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Background: Postoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process. Objective: To evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions. Methods: We previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV). Results: AI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P<0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency. Conclusion: AI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer. [ABSTRACT FROM AUTHOR]

Abstract Background Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. Purpose The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. Methods This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. Results Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. Conclusion The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Background: Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. Purpose: The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. Methods: This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. Results: Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. Conclusion: The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis. [ABSTRACT FROM AUTHOR]

BackgroundPostoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process.ObjectiveTo evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions.MethodsWe previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV).ResultsAI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P

In this study, we propose a 3D dense network with embedded capsule modules (3D-DUCaps) for automatically delineating the clinical target volume of esophageal cancer, addressing the spatial dependence issue between parts and the whole that cannot be effectively captured by 2D networks. The network integrates capsule modules into the encoding layers of the U-Net to enhance feature learning capabilities and preserve more information, enabling the inference of poses and learning the relationship between parts and the whole. Additionally, dense connections are introduced to further promote the fusion of high-level semantic information and low-level feature information, enhancing the network's information propagation capabilities. Compared with traditional 2D deep learning networks, the proposed 3D deep learning network demonstrates stronger spatial awareness and superior boundary delineation capabilities, resulting in the more precise delineation of the clinical target volume of esophageal cancer. Experimental results indicate that the 3D-DUCaps network achieves a 2.4% improvement in the Dice Similarity Coefficient metric compared with the classical 3D-UNet network. [ABSTRACT FROM AUTHOR]

The European Society for Radiotherapy and Oncology-Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) updated a new target volume delineation guideline for postmastectomy radiotherapy (PMRT) after implant-based reconstruction. This study aimed to evaluate the impact on breast complications with the new guideline compared to the conventional guidelines. In total, 308 patients who underwent PMRT after tissue expander or permanent implant insertion from 2016 to 2021 were included; 184 received PMRT by the new ESTRO-ACROP target delineation (ESTRO-T), and 124 by conventional target delineation (CONV-T). The endpoints were major breast complications (infection, necrosis, dehiscence, capsular contracture, animation deformity, and rupture) requiring re-operation or re-hospitalization and any grade ≥2 breast complications. With a median follow-up of 36.4 months, the cumulative incidence rates of major breast complications at 1, 2, and 3 years were 6.6%, 10.3%, and 12.6% in the ESTRO-T group, and 9.7%, 15.4%, and 16.3% in the CONV-T group; it did not show a significant difference between the groups (p = 0.56). In multivariable analyses, target delineation is not associated with the major complications (sHR = 0.87; p = 0.77). There was no significant difference in any breast complications (3-year incidence, 18.9% vs. 23.3%, respectively; p = 0.56). Symptomatic RT-induced pneumonitis was developed in six (3.2%) and three (2.4%) patients, respectively. One local recurrence occurred in the ESTRO-T group, which was within the ESTRO-target volume. The new ESTRO-ACROP target volume guideline did not demonstrate significant differences in major or any breast complications, although it showed a tendency of reduced complication risks. As the dosimetric benefits of normal organs and comparable oncologic outcomes have been reported, further analyses with long-term follow-up are necessary to evaluate whether it could be connected to better clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Part of the current stereotactic arrythmia radioablation (STAR) workflow is transfer of findings from the electroanatomic mapping (EAM) to computed tomography (CT). Here, we analyzed inter- and intraobserver variation in a modified EAM-CT registration using automatic registration algorithms designed to yield higher robustness. Materials and methods: This work is based on data of 10 patients who had previously undergone STAR. Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substatial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. EAM-CT merge consists of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, clinical target volume (CTV) delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. Results: Intraobserver variability: The mean DSC and MHD for 3 CT LVs altogether was 0.92 ± 0.01 and 1.49 ± 0.23 mm. The mean DSC and MHD for 3 CTVs altogether was 0,82 ± 0,06 and 0,71 ± 0,22 mm. Interobserver variability: Segmented CT LVs showed great similarity (mean DSC of 0,91 ± 0,01, MHD of 1,86 ± 0,47 mm). The mean DSC comparing CTVs from both observers was 0,81 ± 0,11 and MHD was 0,87 ± 0,45 mm. Conclusions: The high interobserver similarity of segmented LVs and delineated CTVs confirmed the robustness of the proposed method. Even an inexperienced user can perform a precise EAM-CT merge following workflow instructions. [ABSTRACT FROM AUTHOR]

