Prostate cancer is the second most common cancer in men. Two common radiotherapy techniques, intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc radiotherapy (VMAT), are used for treatment. This study aimed to compare the two techniques for sparing the bladder and bowel. Computed tomography data from prostate cancer patients were analyzed to define the clinical target volume (CTV) and planning target volume (PTV). Treatment plans were generated with Monte Carlo algorithms, and dosimetric analysis was performed using the Monaco Treatment Planning System (TPS). We compared IMRT and VMAT for prostate cancer PTV coverage (% Ref. Volume), with VMAT showing slightly better coverage (98.885±1.704) compared to IMRT (98.594±0.923). VMAT also demonstrated improved PTV conformity. Additionally, VMAT was superior in sparing the bladder (% V4500<40%), while IMRT performed better in bowel preservation (mean Ref. volume CC<195).

BACKGROUND: While pre-operative radiation did not improve abdominal recurrence-free survival for retroperitoneal sarcoma (RPS) in the randomized STRASS trial, it did reduce rates of local recurrence. However, the risk of toxicity was substantial and the time to surgery was prolonged. A combination of hypofractionation and proton therapy may reduce delays from the initiation of radiation to surgery and limit the dose to surrounding organs at risk (OARs). We conducted a dosimetric comparison of the pre-operative ultra-hypofractionated intensity-modulated photon (IMRT) and proton radiotherapy (IMPT).
METHODS: Pre-operative IMRT and IMPT plans were generated on 10 RPS patients. The prescription was 25 Gy radiobiological equivalents (GyEs) (radiobiological effective dose of 1.1) to the clinical target volume and 30 GyEs to the margin at risk, all in five fractions. Comparisons were made using student T-tests.
RESULTS: The following endpoints were significantly lower with IMPT than with IMRT: mean doses to liver, bone, and all genitourinary and gastrointestinal OARs; bowel, kidney, and bone V5-V20; stomach V15; liver V5; maximum doses to stomach, spinal canal, and body; and whole-body integral dose.
CONCLUSIONS: IMPT maintained target coverage while significantly reducing the dose to adjacent OARs and integral dose compared to IMRT. A prospective trial treating RPS with pre-operative ultra-hypofractionated IMPT at our institution is currently being pursued.

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: To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches.
METHODS: Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers.
RESULTS: One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014).
CONCLUSIONS: Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.

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 analyse dosimetric and clinical predictors for acute and late gastrointestinal toxicity following chemoradiotherapy of anal cancer.
METHODS: Consecutive patients with locally advanced (T2 ≥4 cm - T4 or N+) anal cancer were selected from an institutional database (n = 114). All received intensity-modulated radiotherapy with concomitant 5-fluorouracil and mitomycin C. Gastrointestinal toxicity was retrospectively graded according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 and bowel cavity, small bowel and large bowel were contoured. Dosimetric and clinical variables were tested for associations with acute grade ≥3 gastrointestinal toxicity and late grade ≥2 gastrointestinal toxicity using the Mann-Whitney test, area under receiver operating characteristic curve (AUC) and logistic regression.
RESULTS: The median follow-up was 40 months. Acute grade ≥3 gastrointestinal toxicity was seen in 51 (44.7%) of the patients; late grade ≥2 gastrointestinal toxicity was seen in 36 of the patients (39.6% of 91 patients with >1 year recurrence-free follow-up). Bowel cavity V30Gy was the best dosimetric predictor for acute gastrointestinal toxicity (AUC 0.633; P = 0.02). Large bowel V20Gy was the best dosimetric predictor for late gastrointestinal toxicity (AUC 0.698; P = 0.001) but showed no association with acute gastrointestinal toxicity. In multivariate logistic regression, increasing age was significantly associated with acute gastrointestinal toxicity; smoking and large bowel V20Gy were significantly associated with late gastrointestinal toxicity. Patients who experienced acute grade ≥3 gastrointestinal toxicity were not at an increased risk of late grade ≥2 gastrointestinal toxicity (odds ratio 1.3; P = 0.55).
CONCLUSIONS: Factors of importance for acute and late gastrointestinal toxicity were not the same. Bowel cavity V30Gy is a good metric to use for the prediction of acute gastrointestinal toxicity, but the results of our study indicate that individual large and small bowel loops need to be contoured for better prediction of late gastrointestinal toxicity. The role of the large bowel as an important organ at risk for late gastrointestinal toxicity merits further research.

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.

UNASSIGNED: The aim is a dosimetric comparison of dynamic conformal arc integrated with the segment shape optimization and variable dose rate (DCA_SSO_VDR) versus VMAT for liver SBRT and interaction of various treatment plan quality indices with PTV and degree of modulation (DoM) for both techniques.
UNASSIGNED: Twenty-five patients of liver SBRT treated using the VMAT technique were selected. DCA_SSO_VDR treatment plans were also generated for all patients in Monaco TPS using the same objective constraint template and treatment planning parameters as used for the VMAT technique. For comparison purpose, organs at risk (OARs) doses and treatment plans quality indices, such as maximum dose of PTV (Dmax%), mean dose of PTV (Dmean%), maximum dose at 2 cm in any direction from the PTV (D2cm%), total monitor units (MU\'s), gradient index R50%, degree of modulation (DoM), conformity index (CI), homogeneity index (HI), and healthy tissue mean dose (HTMD), were compared.
UNASSIGNED: Significant dosimetric differences were observed in several OARs doses and lowered in VMAT plans. The D2cm%, R50%, CI, HI and HTMD are dosimetrically inferior in DCA_SSO_VDR plans. The higher DoM results in poor dose gradient and better dose gradient for DCA_SSO_VDR and VMAT treatment plans, respectively.
UNASSIGNED: For liver SBRT, DCA_SSO_VDR treatment plans are neither dosimetrically superior nor better alternative to the VMAT delivery technique. A reduction of 69.75% MU was observed in DCA_SSO_VDR treatment plans. For the large size of PTV and high DoM, DCA_SSO_VDR treatment plans result in poorer quality.