Objective: As a prevalent and infiltrative cancer type of the central nervous system, the prognosis of lower-grade glioma (LGG) in adults is highly heterogeneous. Recent evidence has demonstrated the prognostic value of the mRNA expression-based stemness index (mRNAsi) in LGG. Our aim was to develop a stemness index-based signature (SI-signature) for risk stratification and survival prediction. Methods: Differentially expressed genes (DEGs) between LGG in the Cancer Genome Atlas (TCGA) and normal brain tissue samples from the Genotype-Tissue Expression (GTEx) project were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the mRNAsi-related gene sets. Meanwhile, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for the functional annotation of the key genes. ESTIMATE was used to calculate tumor purity for acquiring the correct mRNAsi. Differences in overall survival (OS) between the high and low mRNAsi (corrected mRNAsi) groups were compared using the Kaplan Meier analysis. By combining the Lasso regression with univariate and multivariate Cox regression, the SI-signature was constructed and validated using the Chinese Glioma Genome Atlas (CGGA). Results: There was a significant difference in OS between the high and low mRNAsi groups, which was also observed in the two corrected mRNAsi groups. Based on threshold limits, 86 DEGs were most significantly associated with mRNAsi via WGCNA. Seven genes (ADAP2, ALOX5AP, APOBEC3C, FCGRT, GNG5, LRRC25, and SP100) were selected to establish a risk signature for primary LGG. The ROC curves showed a fair performance in survival prediction in both the TCGA and the CGGA validation cohorts. Univariate and multivariate Cox regression revealed that the risk group was an independent prognostic factor in primary LGG. The nomogram was developed based on clinical parameters integrated with the risk signature, and its accuracy for predicting 3- and 5-years survival was assessed by the concordance index, the area under the curve of the time-dependent receiver operating characteristics curve, and calibration curves. Conclusion: The SI-signature with seven genes could serve as an independent predictor, and suggests the importance of stemness features in risk stratification and survival prediction in primary LGG.

Purpose: We explored the effects of geometrical topological properties of tumors such as tumor length and “axial cross‐sectional area (ACSA)” of tumors (planning target volume [PTV] volume /PTV length) on the dosimetric parameters of organs at risk (lung and heart) in patients with esophagus cancer (EPC) treated by way of intensity‐modulated radiation therapy (IMRT), so as to provide a guideline for the dosimetric limitation for organs at risk in IMRT treatment.Methods: A retrospective analysis was done on 103 cases of patients with EPC who were treated by IMRT from November 2010 to August 2019, in which PTV‐G stood for the externally expanded planning target volume (PTV) of the gross tumor volume (GTV) and PTV‐C for the externally expanded volume of the clinical target volume (CTV). A linear regression model was employed to analyze the several pairs of correlation: the 1st one between the relative length of tumors (PTV length/lung length) and pulmonary dose‐volume parameters, the 2nd one between ACSA of tumors and pulmonary dose‐volume parameters, the 3rd one between PTV length and the dosimetric parameters of the heart, and the last one between ACSA of tumors and the dosimetric parameters of the heart.Results: (i) There was a strong positive correlation between the relative length of tumors (PTV length/lung length) and V₅ (p < 0.001, r = 0.73), and V₁₀ (p < 0.001, r = 0.66) of the lung. There was a moderate positive correlation between the relative length of tumors and V₃₀ (p < 0.001, r = 0.44) of the lung, and a weak positive correlation between the relative length of tumors and V₂₀ (p < 0.001, r = 0.39) of the lung. (ii) There was a strong positive correlation between ACSA of tumors (PTV volume/PTV length) and V₃₀ (p < 0.001, r = 0.67) of the lung, a moderate positive correlation between ACSA of tumors and V₂₀ (p 0.05) of the lung. (iii) There was a moderate positive correlation between PTV length and V₄₀ (p < 0.001, r = 0.58), and D_mean (p < 0.001, r = 0.52) of the heart, yet there was no obvious correlation between ACSA of tumors and D_mean and V₄₀ of the heart (p > 0.05).Conclusions: (i) Compared with the high‐dose region of the lung, the relative length of tumors (PTV length/lung length) has a greater impact on the low‐dose region of the lung. The linear regression equation of scatter plot showed that when the relative length of tumors increased by 0.1, the lung dose‐volume parameters of V₅, V₁₀, V₂₀, and V₃₀ increased by approximately 5.37%, 3.59%, 1.05%, and 1.08%, respectively. When PTV length increased by 1 cm, D_mean and V₄₀ of the heart increased by approximately 153.6 cGy and 2.03%, respectively. (ii) Compared with the low‐dose region of the lung, the value of ACSA of tumors (PTV volume/PTV length) has a greater impact on the high‐dose region of the lung. However, the value of ACSA of tumors has no significant effect on the dosimetric parameters of the heart (D_mean and V₄₀). The linear regression equation of scatter plot showed that when ACSA of tumors increased by 10 cm², the lung dose‐volume parameters of V₁₀, V_20, and V₃₀ increased by approximately 3.11%, 3.37%, and 4.01%, respectively.

Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that can exist between multimodal images. The purpose of this study is to improve the registration accuracy between CT and MR for nasopharyngeal carcinoma cases. 269 cases were enrolled in the study, and 188 cases were designated for training, while a separate set of 81 cases was reserved for testing. Each case had a CT volume and a T1-MR volume. The treatment table was removed from their CT images. The CycleFCNs model was used for deformable registration, and two strategies including adaptive mask registration strategy and weight allocation strategy were adopted for training. Dice similarity coefficient, Hausdorff distance, precision, and recall were calculated for normal tissues of CT-MR image pairs, before and after the registration. Three deformable registration methods including RayStation, Elastix, and VoxelMorph were compared with the proposed method. The registration results of RayStation and Elastix are essentially consistent. Upon employing the VoxelMorph model and the proposed method for registration, a clear trend of increased dice similarity coefficient and decreased hausdorff distance can be observed. It is noteworthy that for the temporomandibular joint, pituitary, optic nerve, and optic chiasma, the proposed method has improved the average dice similarity coefficient from 0.86 to 0.91, 0.87 to 0.93, 0.85 to 0.89, and 0.77 to 0.83, respectively, as compared to RayStation. Additionally, within the same anatomical structures, the average hausdorff distance has been decreased from 2.98 mm to 2.28 mm, 1.83 mm to 1.53 mm, 3.74 mm to 3.56 mm, and 5.94 mm to 5.87 mm. Compared to the original CycleFCNs model, the improved model has significantly enhanced the dice similarity coefficient of the brainstem, pituitary gland, and optic nerve (P < 0.001). The proposed method significantly improved the registration accuracy for multi-modal medical images in NPC cases. These findings have important clinical implications, as increased registration accuracy can lead to more precise tumor segmentation, optimized treatment planning, and ultimately, improved patient outcomes.

Background: This study investigates the dosimetric response of the Delta4 system to varying field sizes and assesses its accuracy in dose verification. Materials and Methods: Output factors for fields ranging from 1 × 1 cm² to 20 × 20 cm² were measured using PTW 60019 detector, PTW 60018 detector, and the Delta4 ...

Background: Styrofoam is a patient‐specific immobilization device in radiotherapy; most previous studies about the impact of styrofoam on setup errors have only analyzed a single tumor type, and have not considered the influence of patient's physical condition on the setup errors of styrofoam. Purpose: This study aims to evaluate the impact of styrofoam device on setup errors in radiotherapy and explore which patient population is more suitable for styrofoam immobilization. Methods: Univariate and multivariate analyses were conducted to compare the setup errors between the experimental group (styrofoam combined with thermoplastic mask) and the control group (thermoplastic mask alone). All cases were categorized based on tumor location into head and neck, thorax, abdomen, and limb cases, with age, gender, surgical history, educational level, and body mass index (BMI) serving as variables in the multivariate analysis. Intragroup analysis was also performed. Results: For all included cases, the experimental group had median setup errors of 1.20 , 2.00 , and 1.30 mm in the vertical, longitudinal, and lateral directions, respectively. In contrast, the control group had median setup errors of 1.50 , 2.00 , and 1.85 mm in the same respective directions. Notably, the experimental group demonstrated statistically significant reductions in average setup errors in the longitudinal direction (2.00 vs. 2.87 mm, p  < 0.01) and lateral direction (1.90 vs. 2.24 mm, p < 0.01) compared to the control group. The intragroup analysis results indicated that factors such as age, gender, surgical history, and educational level had no significant impact on the setup errors in the experimental group. Conclusion: The styrofoam fixation device for patient immobilization can effectively reduce setup errors in both the longitudinal and lateral directions, and the styrofoam fixation device is suitable for most people.