Journal of Radiation Research最新文献

The purpose of this study was to investigate the status of remote-radiotherapy treatment planning (RRTP) in Japan through a nationwide questionnaire survey. The survey was conducted between 29 June and 4 August 2022, at 834 facilities in Japan that were equipped with linear accelerators. The survey utilized a Google form that comprised 96 questions on facility information, information about the respondent, utilization of RRTP between facilities, usage for telework and the inclination to implement RRTPs in the respondent's facility. The survey analyzed the utilization of the RRTP system in four distinct implementation types: (i) utilization as a supportive facility, (ii) utilization as a treatment facility, (iii) utilization as a teleworker outside of the facility and (iv) utilization as a teleworker within the facility. The survey response rate was 58.4% (487 facilities responded). Among the facilities that responded, 10% (51 facilities) were implementing RRTP. 13 served as supportive facilities, 23 as treatment facilities, 17 as teleworkers outside of the facility and 5 as teleworkers within the facility. In terms of system usage between supportive and treatment facilities, 70-80% of the participants utilized the system for emergencies or as overtime work for external workers. A substantial number of facilities (38.8%) reported that they were unfamiliar with RRTP implementation. The survey showed that RRTP utilization in Japan is still limited, with a significant number of facilities unfamiliar with the technology. The study highlights the need for greater understanding and education about RRTP and financial funds of economical compensation.

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45 Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45 Gy group.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

This retrospective study aimed to evaluate the time to recovery from xerostomia and analyze its predictors, along with long-term outcomes of stimulated salivary flow after intensity-modulated radiation therapy (IMRT) for head and neck cancer (HNC). We evaluated patients with HNC who had received IMRT with curative intent between 2012 and 2018 at our institution. The salivary recovery ratio (SRR) was defined as '(the stimulated salivary flow)/(pre-treatment salivary flow)'. The cutoff value of SRR in salivary recovery was estimated via the relationship between SRR and xerostomia grades. The salivary recovery time was defined as the time for SRR to exceed cutoff values from the end of radiotherapy. Fifty-seven patients were analyzed, with a 48-month median follow-up period of stimulated salivary flow. The cutoff value for SRR was 44.8%, and patients with a higher grade of xerostomia had a lower SRR (P < 0.001). The median salivary recovery time was 12 months. The cumulative incidence rates of salivary recovery at two and four years were 84 (95% confidence interval [CI]: 53-79) and 92% (95% CI: 82-97), respectively, and these were significantly lower in patients with a higher mean parotid gland dose, mean oral cavity dose and stimulated salivary flow per parotid gland volume. Stimulated salivary flow and xerostomia recover over a long period after radiotherapy.

The aim of the present work was to examine the effect of polyethylene glycol (PEG)-coated superparamagnetic iron oxide (SPIO) nanoparticles carrying Pik3cb short hairpin RNA (shRNA) in the prevention of restenosis with the aid of ultrasound and a magnetic field. SPIO is a type of contrast agent used in medical imaging to enhance the visibility of specific tissues or organs. It consists of tiny iron oxide nanoparticles that can be targeted to specific areas of interest in the body. PEG-coated SPIO nanoparticles carrying Pik3cb shRNA (SPIO-shPik3cb) were prepared, and the particle size and zeta potential of PEG-coated SPIO nanoparticles with and without Pik3cb shRNA were examined. After a right common artery balloon-injured rat model was established, the rats were randomly divided into four groups, and the injured arteries were transfected with SPIO-shPik3cb, saline, SPIO-shcontrol and naked shRNA Pik3cb. During the treatment, each group was placed under a magnetic field and was transfected with the aid of ultrasound. Rats were sacrificed, and the tissue was harvested for analysis after 14 days. The results suggested that the mean particle size and zeta potential of SPIO-shPik3cbs were 151.45 ± 11 nm and 10 mV, respectively. SPIO-shPik3cb showed higher transfection efficiency and significantly inhibited the intimal thickening compared with naked Pik3cb shRNA in vascular smooth muscle cells (VSMCs) (*P < 0.05). Moreover, SPIO-shPik3cb could also significantly downregulate the expression of pAkt protein compared with naked Pik3cb shRNA. According to the results, SPIO-shPik3cb can remarkably inhibit the intimal thickening under a combination of magnetic field exposure and ultrasound.