Abstract Background Previous researches have demonstrated that adaptive replanning during intensity-modulated radiation therapy (IMRT) could enhance the prognosis of patients with nasopharyngeal carcinoma (NPC). However, the delineation of replanning target volumes remains unclear. This study aimed to evaluate the feasibility of reducing target volumes through adaptive replanning during IMRT by analyzing long-term survival outcomes and failure patterns of locoregional recurrence in NPC. Methods This study enrolled consecutive NPC patients who received IMRT at our hospital between August 2011 and April 2018. Patients with initially diagnosed, histologically verified, non-metastatic nasopharyngeal cancer were eligible for participation in this study. The location and extent of locoregional recurrences were transferred to pretreatment planning computed tomography for dosimetry analysis. Results Among 274 patients, 100 (36.5%) received IMRT without replanning and 174 (63.5%) received IMRT with replanning. Five-year rates of locoregional recurrence-free survival (LRFS) were 90.1% (95%CI, 84.8% to 95.4%) and 80.8% (95%CI, 72.0% to 89.6%) for patients with and without replanning, P = 0.045. There were 17 locoregional recurrences in 15 patients among patients with replanning, of which 1 (5.9%) was out-field and 16 (94.1%) were in-field. Among patients without replanning, 19 patients developed locoregional recurrences, of which 1 (5.3%) was out-field, 2 (10.5%) were marginal, and 16 (84.2%) were in-field. Conclusions In-field failure inside the high dose area was the most common locoregional recurrent pattern for non-metastatic NPC. Adapting the target volumes and modifying the radiation dose prescribed to the area of tumor reduction during IMRT was feasible and would not cause additional recurrence in the shrunken area.

Accurate target delineation (ie, contouring) is essential for radiation treatment planning and radiotherapy efficacy. As a result, improving the quality of target delineation is an important goal in the education of radiation oncology residents. The purpose of this study was to track the concordance of radiation oncology residents' contours with those of faculty physicians over the course of 1 year to assess for patterns. Residents in postgraduate year (PGY) levels 2 to 4 were asked to contour target volumes that were then compared to the finalized, faculty physician–approved contours. Concordance between resident and faculty physician contours was determined by calculating the Jaccard concordance index (JCI), ranging from 0, meaning no agreement, to 1, meaning complete agreement. Multivariate mixed-effect models were used to assess the association of JCI to the fixed effect of PGY level and its interactions with cancer type and other baseline characteristics. Post hoc means of JCI were compared between PGY levels after accounting for multiple comparisons using Tukey's method. In total, 958 structures from 314 patients collected during the 2020-2021 academic year were studied. The mean JCI was 0.77, 0.75, and 0.61 for the PGY-4, PGY-3, and PGY-2 levels, respectively. The JCI score for PGY-2 was found to be lower than those for PGY-3 and PGY-4, respectively (all P <.001). No statistically significant difference of JCI score was found between the PGY-3 and PGY-4 levels. The average JCI score was lowest (0.51) for primary head and/or neck cancers, and it was highest (0.80) for gynecologic cancers. Tracking and comparing the concordance of resident contours with faculty physician contours is an intriguing method of assessing resident performance in contouring and target delineation and could potentially serve as a quantitative metric, which is lacking currently, in radiation oncology resident evaluation. However, additional study is necessary before this technique can be incorporated into residency assessments. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro‐anatomical mapping (EAM). Purpose: In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro‐anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineation trials. Methods: The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice‐by‐slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, were evaluated. Results: All cardiologists reported being more confident and efficient using the proposed method. The inter‐clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were also improved. Conclusions: A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter‐clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to the experiments. [ABSTRACT FROM AUTHOR]

