Introduction: This study investigates the enhancement of relative biological effectiveness (RBE) in proton therapy for ocular tumors using heavy metal nanoparticles using Monte Carlo simulation. Material and Methods: Through multiscale Monte Carlo simulations with Geant4-DNA, we evaluated the impact of gold (Au), bismuth (Bi), iridium (Ir), and platinum (Pt) nanoparticles (with 25 nm radius and 30 mg/g concentration) on the RBE of a 62-MeV spread-out Bragg peak (SOBP). A mammalian fibroblast cell nucleus was simulated and DNA damages induced by protons (with and without the presence of nanoparticles) were calculated as biological endpoint for RBE calculation.Results: The results demonstrated that all nanoparticles increased RBE compared to the nanoparticle-free condition. Pt and Ir nanoparticles yielded the greatest enhancement, outperforming Au, while Bi nanoparticles provided the least improvement. The most substantial RBE increase occurred at the distal end of the SOBP, correlating with the region of highest linear energy transfer (LET). Calculations based on DNA damage and cell survival curves confirmed that the choice of biological endpoint significantly influences the computed RBE values. A key finding was that nanoparticle density, rather than atomic number, was the dominant factor in RBE improvement. This suggests that high-density Pt nanoparticles are a more effective radio-sensitizer than Au. Conclusion: The study underscores the importance of incorporating variable RBE models and radiobiological effects of nanoparticles into proton therapy treatment planning systems to improve clinical outcomes.
Introduction: Skin cancer is a prevalent global malignancy, and despite CNN advances in automated diagnosis, challenges in feature discrimination, robustness, and performance stability remain. Moreover, most previous studies primarily report single-run performance metrics, providing limited insight into the reproducibility and stability of deep learning models in clinically relevant settings. Material and Methods: In this study, a Shuffle Attention–enhanced ResNet50 (SA-ResNet50) architecture was developed for automated skin lesion classification. The proposed model was systematically compared with baseline ResNet50 and ResNet50 integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted on two publicly available dermoscopic datasets, PH2 and HAM10000, using two data-partitioning strategies. To evaluate model robustness, independent-repeated ten times and K-fold cross validation methods were used for experiments, and performance variability was analyzed across repeated runs. Results: The proposed SA-ResNet50 consistently outperformed the baseline models on both datasets while exhibiting lower performance variability. On the PH2 dataset, the proposed model achieved an accuracy of 98.9 ± 0.4%, sensitivity of 99.2 ± 0.3%, and specificity of 99.6 ± 0.2%. On the HAM10000 dataset with the first data partitioning strategy, SA-ResNet50 achieved an accuracy of 90.2 ± 1.6%, sensitivity of 85.7 ± 2.0%, and specificity of 92.8 ± 1.3%. In second strategy, the proposed model achieved an accuracy of 84.31±3.48%, Macro-F1 score of 71.86±7.23%, Balanced Accuracy of 70.29±9.36%, Matthews Correlation Coefficient (MCC) of 68.15±9.36, and an Area Under the ROC Curve (AUC) of 92.47±1.91, demonstrating robust performance on the highly imbalanced HAM10000 dataset with second data partitioning strategy. Conclusion: The experimental results demonstrate that integrating the Shuffle Attention module into the ResNet50 backbone improves discriminative feature representation and enhances model robustness and convergence stability for dermoscopic skin lesion classification across benchmark datasets. These findings suggest that the proposed SA-ResNet50 constitutes an effective and reliable framework for automated skin lesion classification.
