UNASSIGNED: Radiotherapy is one of the most important treatments for high-grade glioma (HGG), but the best way to delineate the target areas for radiotherapy remains controversial, so our aim was to compare the dosimetric differences in radiation treatment plans generated based on the European Organization for Research and Treatment of Cancer (EORTC) and National Research Group (NRG) consensus to provide evidence for optimal target delineation for HGG.
UNASSIGNED: We prospectively enrolled 13 patients with a confirmed HGG from our hospital and assessed dosimetric differences in radiotherapy treatment plans generated according to the EORTC and NRG-2019 guidelines. For each patient, two treatment plans were generated. Dosimetric parameters were compared by dose-volume histograms for each plan.
UNASSIGNED: The median volume for planning target volume (PTV) of EORTC plans, PTV1 of NRG-2019 plans, and PTV2 of NRG-2019 plans were 336.6 cm3 (range, 161.1-511.5 cm3), 365.3 cm3 (range, 123.4-535.0 cm3), and 263.2 cm3 (range, 116.8-497.7 cm3), respectively. Both treatment plans were found to have similar efficiency and evaluated as acceptable for patient treatment. Both treatment plans showed well conformal index and homogeneity index and were not statistically significantly different (P = 0.397 and P = 0.427, respectively). There was no significant difference in the volume percent of brain irradiated to 30, 46, and 60 Gy according to different target delineations (P = 0.397, P = 0.590, and P = 0.739, respectively). These two plans also showed no significant differences in the doses to the brain stem, optic chiasm, left and right optic nerves, left and right lens, left and right eyes, pituitary, and left and right temporal lobes (P = 0.858, P = 0.858, P = 0.701 and P = 0.794, P = 0.701 and P = 0.427, P = 0.489 and P = 0.898, P = 0.626, and P = 0.942 and P = 0.161, respectively).
UNASSIGNED: The NRG-2019 project did not increase the dose of organs at risk (OARs) radiation. This is a significant finding that further lays the groundwork for the application of the NRG-2019 consensus in the treatment of patients with HGGs.
UNASSIGNED: The effect of radiotherapy target area and glial fibrillary acidic protein (GFAP) on the prognosis of high-grade glioma and its mechanism, number ChiCTR2100046667. Registered 26 May 2021.

UNASSIGNED: This study aimed to investigate the ability of enhanced computed tomography (CT)-based radiomics and dosimetric parameters in predicting response to radiotherapy for esophageal cancer.
UNASSIGNED: A retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts.
UNASSIGNED: Univariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69-0.87) and 0.79 (95% CI, 0.65-0.93) in the training and validation groups, respectively.
UNASSIGNED: The combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.

OBJECTIVE: Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters.
METHODS: The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve.
RESULTS: The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility.
CONCLUSIONS: The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.

BACKGROUND: Concurrent chemo-radiotherapy in patients with locally advanced cervical cancer has significant hematologic toxicities (HT), leading to treatment disruption and affecting patient prognosis. We performed the meta-analysis to assess the clinical benefit of pelvic (active) bone marrow (BM) sparing radiotherapy.
METHODS: A systematic methodological search of six primary electronic databases was performed. This systematic review mainly assessed the differences in pelvic (active) BM dose-volume parameters (DVP), hematologic toxicity of pelvic (active) BM sparing versus non-sparing radiotherapy plans. The secondary objective was to explore optimal dose limitation regimens and evaluate other radiation-induced toxicities (gastrointestinal and urological toxicity (GT/UT)). Random-effects models were used for meta-analysis.
RESULTS: Final 65 publications that met inclusion criteria were included in the meta-analysis and descriptive tables. Meta-analysis of mean pelvic BM-DVP differences showed that pelvic BM-V10,20,40,50 (Vx: volume of BM receiving ≥ X Gy) were reduced by -4.6% [95% CI: -6.6, -2.6], -10.9% [-13.2, -8.6], -7.3% [-9.5, -5.2] and -3.4% [-4.3, -2.4] in pelvic BM-sparing plans. Pelvic BM sparing radiotherapy decreased G2/3+ HT [odds ratio (OR) 0.31, (0.23, 0.41)/0.42, (0.28, 0.63)], without increasing GT [G2/3+: OR 0.76, (0.51, 1.14)/0.90, (0.47, 1.74)] and UT [G2/3+: OR 0.91, (0.57, 1.46)/0.54, (0.25, 1.17)]. Pelvic active BM sparing radiotherapy also reduced HT [G2/3+ HT: OR 0.42, (0.23, 0.77)/0.34, (0.16, 0.72)]. There were significant variations between publications in dose restriction regimens.
CONCLUSIONS: The pelvic BM protection radiotherapy can decrease BM dose and HT. Moreover, it does not increase GT and UT. The clinical benefit of pelvic active BM protection needs to be further validated in randomized controlled trials.

OBJECTIVE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.
METHODS: Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman\'s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.
RESULTS: FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.
CONCLUSIONS: Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.

Silica is an independent risk factor for lung cancer in addition to smoking. Chronic silicosis is one of the most common and serious occupational diseases associated with poor prognosis. However, the role of radiotherapy is unclear in patients with chronic silicosis. We conducted a retrospective study to evaluate efficacy and safety in lung cancer patients with chronic silicosis, especially focusing on the incidence of radiation pneumonitis (RP). Lung cancer patients with chronic silicosis who had been treated with radiotherapy from 2005 to 2018 in our hospital were enrolled in this retrospective study. RP was graded according to the National Cancer Institute\'s Common Terminology Criteria for Adverse Events (CTCAE), version 3.0. Of the 22 patients, ten (45.5%) developed RP ≥2. Two RP-related deaths (9.1%) occurred within 3 months after radiotherapy. Dosimetric factors V5, V10, V15, V20 and mean lung dose (MLD) were significantly higher in patients who had RP >2 (P < 0.05). The median overall survival times in patients with RP ≤2 and RP>2 were 11.5 months and 7.1 months, respectively. Radiotherapy is associated with excessive and fatal pulmonary toxicity in lung cancer patients with chronic silicosis.