Background: Previous researches have demonstrated that adaptive replanning during intensity-modulated radiation therapy (IMRT) could enhance the prognosis of patients with nasopharyngeal carcinoma (NPC). However, the delineation of replanning target volumes remains unclear. This study aimed to evaluate the feasibility of reducing target volumes through adaptive replanning during IMRT by analyzing long-term survival outcomes and failure patterns of locoregional recurrence in NPC. Methods: This study enrolled consecutive NPC patients who received IMRT at our hospital between August 2011 and April 2018. Patients with initially diagnosed, histologically verified, non-metastatic nasopharyngeal cancer were eligible for participation in this study. The location and extent of locoregional recurrences were transferred to pretreatment planning computed tomography for dosimetry analysis. Results: Among 274 patients, 100 (36.5%) received IMRT without replanning and 174 (63.5%) received IMRT with replanning. Five-year rates of locoregional recurrence-free survival (LRFS) were 90.1% (95%CI, 84.8% to 95.4%) and 80.8% (95%CI, 72.0% to 89.6%) for patients with and without replanning, P = 0.045. There were 17 locoregional recurrences in 15 patients among patients with replanning, of which 1 (5.9%) was out-field and 16 (94.1%) were in-field. Among patients without replanning, 19 patients developed locoregional recurrences, of which 1 (5.3%) was out-field, 2 (10.5%) were marginal, and 16 (84.2%) were in-field. Conclusions: In-field failure inside the high dose area was the most common locoregional recurrent pattern for non-metastatic NPC. Adapting the target volumes and modifying the radiation dose prescribed to the area of tumor reduction during IMRT was feasible and would not cause additional recurrence in the shrunken area. [ABSTRACT FROM AUTHOR]

Ultra-high dose rate radiotherapy (FLASH-RT) is an external beam radiotherapy strategy that uses an extremely high dose rate (≥40 Gy/s). Compared with conventional dose rate radiotherapy (≤0.1 Gy/s), the main advantage of FLASH-RT is that it can reduce damage of organs at risk surrounding the cancer and retain the anti-tumor effect. An important feature of FLASH-RT is that an extremely high dose rate leads to an extremely short treatment time; therefore, in clinical applications, the steps of radiotherapy may need to be adjusted. In this review, we discuss the selection of indications, simulations, target delineation, selection of radiotherapy technologies, and treatment plan evaluation for FLASH-RT to provide a theoretical basis for future research. [ABSTRACT FROM AUTHOR]

Abstract Background In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images. Methods MRI images from 200 patients were collected for training-validation and testing set. Three popular deep learning models (FCN, U-Net, Deeplabv3) are proposed to automatically delineate GTVnx. FCN was the first and simplest fully convolutional model. U-Net was proposed specifically for medical image segmentation. In Deeplabv3, the proposed Atrous Spatial Pyramid Pooling (ASPP) block, and fully connected Conditional Random Field(CRF) may improve the detection of the small scattered distributed tumor parts due to its different scale of spatial pyramid layers. The three models are compared under same fair criteria, except the learning rate set for the U-Net. Two widely applied evaluation standards, mIoU and mPA, are employed for the detection result evaluation. Results The extensive experiments show that the results of FCN and Deeplabv3 are promising as the benchmark of automatic nasopharyngeal cancer detection. Deeplabv3 performs best with the detection of mIoU 0.8529 ± 0.0017 and mPA 0.9103 ± 0.0039. FCN performs slightly worse in term of detection accuracy. However, both consume similar GPU memory and training time. U-Net performs obviously worst in both detection accuracy and memory consumption. Thus U-Net is not suggested for automatic GTVnx delineation. Conclusions The proposed framework for automatic target delineation of GTVnx in nasopharynx bring us the desirable and promising results, which could not only be labor-saving, but also make the contour evaluation more objective. This preliminary results provide us with clear directions for further study.

Purpose: Target delineation for radiation therapy is a time‐consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post‐operative target delineation, particularly for CT‐based studies. To this end, we trained a CT‐based autocontouring model to contour the post‐operative GTV of pediatric patients with medulloblastoma. Methods: One hundred four retrospective pediatric CT scans were used to train a GTV auto‐contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25–50%, 3 = > 50–75%, and 4 = > 75–100%). Contrast and the contrast‐to‐noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre‐selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). Results: Eighty patients were pre‐selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre‐selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre‐selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13. Conclusion: We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT‐Based auto‐contouring algorithms have potential to positively impact centers with limited MRI access. [ABSTRACT FROM AUTHOR]