The applications of Type B [anisotropic analytical algorithm (AAA) and collapsed cone (CC)] and Type C [Acuros XB (AXB) and photon Monte Carlo (PMC)] dose calculation algorithms in spine stereotactic body radiotherapy (SBRT) were evaluated. Water- and bone-equivalent phantoms were combined to evaluate the percentage depth dose and dose profile. Subsequently, 48 consecutive patients with clinical spine SBRT plans were evaluated. All treatment plans were created using AXB in Eclipse. The prescription dose was 24 Gy in two fractions at a 10 MV FFF on TrueBeam. The doses were then recalculated with AAA, CC and PMC while maintaining the AXB-calculated monitor units and beam arrangement. The dose index values obtained using the four dose calculation algorithms were then compared. The AXB and PMC dose distributions agreed with the bone-equivalent phantom measurements (within ±2.0%); the AAA and CC values were higher than those in the bone-equivalent phantom region. For the spine SBRT plans, PMC, AAA and CC were overestimated compared with AXB in terms of the near minimum and maximum doses of the target and organ at risk, respectively; the mean dose difference was within 4.2%, which is equivalent with within 1 Gy. The phantom study showed that the results from AXB and PMC agreed with the measurements within ±2.0%. However, the mean dose difference ranged from 0.5 to 1 Gy in the spine SBRT planning study when the dose calculation algorithms changed. Users should incorporate a clinical introduction that includes an awareness of these differences.

A nationwide multicenter cohort study on particle therapy was launched by the Japanese Society for Radiation Oncology in Japan in May 2016. We analyzed the outcome of proton beam therapy (PBT) for liver oligometastasis of esophagogastric cancers. Cases in which PBT was performed at all PBT facilities in Japan between May 2016 and February 2019 were enrolled. The patients were selected based on the following criteria: controlled primary cancer, liver recurrence without extrahepatic tumors and no more than three liver lesions. Twenty-two males and two females with a median age of 69 (range, 52-80) years and 35 lesions were included. This study included 6 patients with esophageal and 18 patients with gastric cancer. The median lesion size, fraction size and biological effective dose (BED)10 were 32 (7-104) mm, 3.8 gray (relative biological effect)/fractions (Gy (RBE)/fr) (2-8 Gy (RBE)/fr) and 96.9 (88.8-115.2) Gy, respectively. The median follow-up period was 18 (4-47) months. The 1-, 2- and 3-year overall survival (OS) rates were 75, 51.8 and 45.3%, respectively, and the median OS was 25.3 months. The 1-, 2- and 3-year cumulative local recurrence (LR) rates were 3, 6 and 6%, respectively. Patients' age (P < 0.01), performance status (P = 0.017) and tumor size (P = 0.024) were significant OS-related factors. No Grade 3 or higher adverse events (AEs) were observed. Owing to the low incidence of AEs and the low LR cumulative incidence, PBT is a feasible option for liver oligometastasis of esophagogastric cancers.

Radiotherapy (RT) has been the standard of care for treating a multitude of cancer types. Radiation-induced gastric injury (RIGI) is a common complication of RT for thoracic and abdominal tumors. It manifests acutely as radiation gastritis or gastric ulcers, and chronically as chronic atrophic gastritis or intestinal metaplasia. In recent years, studies have shown that intracellular signals such as oxidative stress response, p38/MAPK pathway and transforming growth factor-β signaling pathway are involved in the progression of RIGI. This review also summarized the risk factors, diagnosis and treatment of this disease. However, the root of therapeutic challenges lies in the incomplete understanding of the mechanisms. Here, we also highlight the potential mechanistic, diagnostic and therapeutic directions of RIGI.

To investigate radiation-induced cytopenia and establish predictive nomograms for hematological toxicity, we reviewed 3786 patients aged 18 or older who received radiation monotherapy between 2010 and 2021 for non-hematologic malignancies. We collected data on patient background, treatment content and hematologic toxicities for 12 weeks after the start of radiotherapy. The patients were randomly divided into training and test groups in 7:3 ratio. In the training group, we conducted ordered logistic regression analysis to identify predictive factors for neutropenia, lymphocytopenia, anemia and thrombocytopenia. Nomograms to predict Grade 2-4 cytopenia were generated and validated in the test group. Grade 3 or higher hematologic toxicities were observed in 9.7, 44.6, 8.3 and 3.1% of patients with neutropenia, lymphocytopenia, anemia and thrombocytopenia, respectively. We identified six factors for neutropenia grade, nine for lymphocytopenia grade and six for anemia grade with statistical significance. In the analysis of thrombocytopenia, the statistical model did not converge because of a small number of events. Nomograms were generated using factors with high predictive power. In evaluating the nomograms, we found high area under the receiver operating characteristic curve values (neutropenia; 0.75-0.85, lymphopenia; 0.89-0.91 and anemia; 0.85-0.86) in predicting Grade 2-4 cytopenia in the test group. We established predictive nomograms for neutropenia, leukocytopenia and anemia and demonstrated high reproducibility when validated in an independent cohort of patients.