Modeling Normal Tissue Complication Probability of Radiation-Induced Alopecia Following Intensity-Modulated Radiation Therapy in Glioblastoma Patients

Multivariate logistic regression, integrating dosimetric and clinical factors, offers enhanced predictive accuracy for RIA compared to the LKB model. These findings emphasize the importance of optimizing IMRT plans to limit scalp dose and considering chemotherapy in risk stratification. Implementation of these models may improve patient-centered care by balancing tumor control and toxicity reduction.

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Publication Details

Journal
Advanced Biomedical Research
Published
2025-12-01
DOI
https://doi.org/10.4103/abr.abr_280_25
Primary Topic
Hair Growth and Disorders
Type
article
Field-Weighted Citation Impact
0.00
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article

Modeling Normal Tissue Complication Probability of Radiation-Induced Alopecia Following Intensity-Modulated Radiation Therapy in Glioblastoma Patients

Ahmad Shanei, Korosh Saber, Mahnaz Roayaei
Advanced Biomedical Research
Hair Growth and Disorders
article

Modeling Normal Tissue Complication Probability of Radiation-Induced Alopecia Following Intensity-Modulated Radiation Therapy in Glioblastoma Patients

Ahmad Shanei, Korosh Saber, Mahnaz Roayaei
article en

Abstract

Multivariate logistic regression, integrating dosimetric and clinical factors, offers enhanced predictive accuracy for RIA compared to the LKB model. These findings emphasize the importance of optimizing IMRT plans to limit scalp dose and considering chemotherapy in risk stratification. Implementation of these models may improve patient-centered care by balancing tumor control and toxicity reduction.

Advanced Biomedical ResearchVol. 14(1)
Isfahan University of Medical Sciences (IR)
Peace, Justice and strong institutions
Openalex Percentile: Top 27%
Hair Growth and Disorders
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