Introduction: Radiation therapy is the major treatment modality of prostate cancer. Genistein (GEN) as the most abundant isoflavone in soybeans prevents the repair of DNA breaks, improving the radiosensitivity of cancer cells. It is demonstrated that cancer cells can be eradicated outside the radiation field as a result of the bystander effect. The aim of this study was to investigate the direct and indirect effect of ionizing radiation (IR), GEN and their combination on prostate cancer cell line (PC-3).Material and Methods: Cells were irradiated to 4 Gy IR 24h after treatment with GEN. Then the medium of cells was transferred to non-irradiated cells to induce bystander effect. The direct and indirect effects of GEN, IR, and their combination (GEN+IR) on cell cycle and apoptosis were assessed by flow-cytometry.Results: Cell cycle arrest has occurred in the G2/M phase in the GEN and GEN+IR groups, while no significant differences were observed in the bystander groups. Apoptosis levels were also significantly increased by GEN, IR and combined treatment. Although apoptosis was significantly higher in Bys+IR group compared to control, indirect exposure to both GEN and IR did not enhance apoptosis levels. Conclusion: Overall, the results demonstrate that GEN enhances PC-3 cells radiosensitivity by promoting apoptosis and cell cycle arrest. However, bystander effect was not observed by indirect exposure of PC-3 cells to both GEN and IR. Future research are crucial to explore the radiation-induced bystander effect in the presence of GEN.
Introduction: Radiotherapy for advanced head and neck cancer (HNC) is complicated by anatomical changes, tumor shrinkage, and organ displacement, which may cause dosimetric inaccuracies and increased toxicity. Adaptive radiotherapy (ART) remains challenging in resource-limited settings due to time and personnel constraints. This study investigated the feasibility of the Anatomy-Preserving Estimation Network (APE-Net), a treatment planning system (TPS)-informed predictive framework for supporting offline adaptive radiotherapy (oART). Material and Methods: Data from 21 retrospectively analyzed HNC patients were used, including baseline (Plan 1), unadapted (Plan 2), and adapted (Plan 3) treatment plans. Patients underwent cone-beam computed tomography (CBCT) monitoring, with rescan CT (rCT) acquired when anatomical deviations of >1 cm occurred. Plan 2 was recalculated using rCT images and original treatment parameters, while Plan 3 was re-optimized using the Eclipse TPS (Version 16, Varian Medical Systems, Palo Alto, CA, USA) at Moi Teaching and Referral Hospital, Eldoret, Kenya. TPS-derived dosimetric and volumetric features were extracted. 108 synthetic TPS-derived feature samples for augmentation were generated. APE-Net was implemented as a fully connected multilayer perceptron (MLP) trained on TPS-derived feature vectors to estimate adaptive treatment parameters. Results: APE-Net trained on both real and synthetic TPS-derived features demonstrated moderate predictive performance, achieving a coefficient of determination (R²) of 0.681, mean absolute error (MAE) of 9.092% and root mean squared error (RMSE) of 16.776%. Training and validation loss curves showed stable convergence with limited divergence. Conclusion: APE-Net demonstrates feasibility as a TPS-informed machine-learning framework for supporting predictive oART workflows in resource-limited settings.
Introduction: Breast cancer (BC) ranks as the second most frequently diagnosed cancer among women globally. The aim of this study was to evaluate the effect of the patient’s arm positioning on dose distribution in the organs at risk (OARs) and target in BC radiotherapy for mastectomy patients.Material and Methods: Computed tomography (CT) scans of images of 30 right BC patients were collected. The patients were divided into two groups, each with 15 patients. The first group was with right arm-up positioning, and the second was with both arms-up positioning. The Monaco treatment planning system (TPS) was used for treatment planning. Radiotherapy included a prescribed dose of 50 Gy/25 fractions. For the planning target volume (PTV), the minimum dose (Dmin), mean dose (Dmean), maximum dose (Dmax), homogeneity index (HI), conformity index (CI), and tumor control probability (TCP) were calculated. For the OARs Dmin, Dmean, Dmax, and V20 Gy (%) for the lung and normal tissue complication probability (NTCP) were assessed. We compared the two groups based on PTV and OAR levels.Results: There was no statistical difference between quantities for PTV and OARs for the lung, heart, and spinal cord, but a statistical difference was only found in Dmean and Dmax for the thyroid gland.Conclusion: Although a statistically significant difference in thyroid dose was found, the absolute difference is small and within tolerance limits. Both positioning techniques provide comparable coverage of the target volume and sparing of the critical organs. With regard to patient comfort and ease of reproducibility during radiotherapy treatments, positioning with both arms up is generally favored, while positions with the right arm up may be an alternative if thyroid dose minimization is an issue.
