From CBCT to MR-Linac in Image-Guided Prostate Cancer Radiotherapy Towards Treatment Personalization
Bibliographic record
Abstract
Purpose: Image-guided radiotherapy (IGRT) has been widely implemented in the treatment of prostate cancer, offering a number of advantages regarding the precision of dose delivery. This study provides an overview of factors, clinical and physical alike, that increase treatment accuracy in prostate cancer radiotherapy in the context of IGRT. The following aspects are explored based on recent literature: the radiotherapy technique used in conjunction with IGRT, the type and frequency of IGRT, the impact of radiotherapy technique/IGRT on target dosimetry and organs at risk, the influence of IGRT on planning target volume margins, the impact of treatment time on dosimetric outcome and clinical outcomes using IGRT repositioning or an online adaptive plan. Methods: A systematic search of the literature was conducted within Pubmed/Medline databases to find relevant studies. Of the 152 articles fulfilling the initial search criteria, 79 were selected for final analysis. Results: The frequency of image guidance, the treatment regimen and the radiation technique are important factors that contribute to the optimization and personalization of the treatment plan. The daily anatomy and volume of the bladder and rectum can vary considerably, which can significantly impact the dosimetric effects on these organs. When used in conjunction with volumetric modulated arc therapy, IGRT allows for shaping the dose distribution to avoid nearby critical structures such as the bladder and rectum. Conclusions: Precise tumor targeting via IGRT can result in fewer geometric uncertainties, thereby improving treatment outcome both in terms of superior target coverage and sparing organs at risk.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".