Radiation-induced skin injury (RSI) refers to a frequently occurring complication of radiation therapy. Nearly 90% of patients having received radiation therapy underwent moderate-to-severe skin reactions, severely reducing patients' quality of life and adversely affecting their disease treatment. No gold standard has been formulated for RSIs. In the present study, the mechanism of RSI and topical medications was discussed. Besides, this study can be referenced for clinicians to treat RSIs to guide subsequent clinical medicine.

Radiation‐induced brain necrosis (RBN) is a serious complication of intracranial as well as skull base tumors after radiotherapy. In the past, due to the lack of effective treatment, radiation brain necrosis was considered to be progressive and irreversible. With better understanding in histopathology and neuroimaging, the occurrence and development of RBN have been gradually clarified, and new treatment methods are constantly emerging. In recent years, some scholars have tried to treat RBN with bevacizumab, nerve growth factor, and gangliosides and have achieved similar results. Some cases of brain necrosis can be repairable and reversible. We aimed to summarize the incidence, pathogenesis, and treatment of RBN.

Immunotherapy serves as another effective cancer treatment apart from surgery, chemoradiotherapy, and targeted drug therapy. Radiotherapy combined with immunotherapy has significantly improved the effective cure rate for patients in several clinical trials. It subverted the traditional view that radiotherapy kills immune cells and has immunosuppressive effects, indicating a synergistic effect of radiotherapy and immunotherapy. In this article, we reviewed and summarized the molecular mechanism of the combined use of radiotherapy and immunotherapy, as well as the clinical treatment and safety of the combination of the two. We describe the rationale for the integration of radiotherapy and immunotherapy in patients with cervical cancer, present safety and efficacy data that support this combination strategy, and highlight unanswered question sand future research needs. Besides, this study can be referenced for clinicians to guide subsequent clinical medicine.

Purpose: Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer. Materials and methods: We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT images in gynecological cancer. In nnU-Net, three architectures, including 2D U-Net, 3D U-Net and 3D-Cascade U-Net, were trained and finally ensembled. 207 cases were randomly chosen for training, and 30 for testing. Quantitative evaluation used well-established image segmentation metrics, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95%), and average surface distance (ASD). Qualitative analysis of automated segmentation results was performed visually by two radiation oncologists. The dosimetric evaluation was performed by comparing the dose-volume parameters of both predicted segmentation and human contouring. Results: nnU-Net obtained high qualitative and quantitative segmentation accuracy on the test dataset and performed better than previously reported methods in bladder and rectum segmentation. In quantitative evaluation, 3D-Cascade achieved the best performance in the bladder (DSC: 0.936 ± 0.051, HD95%: 3.503 ± 1.956, ASD: 0.944 ± 0.503), rectum (DSC: 0.831 ± 0.074, HD95%: 7.579 ± 5.857, ASD: 3.6 ± 3.485), and HRCTV (DSC: 0.836 ± 0.07, HD95%: 7.42 ± 5.023, ASD: 2.094 ± 1.311). According to the qualitative evaluation, over 76% of the test data set had no or minor visually detectable errors in segmentation. Conclusion: This work showed nnU-Net’s superiority in segmenting OARs and HRCTV in gynecological brachytherapy cases in our center, among which 3D-Cascade shows the highest accuracy in segmentation across different applicators and patient anatomy.

Background. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction techniques have been reported as promising solutions with high efficiency and accuracy. However, current dose prediction studies mainly target EBRT which are inappropriate for brachytherapy, the model designed specifically for brachytherapy has not yet well-established. Purpose. To predict dose distribution in brachytherapy using a novel Squeeze and Excitation Attention Net (SE_AN) model. Method. We hypothesized the tracks of ¹⁹²Ir inside applicators are essential for brachytherapy dose prediction. To emphasize the applicator contribution, a novel SE module was integrated into a Cascaded UNet to recalibrate informative features and suppress less useful ones. The Cascaded UNet consists of two stacked UNets, with the first designed to predict coarse dose distribution and the second added for fine-tuning 250 cases including all typical clinical applicators were studied, including vaginal, tandem and ovoid, multi-channel, and free needle applicators. The developed SE_AN was subsequently compared to the classic UNet and classic Cascaded UNet (without SE module) models. The model performance was evaluated by comparing the predicted dose against the clinically approved plans using mean absolute error (MAE) of DVH metrics, including D _2cc and D _90%. Results. The MAEs of DVH metrics demonstrated that SE_AN accurately predicted the dose with 0.37 ± 0.25 difference for HRCTV D _90%, 0.23 ± 0.14 difference for bladder D _2cc, and 0.28 ± 0.20 difference for rectum D _2cc. In comparison studies, UNet achieved 0.34 ± 0.24 for HRCTV, 0.25 ± 0.20 for bladder, 0.25 ± 0.21 for rectum, and Cascaded UNet achieved 0.42 ± 0.31 for HRCTV, 0.24 ± 0.19 for bladder, 0.23 ± 0.19 for rectum. Conclusion. We successfully developed a method specifically for 3D brachytherapy dose prediction. Our model demonstrated comparable performance to clinical plans generated by experienced dosimetrists. The developed technique is expected to improve the standardization and quality control of brachytherapy treatment planning.

Amino acid metabolism plays a critical role in the progression and development of breast cancer. Cancer cells, including those in breast cancer, reprogram amino acid metabolism to meet the demands of rapid proliferation, survival, and immune evasion. This includes alterations in the uptake and utilization of amino acids, such as glutamine, serine, glycine, and arginine, which provide essential building blocks for biosynthesis, energy production, and redox homeostasis. Notably, the metabolic phenotypes of breast cancer cells vary across molecular subtypes and disease stages, emphasizing the need for patient stratification and personalized therapeutic strategies. Advances in multi-level diagnostics, including phenotyping and predictive tools, such as AI-based analysis and body fluid profiling, have highlighted the potential for tailoring treatments to individual metabolic profiles. Enzymes, such as glutaminase and serine hydroxymethyltransferase, often upregulated in breast cancer, represent promising therapeutic targets. Understanding the interplay between amino acid metabolism and breast cancer biology, alongside the integration of personalized medicine approaches, can uncover novel insights into tumor progression and guide the development of precision therapies. This review explores the metabolic pathways of amino acids in breast cancer, with a focus on their implications for personalized treatment strategies.