Worldwide increase of head and neck cancers ranks these malignancies among top causes of cancer in human population. Radiation induced skin injury (RISI) is one of the major side effects of radiotherapy (RT). Skin of the neck is exposed to radiation due to necessity of therapeutic or prophylactic (elective) irradiation of neck lymph nodes and target organs, including the larynx and hypopharynx. The location of the neck exposes these regions of the skin to various additional exposomes such as ultraviolet radiation (UVR), pollution and cigarette smoke. There are many controversies or inconsistencies regarding RISI, from molecular aspects and therapy to terminology. There is lack of high-quality and large-sample studies in both forms of RISI: acute (aRISI) and chronic (cRISI). Finally, no gold standards in the management of aRISI and cRISI have been established yet. In this article, the authors discuss the pathogenesis, clinical picture, prevention and clinical interventions and present a proposed treatment algorithm.

OBJECTIVE: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).
METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.
RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.
CONCLUSIONS: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

OBJECTIVE: Active radiation skin injury (ARSI) has the highest incidence of acute adverse reactions caused by radiotherapy (RT) in patients with head and neck cancer (HNC). This study aimed to screen risk factors that can facilitate the identification of HNC patients at high risk of ARSI.
METHODS: Data from 255 stage III-IV HNC patients who underwent intensity-modulated radiation therapy (IMRT) were collected. The data from our medical records, including clinical characteristics and hematological indices before RT, were retrospectively collected and arranged. The Common Terminology Criteria for Adverse Events Criteria (CTCAE), Radiation Therapy Oncology Group Criteria (RTOG), World Health Organization Criteria (WHO), Oncology Nursing Society (ONS), Acute Radiation Dermatitis Graduation Scale, Douglas & Fowler and Radiation Dermatitis Severity Scale (RDSS) were used to assess ARSI. Of these, CTCAE was used for further analysis. Binary logistic regression analyses were used to identity risk factors. To establish the correction between each risk factor and the ARSI score, the odds ratio (OR) and 95% confidence interval (CI) were computed.
RESULTS: The assessment results of the CTCAE with RTOG, WHO, ONS, Graduation Scale, Douglas & Fowler and RDSS have good consistency. After radiotherapy, 18.4% of patients had at least 3 (3 +) grade ARSI. Multivariate logistic regression analysis revealed that the KPS score, blood glucose level, white blood cell count, and plasma free thyroxine (FT4) concentration were independent risk factors for 3 + grade ARSI. A nomogram was constructed on the basis of these risk factors, which demonstrated good predictive power according to the area under the ROC curve (AUC). The satisfactory consistency and clinical efficacy of the nomogram were confirmed by calibration curves and decision curve analysis (DCA).
CONCLUSIONS: A low KPS score, high blood glucose level, high white blood cell count, and high thyroid hormone prior to radiotherapy for stage III-IV HNC are independent risk factors for grade 3 + RSI.

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OBJECTIVE: Head and neck cancer patients frequently develop radiation dermatitis (RD) during radiotherapy. We compared liposomal gel with and without chamomile extract for the prevention and management of RD in head and neck cancer patients undergoing radiotherapy.
METHODS: Sixty participants undergoing radiotherapy for the first time for head and neck cancer were recruited in a double-blind randomized clinical trial. Primary outcomes were the occurrence of dry desquamation and the cumulative dose of ionizing radiation at the first occurrence of dry desquamation. Secondary outcomes were the occurrence of erythema, moist desquamation, global RD, and participant self-reported symptoms.
RESULTS: Dry desquamation occurred in 76.9 % (95 % CI: 57.9, 89.0) of participants in the chamomile liposomal gel group and in 88.9 % (95 % CI: 71.9, 96.1) in the liposomal gel group (p = 0.43). The median cumulative dose of ionizing radiation when dry desquamation occurred was 48.0 Gy in the chamomile liposomal gel group and 40.0 Gy in the liposomal gel group. Fewer symptoms were reported in the chamomile liposomal gel group.
CONCLUSIONS: No statistically significant differences in outcomes were found between the two groups, though the chamomile liposomal gel group presented with lower grades of RD than the liposomal gel group. In addition, when compared to the literature on other topical interventions to manage RD, our results show that chamomile liposomal gel has potential for better management and prevention of RD in this population, which should be further tested. This study provides crucial information to design future studies. Clinical Trials Registration (REBEC): RBR-92cts3.

OBJECTIVE: FAST-Forward and UK-FAST-trials have demonstrated the safety and efficacy of five-fraction breast adjuvant radiation therapy (RT) and have become the standard of care for selected early breast cancer patients. In response to the additional burden caused by the COVID-19 pandemic, we implemented \"One-Week Breast RT,\" an innovative program delivering five-fraction whole breast RT in a complete 5-day workflow. The primary objective of this study was to demonstrate the feasibility and safety of our program. The secondary objective was to evaluate cosmetic results.
METHODS: A total of 120 patients treated from February 2021 to March 2022, received whole breast RT without lymph node irradiation nor boost, with 26 Gy in five fractions over one week. Inverse planning with restricted optimization parameters offers systematic deep inspiration breath-hold aimed to provide treatment plans compliant with FAST-Forward recommendations. Toxicity and cosmetic evaluations were prospectively registered prior (pre-RT), at the end (end-RT), and 6 months after RT (6 months) based on Common Terminology Criteria for Adverse Events v. 4.03 and Harvard scale.
RESULTS: With a median age of 70 years (interquartile range (IQR): 66-74) and a median follow-up of 6 months (IQR: 6.01-6.25), most patients (93.3%) completed their RT in one week from baseline to the end of the treatment consultation. The most common acute toxicities (at end-RT) were skin-related: radio-dermatitis (72%), induration (35%), hyperpigmentation (8%), and breast edema (16%). The rate of radio-dermatitis decreased from end-RT to 6 months (71.7% vs 5.4%, P< 0.001). No patient experienced grade ≥3 toxicity. At 6 months, cosmetic results were generally good or excellent (94.1%).
CONCLUSIONS: This study confirms the feasibility and acute safety of the \"One-Week Breast RT\" in real life. Favorable toxicity profiles and good cosmetic outcomes are in line with FAST-Forward results. A prospective national cohort, aimed at decreasing treatment burden, maintaining safety, efficacy, and improving RT workflow efficiency with longer follow-up is ongoing.

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OBJECTIVE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models.
METHODS: Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson\'s correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning.
RESULTS: Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD.
CONCLUSIONS: The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

Radiotherapy is a pivotal means of cancer treatment, but it often leads to radiation dermatitis, a skin injury caused by radiation-induced excess reactive oxygen species (ROS). Scavenging free radicals in the course of radiation therapy will be an effective means to prevent radiation dermatitis. This study demonstrates a novel double network hydrogel doped with MoS2 nanosheets for the prevention of radiation-induced dermatitis. The resultant SPM hydrogel constructed from polyacrylamide (PAM) and sodium alginate (SA) nanofiber presented favorable mechanical and adhesion properties. It could conform well to the human body\'s irregular contours without secondary dressing fixation, making it suitable for skin protection applications. The in vitro and in vivo experiments showed that the antioxidant properties conferred by MoS2 nanosheets enable SPM to effectively mitigate excessive ROS and reduce oxidative stress, thereby preventing radiation dermatitis caused by oxidative damage. Biosafety assessments indicated good biocompatibility of the composite hydrogel, suggesting SPM\'s practicality and potential as an external dressing for skin radiation protection.