrogate for tumor bed location. This highlights the need for better communication of OBS techniques to radiation oncologists. To facilitate OBS practice and investigate clip placement reliability, a realistic silicone-based breast phantom was constructed with components emulating a breast parenchyma, epidermis, areola, nipple, chest wall, and a tumor. OBS was performed on the phantom and surgical clips were placed to mark the tumor bed. The phantom was imaged with CT, MRI, and ultrasound (US). The parenchyma’s signal-to-noise ratio (SNR) and clips to parenchyma’s contrast-to-noise ratio (CNR) were measured. The phantom’s CT Hounsfield Unit (HU), relative electron density (RED), and mass density were determined. 6 and 10 MV photon beam attenuation measurements were performed in phantom material. The Young’s Modulus and ultimate tensile strength (UTS) of the phantom parenchyma and epidermis were measured. Results showed that the breast phantom components were visible on all imaging modalities with adequate SNR and CNR. The phantom’s HU is 130 ± 10. The RED is 0.983. Its mass density is 1.01 ± 0.01 g cm −3 . Photon attenuation measurements in phantom material were within 1% of those in water. The Young’s Moduli were 13.4 ± 4.2 kPa (mechanical) and 30.2 ± 4.1 kPa (US elastography) for the phantom parenchyma. The UTS’ were 0.05 ± 0.01 MPa (parenchyma) and 0.23 ± 0.12 MPa (epidermis). We conclude that the phantom’s imaging characteristics resemble a fibroglandular breast’s and allow clear visualization of high-density markers used in radiation therapy. The phantom material is suitable for dose measurements in MV photon beams. Mechanical results confirmed the phantom’s similarity to breast tissue. The phantom enables investigation of surgical clip displacements pre- and post-OBS, and is useful for radiation therapy quality assurance applications.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".