Magnetic resonance for radiotherapy management and treatment planning in prostatic carcinoma
Bibliographic record
Abstract
MRI has an important role for radiotherapy (RT) treatment planning in prostate cancer (PCa) providing accurate visualization of the dominant intraprostatic lesion (DIL) and locoregional anatomy, assessment of local staging and depiction of implanted devices. MRI enables the radiation oncologist to optimize RT planning by better defining target tumour volumes (thereby increasing local tumour control), as well as decreasing morbidity (by minimizing the dose to adjacent normal structures). Using MRI, radiation oncologists can define the DIL for delivery of boost doses of RT using a variety of techniques including: stereotactic body radiotherapy, intensity-modulated radiotherapy, proton RT or brachytherapy to improve tumour control. Radiologists require a familiarity with the different RT methods used to treat PCa, as well as an understanding of the advantages and disadvantages of the various MR pulse sequences available for RT planning in order to provide an optimal multidisciplinary RT treatment approach to PCa. Understanding the expected post-RT appearance of the prostate and typical characteristics of local tumour recurrence is also important because MRI is rapidly becoming an integral component for diagnosis, image-guided histological sampling and treatment planning in the setting of biochemical failure after RT or surgery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".