Evaluation of Unity 1.5 T MR-linac plan quality in patients with prostate cancer.

The Unity magnetic resonance (MR) linear accelerator (MRL) with MR-guided adaptive radiotherapy (MRgART) is capable of online MRgART where images are acquired on the treatment day and the radiation treatment plan is immediately replanned and performed. We evaluated the MRgART plan quality and plan reproducibility of the Unity MRL in patients with prostate cancer. There were five low- or moderate-risk and five high-risk patients who received 36.25 Gy or 40 Gy, respectively in five fractions. All patients underwent simulation magnetic resonance imaging (MRI) and five online adaptive MRI. We created plans for 5, 7, 9, 16, and 20 beams and for 60, 100, and 150 segments. We evaluated the target and organ doses for different number of beams and segments, respectively. Variation in dose constraint between the simulation plan and online adaptive plan was measured for each patient to assess plan reproducibility. The plan quality improved with the increasing number of beams. However, the proportion of significantly improved dose constraints decreased as the number of beams increased. For some dose parameters, there were statistically significant differences between 60 and 100 segments, and 100 and 150 segments. The plan of five beams exhibited limited reproducibility. The number of segments had minimal impact on plan reproducibility, but 60 segments sometimes failed to meet dose constraints for online adaptive plan. The optimization and delivery time increased with the number of beams and segments. We do not recommend using five or fewer beams for a reproducible and high-quality plan in the Unity MRL. In addition, many number of segments and beams may help meet dose constraints during online adaptive plan. Treatment with the Unity MRL should be performed with the appropriate number of beams and segments to achieve a good balance among plan quality, delivery time, and optimization time.

Risk Prediction Model for Crohn's Disease Based on Hematological Indicators.

BACKGROUND: The similarity between Crohn's disease (CD) and non-CD, especially with ulcerative colitis (UC) or intestinal tuberculosis (ITB), makes the diagnostic error rate not low. Therefore, there is an urgent need for an efficient, fast, and simple predictive model that can be applied in clinical practice. The purpose of this study is to establish the risk prediction model for CD based on five routine laboratory tests by logistic-regression algorithm, to construct the early warning model for CD and the corresponding visual nomograph, and to provide an accurate and convenient reference for the risk determination and differential diagnosis of CD, in order to assist clinicians to better manage CD and reduce patient suffering. METHODS: Using a retrospective analysis, a total of 310 cases were collected from 2020 to 2022 at The Sixth Affiliated Hospital, Sun Yat-sen University, who were diagnosed by comprehensive clinical diagnosis, including 100 patients with CD, 50 patients with ulcerative colitis (UC), 110 patients with non-inflammatory bowel disease (non-IBD) diseases (65 cases of intestinal tuberculosis, radioactive enterocolitis 39, and colonic diverticulitis 6), and 50 healthy individuals (NC) in the non-CD group. Risk prediction models were established by measuring ESR, Hb, WBC, ALb, and CH levels in hematology. The models were evaluated and visualized using logistic-regression algorithm. RESULTS: 1) ESR, WBC, and WBC/CH ratios in the CD group were higher than those in the non-CD group, while ALb, Hb, CH, WBC/ESR ratio, and Hb/WBC ratio were lower than those in the non-CD group, and the differences were statistically significant (all p < 0.05). 2) CD occurrence had a strong correlation with the WBC/CH ratio, with the correlation coefficient exceeding 0.4; CD occurrence was correlated with other indicators. 3) A risk prediction model containing age, gender, ESR, ALb, Hb, CH, WBC, WBC/CH, WBC/ESR, and Hb/WBC characteristics was constructed using a logistic-regression algorithm. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of the model were 83.0%, 76.2%, 59.0%, 90.5%, and 0.86, respectively. The model based on the corresponding index also had high diagnostic accuracy (AUC = 0.88) for differentiating CD from ITB. Visual nomograph based on the logistic-regression algorithm was also constructed for clinical application reference. CONCLUSIONS: In this study, a CD risk prediction model was established and visualized by five conventional hema-tological indices: ESR, Hb, WBC, ALb, and CH, in addition to a high diagnostic accuracy for the differential diagnosis of CD and ITB.

Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.

PURPOSE: Deep learning-based virtual patient-specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning-based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. METHODS: Overall, 96 volumetric-modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement-based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (r) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). RESULTS: At the 2%/2 mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32% ± 0.43% and 0.54 ± 0.03, 2.70% ± 0.26%, and 0.32 ± 0.08, and 2.96% ± 0.23% and 0.24 ± 0.22, respectively; at the 3%/3 mm criterion, these values were 1.25% ± 0.05% and 0.36 ± 0.18, 1.57% ± 0.35% and 0.19 ± 0.20, and 1.39% ± 0.32% and 0.17 ± 0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2 mm and 3%3 mm. CONCLUSIONS: These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models.

Increase in rainfall intensity promotes soil nematode diversity but offset by nitrogen addition in a temperate grassland.

Precipitation regime in arid and semi-arid regions is exhibiting a trend of increase in rainfall intensity but reduction in frequency under global climate change. In addition, nitrogen (N) deposition occurs simultaneously in the same regions. Nematodes are the dominant soil biota in terrestrial ecosystems and are involved in various underground processes. How the diversity of nematode communities responds to changing precipitation regime and how N deposition regulates the responses remain unclear. Here, we performed a field experiment initiated in 2012 to examine the effect of changes in the precipitation regime (2 mm precipitation intensity, 5 mm precipitation intensity, 10 mm precipitation intensity, 20 mm precipitation intensity, and 40 mm precipitation intensity) and N addition (10 g N m-2 yr-1) on soil nematode community in a semi-arid grassland in Inner Mongolia of China. We found that the abundance and diversity of nematodes increased under the treatments with fewer but stronger precipitation events (the largest abundance of total nematodes was 1458.37 individuals/100 g dry soil occurred under 40 mm intensity treatment). However, N addition reduced nematode diversity under these treatments, which largely offset the positive effects of increased rainfall intensity alone. Soil pH and plant belowground biomass were the main factors affecting nematode diversity. Our results imply that, as a consequence of global climate change, an increase in the intensity of rainfall events in the coming decades may favor the nematode communities within arid and semi-arid ecosystems. However, this positive effect may be largely offset by soil acidification in the regions experiencing heavy N deposition.