Estimating Potential Benefits to Neurocognition with Proton Therapy in Adults with Brain Tumors
Bibliographic record
Abstract
Purpose. Photon radiation therapy (RT) is important in the treatment of many brain tumors but can negatively affect neurocognition. Proton therapy (PT) can reduce doses to normal brain structures. We compared photon and proton plans to estimate the potential benefit in cognition if the patient were treated with PT.Materials and Methods. We analyzed 23 adult patients with proton and photon plans for the treatment of a primary brain tumor. Cognitive outcomes were predicted using converted equivalent dose (EQD2) with an α/β ratio of 3 to left temporal lobe and normal brain tissue. Risks of cognitive decline on 2 specific tests, the Controlled Oral Word Association Test (COWAT [letter S], a test of verbal fluency) and the Wechler Adult Intelligence Scale (WAIS-IV Coding Test, a test of processing speed) were derived from a previously published model.Results. Dose reductions to left temporal lobe and normal brain tissue translated into lower estimated probabilities of impairment in specific neurocognitive test scores after PT. With a mean dose reduction from 1490 to 1092 cGy in EQD2 to the left temporal lobe (P < .001), there was reduction in probability of impairment in the COWAT (Letter S) test from 6.8% to 5.4%. Similar results were seen with the normal brain (750 to 451 cGy in EQD2, P < .001), with reduction in probability of impairment in the WAIS-IV Coding test from 5% to 4.1%. Other structures experiencing dose reduction with PT included each cochlea, posterior fossa, each temporal lobe, and each hippocampus.Conclusion. We confirmed an association between PT and lower doses to brain substructures, which is expected to result in a modest decrease in probability of impairment in neurocognitive test scoring. These findings should be confirmed in prospective cohorts of patients treated with PT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".