Use of focal radiotherapy boost for prostate cancer and perceived barriers toward its implementation: a survey
Bibliographic record
Abstract
Abstract Background In a recent phase III randomized control trial (FLAME), delivering a focal radiotherapy (RT) boost to tumors visible on MRI was shown to improve outcomes for prostate cancer patients 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 An online survey assessing the use of intraprostatic focal boost was conducted in December 2022 and February 2023. The survey link was distributed to radiation oncologists worldwide via email list, group text platform, and social media. Results The survey initially collected 205 responses from various countries over a two-week period in December 2022. The survey was then reopened for one week in February 2023 to allow for more participation, leading to a total of 263 responses. 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%). 57% of participants reported not routinely using intraprostatic focal boost. Even among complete subspecialists, a substantial proportion (39%) do not routinely use focal boost. Less than half of participants in both high-income and low-to-middle-income countries were shown to routinely use focal boost. The most commonly 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%). Conclusion Despite level 1 evidence from the FLAME trial, most radiation oncologists surveyed are not routinely offering focal RT boost. Adoption of this technique might be accelerated 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".