Purpose: 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. Methods: 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. Results: 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). Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images. Methods: MRI images from 200 patients were collected for training-validation and testing set. Three popular deep learning models (FCN, U-Net, Deeplabv3) are proposed to automatically delineate GTVnx. FCN was the first and simplest fully convolutional model. U-Net was proposed specifically for medical image segmentation. In Deeplabv3, the proposed Atrous Spatial Pyramid Pooling (ASPP) block, and fully connected Conditional Random Field(CRF) may improve the detection of the small scattered distributed tumor parts due to its different scale of spatial pyramid layers. The three models are compared under same fair criteria, except the learning rate set for the U-Net. Two widely applied evaluation standards, mIoU and mPA, are employed for the detection result evaluation. Results: The extensive experiments show that the results of FCN and Deeplabv3 are promising as the benchmark of automatic nasopharyngeal cancer detection. Deeplabv3 performs best with the detection of mIoU 0.8529 ± 0.0017 and mPA 0.9103 ± 0.0039. FCN performs slightly worse in term of detection accuracy. However, both consume similar GPU memory and training time. U-Net performs obviously worst in both detection accuracy and memory consumption. Thus U-Net is not suggested for automatic GTVnx delineation. Conclusions: The proposed framework for automatic target delineation of GTVnx in nasopharynx bring us the desirable and promising results, which could not only be labor-saving, but also make the contour evaluation more objective. This preliminary results provide us with clear directions for further study. Key points: • The precise delineation of nasopharynx gross tumor volume (GTVnx) is a critical step for nasopharyngeal cancer radiotherapy. • Three deep learning models were trained to automatically delineate GTVnx. • Automatic detection and recognition of GTVnx based on those models revealed desirable results and brought a positive impact on improving delineation accuracy and reducing workload. [ABSTRACT FROM AUTHOR]

Background: To investigate the differences between physicians in target delineation in intensity-modulated radiation therapy for nasopharyngeal carcinoma as well as their impact on target dose coverage. Methods: Ninety-nine in-hospital patients were randomly selected for retrospective analysis, and the target volumes were delineated by 2 physicians. The target volumes were integrated with the original plans, and the differential parameters, including the Dice similarity coefficient (DSC), Hausdorff distance (HD), and Jaccard similarity coefficient (JSC) were recorded. The dose–volume parameters to evaluate target dose coverage were analyzed by superimposing the same original plan to the 2 sets of images on which the target volumes were contoured by the 2 physicians. The significance of differences in target volumes and dose coverage were evaluated using statistical analysis. Results: The target dose coverage for different sets of target volumes showed statistically significant differences, while the similarity metrics to evaluate geometric target volume differences did not. More specifically, for PGTVnx, the median DSC, JSC, and HD were 0.85, 0.74, and 11.73, respectively; for PCTV1, the median values were 0.87, 0.77, and 11.78, respectively; for PCTV2, the median values were 0.90, 0.82, and 16.12, respectively. For patients in stages T3-4, DSC, and JSC were reduced but HD was increased compared to those in stages T1-2. Dosimetric analysis indicated that, for the target volumes, significant differences between the 2 physicians were found in D95, D99, and V100 for all the target volumes (ie, PGTVnx, PCTV1, and PCTV2) across the whole group of patients, as well as in patients with disease stages T3-4 and T1-2. Conclusions: The target volumes delineated by the 2 physicians had a high similarity, but the maximal distances between the outer contours of the 2 sets were significantly different. In patients with advanced T stages, significant differences in dose distributions were found, stemming from the deviations of target delineation. [ABSTRACT FROM AUTHOR]

Using multigeophysical exploration techniques is a common way for deep targets to be explored in complex survey areas. How to locate an unknown underground target using multiple datasets is a great challenge. The useful information in the multisource geophysical model can be extracted and fused with the help of data fusion, which also works well to correct the interpretation divergence brought on by expert experience, with image feature extraction being the key step in the fusion of the geophysical models. Traditionally, this method is often used for these kinds of geophysical images, but it significantly reduces the efficiency of feature extraction. As a result, we propose a novel method based on a transfer learning method to extract the features of multisource images. First, the ResNet50 network is used to extract the initial features of the images. Owing to the problems of feature redundancy and fuzzy features in initial features, Spearman and zero phase component analysis can be used to achieve feature reduction and enhancement, which can further improve the computational efficiency and fusion accuracy in fusion. Finally, the fusion image is obtained using fusion rules that we designed based on the current state. The algorithm's reliability is tested using field data from the Iliamna Volcano. The case study demonstrates the effectiveness of the proposed strategy, which also offers a novel way to locate subsurface targets. [ABSTRACT FROM AUTHOR]

