Exploring radiation-induced fibrosis: biological mechanisms and new frontiers in research and therapeutics
Bibliographic record
Abstract
PURPOSE: Radiation-induced fibrosis (RIF) is a significant long-term complication of radiotherapy, affecting many cancer patients months to years after treatment. Characterized by progressive tissue stiffening, loss of elasticity, and impaired organ function, RIF can deleteriously impact a patient's quality of life. Commonly affected sites include the skin, lung, heart, and kidney. Advances in radiotherapy techniques, such as intensity-modulated radiation therapy (IMRT), stereotactic body radiotherapy (SBRT), and image-guided radiotherapy (IGRT), have improved the precision of radiation delivery, reducing acute damage to healthy tissues; RIF however, remains a prevalent complication despite these technological advancements. This review explores the underlying cellular and molecular mechanisms of RIF, emphasizing fibroblast proliferation, myofibroblast activation, and excessive extracellular matrix (ECM) deposition in its progression. Additionally, this review highlights in vitro and in vivo models that are instrumental in studying RIF and evaluates current therapeutic strategies aimed at mitigating RIF. CONCLUSION: inhibitors, and molecular interventions aimed at ECM regulation, offer promising avenues for mitigating or reversing RIF. Additionally, integrating computational models into clinical practice could enhance personalized treatment planning, enabling better prediction and prevention of RIF in patients. Addressing these challenges is critical for improving the quality of life of patients affected by RIF and improving their outcomes, particularly with the growing population of long-term cancer survivors in the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".