A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.

BACKGROUND: Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. METHODS: Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations. RESULTS: Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. CONCLUSIONS: This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Aquaporin 4 beyond a water channel; participation in motor, sensory, cognitive and psychological performances, a comprehensive review.

Aquaporin 4 (AQP4) is a protein highly expressed in the central nervous system (CNS) and peripheral nervous system (PNS) as well as various other organs, whose different sites of action indicate its importance in various functions. AQP4 has a variety of essential roles beyond water homeostasis. In this article, we have for the first time summarized different roles of AQP4 in motor and sensory functions, besides cognitive and psychological performances, and most importantly, possible physiological mechanisms by which AQP4 can exert its effects. Furthermore, we demonstrated that AQP4 participates in pathology of different neurological disorders, various effects depending on the disease type. Since neurological diseases involve a spectrum of dysfunctions and due to the difficulty of obtaining a treatment that can simultaneously affect these deficits, it is therefore suggested that future studies consider the role of this protein in different functional impairments related to neurological disorders simultaneously or separately by targeting AQP4 expression and/or polarity modulation.

Dose perturbation due to dental amalgam in the head and neck radiotherapy: A phantom study.

Dental amalgam, causes perturbation in photon dose distribution of head and neck (H&N) radiotherapy. The aim of this study was to evaluate the effects of dental amalgam on dose distribution of H&N radiotherapy and accuracy of dose calculations algorithm of commercial treatment planning system (TPS). In this study, the measurements were performed using a constructed H&N anthropomorphic. The sample of healthy teeth and teeth filled by amalgam inserted in the desired segment of the phantom in turn. After scanning and organs segmentation of phantom, intensity-modulated radiation therapy (IMRT) plan including 7 fields in the absence (plan 1) and presence (plan 2) of dental amalgam were created separately. Phantom was irradiated using 6 MV linear accelerator (SIMENS-ARTISTE, 5918). Assessment of the effects of dental amalgam on dose distribution and the accuracy of dose calculation algorithm of TPS was done by measurement and comparing of organ's received dose using thermoluminescent dosimeter (TLDs), placed on a phantom and TPS calculations. The scattering and attenuation due to the presence of dental amalgam led to an increase in parotid glands received dose (up to 24.38%) and a decrease in mean dose (up to -6.25%) PTV70. Results of this study revealed that discrepancies between the collapsed cone convolution (CCC) algorithm calculations Prowess Panther TPS and TLD measurements were -19.77% to 27.49% in presence of amalgam and -1.09% to 5.03% in presence of healthy teeth in phantoms. Attenuation and scattering due to amalgam in IMRT of H&N cancer may lead to a significant dose perturbation which is not predictable by dose calculation of TPS.