Background: During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC. Material/methods: 32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD). Results: 32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08–0.39 cm) and 0.07 cm (range 0.04–0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p

Aims: The introduction of on-line magnetic resonance image-guided radiotherapy (MRIgRT) has led to an improvement in the therapeutic workflow of radiotherapy treatments thanks to the better visualization of therapy volumes assured by the higher soft tissue contrast. Magnetic Resonance contrast agents (MRCA) could improve the target delineation in on-line MRIgRT planning as well as reduce inter-observer variability and enable innovative treatment optimization protocols. The aim of this survey is to investigate the utilization of MRCA among centres that clinically implemented on-line MRIgRT technology. Methods: In September 2021, we conducted an online survey consisting of a sixteen-question questionnaire that was distributed to the all the hospitals around the world equipped with MR Linacs. The questionnaire was developed by two Italian 0.35 T and 1.5 T MR-Linac centres and was validated by four other collaborating centres, using a Delphi consensus methodology. Results: The survey was distributed to 52 centres and 43 centres completed it (82.7%). Among these centres, 23 institutions (53.5%) used the 0.35T MR-Linac system, while the remaining 20 (46.5%) used the 1.5T MR-Linac system.According to results obtained, 25 (58%) of the centres implemented the use of MRCA for on-line MRIgRT. Gadoxetate (Eovist®; Primovist®) was reported to be the most used MRCA (80%) and liver the most common site of application (58%). Over 70% of responders agreed/strongly agreed to the need for international guidelines. Conclusions: The use of MRCA in clinical practice presents several pitfalls and future research will be necessary to understand the actual advantage derived from the use of MRCA in clinical practice, their toxicity profiles and better define the need of formulating guidelines for standardising the use of MRCA in MRIgRT workflow.

PET is a versatile imaging modality widely used in oncology for diagnosing, staging, predicting outcomes, and surveillance for a variety of cancers. In radiation oncology, combining PET and computed tomography imaging can markedly enhance treatment planning through improved target volume delineation. This review examines data and clinical approaches across 3 major cancer types to evaluate the role of PET in target volume delineation, with data and current approaches for thoracic, genitourinary, and head and neck malignancies detailed. Additionally, it emphasizes various practical applications of PET in radiation therapy planning, several of which have been recently demonstrated in clinical trials., Competing Interests: Disclosure This research was funded, in part, through the NIH, United States/NCI Cancer Center Support Grant P30 CA008748., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Simple Summary: Nasopharyngeal carcinoma (NPC) is commonly treated using high-dose radiotherapy. Careful radiotherapy planning is crucial for the eradication of cancer cells while avoiding injuries to normal structures. This balance is often delicate given the complex anatomic location in which NPC is situated. This article highlights the considerations, practical pearls, and recent advances in the precise delivery of radiotherapy in NPC patients. Radiotherapy is the primary treatment modality for nasopharyngeal carcinoma (NPC). Successful curative treatment requires optimal radiotherapy planning and precise beam delivery that maximizes locoregional control while minimizing treatment-related side effects. In this article, we highlight considerations in target delineation, radiation dose, and the adoption of technological advances with the aim of optimizing the benefits of radiotherapy in NPC patients. [ABSTRACT FROM AUTHOR]

Background and Purpose: Magnetic resonance (MR) guided radiotherapy utilizes MR images for (online) plan adaptation and image guidance. The aim of this study was to investigate the impact of variation in MR acquisition time and scan resolution on image quality, interobserver variation in contouring and interobserver variation in registration. Materials and Methods: Nine patients with prostate cancer were included. Four T2-weighted 3D turbo spin echo (T2w 3D TSE) sequences were acquired with different acquisition times and resolutions. Two radiologists assessed image quality, conspicuity of the capsule, peripheral zone and central gland architecture and motion artefacts on a 5 point scale. Images were delineated by two radiation oncologists and interobserver variation was assessed by the 95% Hausdorff distance. Seven observers registered the MR images on the planning CT. Registrations were compared on systematic offset and interobserver variation. Results: Acquisition times ranged between 1.3 and 6.3 min. Overall image quality and capsule definition were significantly worse for the MR sequence with an acquisition time of 1.3 min compared to the other sequences. Median 95% Hausdorff distance showed no significant differences in interobserver variation of contouring. Systematic offset and interobserver variation in registration were small (

