Compared with solid scintillators, liquid scintillators have limited capability in dosimetry and radiography due to their relatively low light yields. Here, we report a new generation of highly efficient and low-cost liquid scintillators constructed by surface hybridisation of colloidal metal halide perovskite CsPbA₃ (A: Cl, Br, I) nanocrystals (NCs) with organic molecules (2,5-diphenyloxazole). The hybrid liquid scintillators, compared to state-of-the-art CsI and Gd₂O₂S, demonstrate markedly highly competitive radioluminescence quantum yields under X-ray irradiation typically employed in diagnosis and treatment. Experimental and theoretical analyses suggest that the enhanced quantum yield is associated with X-ray photon-induced charge transfer from the organic molecules to the NCs. High-resolution X-ray imaging is demonstrated using a hybrid CsPbBr₃ NC-based liquid scintillator. The novel X-ray scintillation mechanism in our hybrid scintillators could be extended to enhance the quantum yield of various types of scintillators, enabling low-dose radiation detection in various fields, including fundamental science and imaging.

A deep-neural-network (DNN) was successfully used to predict clinically-acceptable dose distributions from organ contours for intensity-modulated radiotherapy (IMRT). To provide the next step in the DNN-based plan automation, we propose a DNN that directly generates beam fluence maps from the organ contours and volumetric dose distributions, without inverse planning. We collected 240 prostate IMRT plans and used to train a DNN using organ contours and dose distributions. After training was done, we made 45 synthetic plans (SPs) using the generated fluence-maps and compared them with clinical plans (CP) using various plan quality metrics including homogeneity and conformity indices for the target and dose constraints for organs at risk, including rectum, bladder, and bowel. The network was able to generate fluence maps with small errors. The qualities of the SPs were comparable to the corresponding CPs. The homogeneity index of the target was slightly worse in the SPs, but there was no difference in conformity index of the target, V_60Gy of rectum, the V_60Gy of bladder and the V_45Gy of bowel. The time taken for generating fluence maps and qualities of SPs demonstrated the proposed method will improve efficiency of the treatment planning and help maintain the quality of plans.

Volumetric-modulated arc therapy (VMAT) is a highly sophisticated linear accelerator-based treatment method, and allows dose rate-changing intensity modulation with gantry rotation. We report our clinical experiences with stereotactic body radiation therapy (SBRT) using a respiratory-gated VMAT technique for patients with hepatocellular carcinoma (HCC) when established curative treatments cannot be applied. A total of 119 patients (139 lesions) with HCC who were treated with SBRT were registered between March 2012 and July 2013 at our institution. A dose of 10–15 Gy per fraction was applied over 3–4 consecutive days, resulting in a total dose of 30–60 Gy. The median follow-up period was 25.8 months (range, 3.2–36.8 months). The overall 3-year survival rate was 83.8%. The local control rate at 3 years was 97.0% in all treated lesions. Multivariate analysis revealed that the Child-Pugh class before SBRT had significant effects on overall survival (Child-Pugh A: hazard ratio = 0.463; 95% CI, 0.262–0.817; p = 0.008). SBRT using a respiratory-gated VMAT technique was an excellent ablative treatment modality for patients with HCC. SBRT is a good alternative treatment for patients with small HCCs that are unsuitable for surgical resection or local ablative therapy.

Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.

Purpose: To compare the dosimetric impact and treatment delivery efficacy of phase‐gated volumetric modulated arc therapy (VMAT) vs amplitude‐gated VMAT for stereotactic body radiation therapy (SBRT) for lung cancer by using realistic three‐dimensional‐printed phantoms.Methods: Four patient‐specific moving lung phantoms that closely simulate the heterogeneity of lung tissue and breathing patterns were fabricated with four planning computed tomography (CT) images for lung SBRT cases. The phantoms were designed to be bisected for the measurement of two‐dimensional dose distributions by using EBT3 dosimetry film. The dosimetric accuracy of treatment under respiratory motion was analyzed with the gamma index (2%/1 mm) between the plan dose and film dose measured under phase‐ and amplitude‐gated VMAT. For the validation of the direct usage of the real‐time position management (RPM) data for respiratory motion, the relationship between the RPM signal and the diaphragm position was measured by four‐dimensional CT. By using data recorded during the beam delivery of both phase‐ and amplitude‐gated VMAT, the total time intervals were compared for each treatment mode.Results: Film dosimetry showed a 5.2 ± 4.2% difference of gamma passing rate (2%/1 mm) on average between the phase‐ vs amplitude‐gated VMAT [77.7% (72.7%–85.9%) for the phase mode and 82.9% (81.4%–86.2%) for the amplitude mode]. For delivery efficiency, frequent interruptions were observed during the phase‐gated VMAT, which stopped the beam delivery and required a certain amount of time before resuming the beam. This abnormality in phase‐gated VMAT caused a prolonged treatment delivery time of 366 s compared with 183 s for amplitude‐gated VMAT.Conclusions: Considering the dosimetric accuracy and delivery efficacy between the gating methods, amplitude mode is superior to phase mode for gated VMAT treatment.