Immunological factors underlying radiation-induced toxicity in prostate cancer patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: Radiotherapy (RT) represents a well-established treatment modality for patients diagnosed with prostate cancer. Despite a low toxicity profile, a small proportion of patients suffer long-term effects with a decrease in quality of life. Several risk factors for the prediction of radiation-induced toxicity have been evaluated. This study aimed to systematically review the current literature on the immunological factors underlying radiation-induced toxicity in prostate cancer patients undergoing RT. METHODS: A comprehensive search in three databases (PubMed, Web of Science, Scopus) was conducted. Eligible papers reported on toxicity and immunological factors in patients treated with RT with curative intent. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in each step. The quality of the included articles was assessed utilizing the Newcastle Ottawa Quality Assessment Scale (NOS). RESULTS: A total of 15 studies, encompassing 1237 patients, were included, with a median age ranging from 62 to 74 years. Ten studies reported on primary RT only, while five assessed both primary and postoperative RT. Tumor stage and lymph node involvement varied among studies. Utilized radiation treatment modalities were intensity-modulated radiotherapy, stereotactic body radiotherapy, three-dimensional conventional radiotherapy, and low-dose rate brachytherapy. Androgen deprivation therapy was administered and reported inconsistently. Immunological factors and assessed toxicities varied between studies. Thus, reports on immunological factors and predictive biomarkers remain scarce and inconclusive. Besides lymphocyte counts, the most common molecules that significantly correlated with RT-induced toxicity in prostate cancer patients were interleukin-6 (IL-6), interleukin-2 (IL-2), tumor necrosis factor-α (TNF-α), and transforming growth factor-β1 (TGF-β1). CONCLUSION: Publications on immunological factors as predictors of RT-induced toxicity remain rare and exhibit a high degree of variability in treatment, toxicity, and immunological factor assessment. Yet, IL-6, IL-2, TNF-α, and lymphocyte counts are the most consistent markers for predicting toxicity in prostate cancers post-RT. Future preclinical and clinical studies should corroborate these findings, with the final goal of improving patient treatment 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".