Longitudinal Cytokine Expression during IMRT for Prostate Cancer and Acute Treatment Toxicity
Bibliographic record
Abstract
PURPOSE: Proteomic profiling of patients undergoing intensity-modulated radiotherapy (IMRT) for prostate cancer can identify unique biomarkers that reflect acute toxicity in normal tissues. Our objectives were to measure inflammatory cytokine proteins during IMRT and assess the variability of individual proteomic signatures. EXPERIMENTAL DESIGN: Forty-two patients with intermediate-risk prostate cancer were recruited as follows: group 1, definitive IMRT (78 Gy in 39 fractions, n = 22), and group 2, IMRT postprostatectomy (66 Gy in 33 fractions, n = 20). Blood/urine samples were collected at baseline and weekly during IMRT. Acute toxicity was graded weekly during radiotherapy using CTC-AE v3.0 criteria. Multiplexed immunoassays were used to quantify cytokines including granulocyte macrophage colony-stimulating factor, IFN-gamma, tumor necrosis factor-alpha, interleukin (IL)-1alpha, IL-2, IL6, IL-8, IL-10, and IL-12p70. RESULTS: We observed positive correlations between cytokine expression between serum and plasma, but not between serum/plasma and urine. The Mann-Whitney test showed a significant increase in IFN-gamma and IL-6 during IMRT (P = 0.0077, 0.0035). Increasing IL-2 and IL-1 expression were associated with increased probability of acute gastrointestinal and genitourinary toxicity, respectively. CONCLUSIONS: Determination of radiation-response signatures is feasible using multiplexed immunoassays and is a promising predictive early biomarker of toxicity outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".