• Multi-institutional deep learning segmentation models for breast cancer lymph nodes. • Models were trained on a consensus cohort following ESTRO delineation guidelines. • Models were tested against interobserver variation in a gold standard dataset. • Models were qualitatively evaluated by blinded delineation experts. • The models performed on par with expert delineators. This study aimed at training and validating a multi-institutional deep learning (DL) auto segmentation model for nodal clinical target volume (CTVn) in high-risk breast cancer (BC) patients with both training and validation dataset created with multi-institutional participation, with the overall aim of national clinical implementation in Denmark. A gold standard (GS) dataset and a high-quality training dataset were created by 21 BC delineation experts from all radiotherapy centres in Denmark. The delineations were created according to ESTRO consensus delineation guidelines. Four models were trained: One per laterality and extension of CTVn internal mammary nodes. The DL models were tested quantitatively in their own test-set and in relation to interobserver variation (IOV) in the GS dataset with geometrical metrics, such as the Dice Similarity Coefficient (DSC). A blinded qualitative evaluation was conducted with a national board, presented to both DL and manual delineations. A median DSC > 0.7 was found for all, except the CTVn interpectoral node in one of the models. In the qualitative evaluation 'no corrections needed' were acquired for 297 (36 %) in the DL structures and 286 (34 %) for manual delineations. A higher rate of 'major corrections' and 'easier to start from scratch' was found in the manual delineations. The models performed within the IOV of an expert group, with two exceptions. DL models were developed on a national consensus cohort and performed on par with the IOV between BC experts and had a comparable or higher clinical acceptance than expert manual delineations. [ABSTRACT FROM AUTHOR]

Abstract Despite aggressive management consisting of maximal safe surgical resection followed by external beam radiation therapy (60 Gy/30 fractions) with concomitant and adjuvant temozolomide, approximately 90% of WHO grade IV gliomas (glioblastomas, GBM) will recur locally within 2 years. For patients with recurrent GBM, no standard of care exists. Thanks to the continuous improvement in radiation science and technology, reirradiation has emerged as feasible approach for patients with brain tumors. Using stereotactic radiosurgery (SRS) or stereotactic radiotherapy (SRT), either hypofractionated or conventionally fractionated schedules, several studies have suggested survival benefits following reirradiation of patients with recurrent GBM; however, there are still questions to be answered about the efficacy and toxicity associated with a second course of radiation. We provide a clinical overview on current status and recent advances in reirradiation of GBM, addressing relevant clinical questions such as the appropriate patient selection and radiation technique, optimal dose fractionation, reirradiation tolerance of the brain and the risk of radiation necrosis.

Objectives: Aim to analyze the effect of radiotherapy for cervical lymph node metastatic carcinoma with unknown primary (CCUP) and compare the survival benefits between Comprehensive radiotherapy and Involved Field radiotherapy. Materials and methods: The patients diagnosed with CCUP between 2009 and 2019 in our institution were analyzed retrospectively. The categorical variables were tested by χ2 test. Kaplan–Meier method was used for survival analysis. Log-rank test and Cox proportional hazards regression were performed with overall survival (OS) and disease-free survival (DFS) as the primary outcome variables. Results: Of 139 patients, 64.7% (90/139) of them received radiotherapy. Of the 90 patients who underwent radiotherapy, 45.6% (41/90) received Involved Field radiotherapy and the rest 49 patients received Comprehensive radiotherapy. The median follow-up of 139 patients is 69 months. The 1-year, 3-year, and 5-year OS rates are 87%, 62%, and 39%, respectively, and the DFS rates are 73%, 45%, and 29%, respectively. Multivariate analysis of 139 patients with CCUP shows that differentiation grade, N stage, radiotherapy, and the length of the largest lymph node (DmaxLN) are the independent prognostic factors for both OS and DFS. Subgroup analysis of 90 patients who received radiotherapy shows that the Comprehensive radiotherapy group has a better OS (P < 0.001) and DFS (P < 0.001) compared with Involved Field radiotherapy. Conclusion: Radiotherapy is the independent prognostic factor for CCUP. Comprehensive radiotherapy may be superior to Involved Field radiotherapy in survival benefits. [ABSTRACT FROM AUTHOR]

