Use of focal radiotherapy boost for prostate cancer: radiation oncologists’ perspectives and perceived barriers to implementation
Bibliographic record
Abstract
BACKGROUND: In a recent phase III randomized control trial, delivering a focal radiotherapy (RT) boost to tumors visible on MRI was shown to improve disease-free survival and regional/distant metastasis-free survival for patients with prostate cancer-without increasing toxicity. The aim of this study was to assess how widely this technique is being applied in current practice, as well as physicians' perceived barriers toward its implementation. METHODS: We invited radiation oncologists to complete an online questionnaire assessing their use of intraprostatic focal boost in December 2022 and February 2023. To include perspectives from a broad range of practice settings, the invitation was distributed to radiation oncologists worldwide via email list, group text platform, and social media. RESULTS: 263 radiation oncologist participants responded. The highest-represented countries were the United States (42%), Mexico (13%), and the United Kingdom (8%). The majority of participants worked at an academic medical center (52%) and considered their practice to be at least partially genitourinary (GU)-subspecialized (74%). Overall, 43% of participants reported routinely using intraprostatic focal boost. Complete GU-subspecialists were more likely to implement focal boost, with 61% reporting routine use. In both high-income and low-to-middle-income countries, less than half of participants routinely use focal boost. The most cited barriers were concerns about registration accuracy between MRI and CT (37%), concerns about risk of additional toxicity (35%), and challenges to accessing high-quality MRI (29%). CONCLUSIONS: Two years following publication of a randomized trial of patient benefit without increased toxicity, almost half of the radiation oncologists surveyed are now routinely offering focal RT boost. Further adoption of this technique might be aided by increased access to high-quality MRI, better registration algorithms of MRI to CT simulation images, physician education on benefit-to-harm ratio, and training on contouring prostate lesions on MRI.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".