ArticleFrontiers in oncology2022
Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Practical recommendations for the management of radiodermatitis: on behalf of the ESTRO RTT committee.Radiation oncology (London, England) · 2025Guideline
- Prediction models of adverse outcomes following surgery and radiotherapy for breast cancer: a systematic review.ESMO real world data and digital oncology · 2026Review
- Validation of the Mechanism of Action of Jiedu Shengji Oil in the Treatment of Radiation Dermatitis based on Network Pharmacology andCurrent computer-aided drug design · 2026Article
- AI-enabled precision prediction and proactive management of cutaneous toxicities in cancer immunoradiotherapy (ICI+RT).Frontiers in oncology · 2026Review
- Artificial intelligence-based automated grading and severity prediction of radiation dermatitis: a review.Radiation oncology (London, England) · 2025Review
- Prediction of acute skin toxicity in tomotherapy of breast cancer using skin DVH data.Scientific reports · 2025Article
- Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Plasma branched-chain amino acids and risk of radiation-induced acute skin toxicity in women with breast cancer: results from the ATHENA project.Frontiers in oncology · 2025Article
- Predictive value of 8-hydroxy-2'-deoxyguanosine and 8-nitroguanine production in radiation-induced skin damage after postoperative breast cancer radiotherapy.Exploration of targeted anti-tumor therapy · 2025Article
- A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.European journal of medical research · 2024Article
- Article
- Ultra-Hypofractionated Whole Breast Radiotherapy with Automated Hybrid-VMAT Technique: A Pilot Study on Safety, Skin Toxicity and Aesthetic Outcomes.Breast cancer (Dove Medical Press) · 2024Article
- Metabolomics in Radiotherapy-Induced Early Adverse Skin Reactions of Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2024Article
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity. Methods and materials: One hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I Results: Thirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I Conclusions: Spectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.
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