• Interobserver reproducibility for boost delineation in breast cancer is mostly poor. • The presence of clips and fluid in the surgical bed improves this reproducibility. • Pre-operative imaging is useful to improve the accuracy of tumour bed delineation. • Boost delineation has become even more challenging since oncoplastic surgery exists. • Guidelines involving surgeon, pathologist, radiologist and oncologist are essential. Several randomized controlled trials have demonstrated the benefit of a boost to the tumor bed (TB) to reduce the risk of ipsilateral breast tumor recurrence. Recent technological progress has facilitated improved conformation of isodoses around the target volume. The accuracy and reproducibility of TB delineation have become even more essential. The purpose of this study is to review the extant knowledge on the boost delineation in breast cancer, focusing on interobserver variability (IOV) and the influence of various factors, such as the presence of clips or different imaging modalities to improve IOV. Most studies investigating IOV for boost delineation have shown poor reproducibility (with comparison indices such as the dice similarity index around 0.5). Clips in the lumpectomy cavity (LC), postoperative fluid accumulation in the LC and/or high cavity visualization score appeared to be associated with improved IOV. Likewise, the use of preoperative imaging (CT and/or MRI) may also be useful in improving the accuracy of TB definition but without any real gain in terms of IOV. Moreover, the delineation of boost has become even more challenging since the development of oncoplastic surgery. To improve the reproducibility and the accuracy of boost delineation, this review suggests that within each center, a group of multidisciplinary experts, including surgeons, radiation oncologists, pathologists, and radiologists, should convene to develop local guidelines, which may include the choice of preoperative imaging, the number and location of surgical clips, pathological margins, and orientation. The elaboration of contouring atlas is certainly of great assistance. [ABSTRACT FROM AUTHOR]

Background and Purpose: This study aimed to investigate inter-/intra-observer delineation variability in GTVs of primary esophageal carcinomas (ECs) based on planning CT with reference to different combinations of diagnostic multimodal images from endoscopy/EUS, esophagography and FDG-PET/CT. Materials and Methods: Fifty patients with pathologically proven thoracic EC who underwent diagnostic multimodal images before concurrent chemoradiotherapy were enrolled. Five radiation oncologist independently delineated the GTVs based on planning CT only (GTVC), CT combined with endoscopy/EUS (GTVCE), CT combined with endoscopy/EUS and esophagography (X-ray) (GTVCEX), and CT combined with endoscopy/EUS, esophagography, and FDG-PET/CT (GTVCEXP). The intra-/inter-observer variability in the volume, longitudinal length, generalized CI (CIgen), and position of the GTVs were assessed. Results: The intra-/inter-observer variability in the volume and longitudinal length of the GTVs showed no significant differences (p> 0.05). The mean intra-observer CIgen values for all observers was 0.73 ± 0.15. The mean inter-observer CIgen values for the four multimodal image combinations was 0.67 ± 0.11. The inter-observer CIgen for the four combined images was the largest, showing significant differences with those for the other three combinations. The intra-observer CIgen among different observers and inter-observer CIgen among different combinations of multimodal images showed significant differences (p <0.001). The intra-observer CIgen for the senior radiotherapists was larger than that for the junior radiotherapists (p <0.001). Conclusion: For radiation oncologists with advanced medical imaging training and clinical experience, using diagnostic multimodal images from endoscopy/EUS, esophagography, and FDG-PET/CT could reduce the intra-/inter-observer variability and increase the accuracy of target delineation in primary esophageal carcinomas. [ABSTRACT FROM AUTHOR]

