Chemotherapy-Induced Alopecia by Docetaxel: Prevalence, Treatment and Prevention
Bibliographic record
Abstract
Docetaxel is a commonly used taxane chemotherapeutic agent in the treatment of a variety of cancers, including breast cancer, ovarian cancer, prostate cancer, non-small cell lung cancer, gastric cancer, and head and neck cancer. Docetaxel exerts its anti-cancer effects through inhibition of the cell cycle and induction of proapoptotic activity. However, docetaxel also impacts rapidly proliferating normal cells in the scalp hair follicles (HFs), rendering the HFs vulnerable to docetaxel-induced cell death and leading to chemotherapy-induced alopecia (CIA). In severe cases, docetaxel causes persistent or permanent CIA (pCIA) when hair does not grow back completely six months after chemotherapy cessation. Hair loss has severe negative impacts on patients’ quality of life and may even compromise their compliance with treatment. This review discusses the notable prevalence of docetaxel-induced CIA and pCIA, as well as their prevention and management. At this moment, scalp cooling is the standard of care to prevent CIA. Treatment options to promote hair regrowth include but are not limited to minoxidil, photobiomodulation (PBMT), and platelet-rich plasma (PRP). In addition, a handful of current clinical trials are exploring additional agents to treat or prevent CIA. Research models of CIA, particularly ex vivo human scalp HF organ culture and in vivo mouse models with human scalp xenografts, will help expedite the translation of bench findings of CIA prevention and/or amelioration to the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".