Dose Enhancement for the Flattening-Filter-Free and Flattening-Filter Photon Beams in Nanoparticle-Enhanced Radiotherapy: A Monte Carlo Phantom Study
Bibliographic record
Abstract
Monte Carlo simulations were used to predict the dose enhancement ratio (DER) using the flattening-filter-free (FFF) and flattening-filter (FF) photon beams in prostate nanoparticle-enhanced radiotherapy, with multiple variables such as nanoparticle material, nanoparticle concentration, prostate size, pelvic size, and photon beam energy. A phantom mimicking the patient's pelvis with various prostate and pelvic sizes was used. Macroscopic Monte Carlo simulation using the EGSnrc code was used to predict the dose at the prostate or target using the 6 MV FFF, 6 MV FF, 10 MV FFF, and 10 MV FF photon beams produced by a Varian TrueBeam linear accelerator (Varian Medical System, Palo Alto, CA, USA). Nanoparticle materials of gold, platinum, iodine, silver, and iron oxide with concentration varying in the range of 3-40 mg/ml were used in simulations. Moreover, the prostate and pelvic size were varied from 2.5 to 5.5 cm and 20 to 30 cm, respectively. The DER was defined as the ratio of the target dose with nanoparticle addition to the target dose without nanoparticle addition in the simulation. From the Monte Carlo results of DER, the best nanoparticle material with the highest DER was gold, based on all the nanoparticle concentrations and photon beams. Smaller prostate size, smaller pelvic size, and a higher nanoparticle concentration showed better DER results. When comparing energies, the 6 MV beams always had the greater enhancement ratio. In addition, the FFF photon beams always had a better DER when compared to the FF beams. It is concluded that gold nanoparticles were the most effective material in nanoparticle-enhanced radiotherapy. Moreover, lower photon beam energy (6 MV), FFF photon beam, higher nanoparticle concentration, smaller pelvic size, and smaller prostate size would all increase the DER in prostate nanoparticle-enhanced radiotherapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".