Case Report: MR-Guided Adaptive Radiotherapy, Some Room to Maneuver
Bibliographic record
Abstract
Background: A magnetic resonance linear accelerator (MR-Linac) provides superior soft tissue contrast to evaluate inter- and intra-fraction motion and facilitate online adaptive radiation therapy (ART). We present here an unusual case of locally advanced castrate-resistant prostate cancer treated with high-dose palliative ultra-hypofractionated radiation therapy on the MR-Linac with significant inter-fraction tumor regression. Case Presentation: The patient was a 65-year-old man diagnosed with metastatic prostate cancer to bone and pelvic lymph nodes 7 years prior. At diagnosis, he presented with a PSA of 23 ng/ml and was commenced on a luteinizing hormone-releasing hormone agonist, achieving a PSA nadir of 4.68 ng/ml at 12 months. The patient subsequently had progressive lower urinary tract symptoms, his PSA increased to 47 ng/ml, and there was a markedly enlarged pelvic mass involving the prostate with gross extra-capsular disease and invasion into the posterior bladder wall. The patient was referred for palliative radiation to the pelvic mass due to urinary symptoms, pain, and lower limb paraesthesia. Treatment was planned to be delivered on the MR-Linac with a schedule of 36 Gy over 6 weekly factions allowing for maximal target dose delivery while minimizing surrounding organs at risk (OARs) radiation exposure. Unexpectedly, the target volume had a marked 49% (453 cc to 233 cc) reduction that was accounted for in the online adaptive process. A new reference plan was generated after 3 fractions to add sacral plexus as an OAR, previously not visible due to mass encroachment. The patient reported ongoing reduction in urinary symptoms, pelvic pain, and lower limb paresthesia by the end of treatment. Conclusion: Using daily MR-guided ART, improved visualization of the changing target and OARs ensured safe dose escalation. The unexpected positive response of the target and improved patient outcomes demonstrated the added value of the MR-Linac for online adaptive radiotherapy in this setting.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".