Background and PurposeThis study aimed to investigate inter-/intra-observer delineation variability in GTVs of primary esophageal carcinomas (ECs) based on planning CT with reference to different combinations of diagnostic multimodal images from endoscopy/EUS, esophagography and FDG-PET/CT.Materials and MethodsFifty patients with pathologically proven thoracic EC who underwent diagnostic multimodal images before concurrent chemoradiotherapy were enrolled. Five radiation oncologist independently delineated the GTVs based on planning CT only (GTVC), CT combined with endoscopy/EUS (GTVCE), CT combined with endoscopy/EUS and esophagography (X-ray) (GTVCEX), and CT combined with endoscopy/EUS, esophagography, and FDG-PET/CT (GTVCEXP). The intra-/inter-observer variability in the volume, longitudinal length, generalized CI (CIgen), and position of the GTVs were assessed.ResultsThe intra-/inter-observer variability in the volume and longitudinal length of the GTVs showed no significant differences (p>0.05). The mean intra-observer CIgen values for all observers was 0.73 ± 0.15. The mean inter-observer CIgen values for the four multimodal image combinations was 0.67 ± 0.11. The inter-observer CIgen for the four combined images was the largest, showing significant differences with those for the other three combinations. The intra-observer CIgen among different observers and inter-observer CIgen among different combinations of multimodal images showed significant differences (p

PurposeSpine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation.MethodsTwenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95).ResultsQualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively.ConclusionESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time.

Purpose: Spine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation. Methods: Twenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95). Results: Qualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively. Conclusion: ESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time. [ABSTRACT FROM AUTHOR]

Abstract Background To compare irradiation dose to the second and third portions of duodenum (Duo2 and Duo3) with a new refined and old delineation method of para-aortic region for patients with locally advanced cervical cancer (LACC) receiving prophylactic extended-field radiotherapy (EFRT). Methods Twenty consecutive patients with LACC were treated with prophylactic EFRT from January 2016 to January 2017 at our institute. Two delineation methods of para-aortic region were designed for each patient, the old delineation method ensured a full coverage of aortic and inferior vena cava, while the right paracaval region above L3 was omitted from CTV in the new delineation method. Patients received a dose of 50.4Gy in 28 fractions for PCTV and a dose of 60.2Gy in 28 fractions for PGTV with volumetric-modulated arc therapy (VMRT). The dose delivered to Duo2 and Duo3 with these two delineation methods were compared. Results All treatment plans achieved excellent target volume coverage with 95% of PCTV receiving 50.4Gy and 95% of PGTV receiving 60.2Gy. There was no difference between delineation methods in low dose level (V5, V10, V15, V20, V25) for Duo2 and Duo3. The V30, V35, V40, V45, V50, Dmax, Dmean and D2cc for Duo2 with the new and old delineation methods were 55.76% vs 80.54% (P = 0.009), 34.72% vs 70.91% (P

Despite aggressive management consisting of maximal safe surgical resection followed by external beam radiation therapy (60 Gy/30 fractions) with concomitant and adjuvant temozolomide, approximately 90% of WHO grade IV gliomas (glioblastomas, GBM) will recur locally within 2 years. For patients with recurrent GBM, no standard of care exists. Thanks to the continuous improvement in radiation science and technology, reirradiation has emerged as feasible approach for patients with brain tumors. Using stereotactic radiosurgery (SRS) or stereotactic radiotherapy (SRT), either hypofractionated or conventionally fractionated schedules, several studies have suggested survival benefits following reirradiation of patients with recurrent GBM; however, there are still questions to be answered about the efficacy and toxicity associated with a second course of radiation. We provide a clinical overview on current status and recent advances in reirradiation of GBM, addressing relevant clinical questions such as the appropriate patient selection and radiation technique, optimal dose fractionation, reirradiation tolerance of the brain and the risk of radiation necrosis. [ABSTRACT FROM AUTHOR]

Objective We aimed to investigate the correlation between dose distribution and relapse pattern in glioblastoma patients who underwent adjuvant radiotherapy (RT) and to discuss European Organisation for Research and Treatment of Cancer (EORTC) and Radiation Therapy Oncology Group (RTOG) guidelines commonly used for target volume delineation. Materials and Method Thirty-one consecutive glioblastoma patients who underwent adjuvant concomitant chemoradioterapy (temozolamide) after biopsy or surgical resection in our clinic between October 2011 and June 2018 were enrolled. Total dose of 60 Gy with 14 Gy boost after 46 Gy RT was given with 3 dimensional conformal (3DCRT) in 22 patients and intensity modulated technique (IMRT) in 9 patients. All patients were administered concomitant temozolamide 75 mg/m2/day. The MR images taken within 2 weeks before RT is considered as basal investigation. Recurrent lesions in control MR spectroscopy images within 2-3 months after RT were retrospectively contoured by a radiologist and fused with planning CT images. Increase in contrast enhancement and enhanced volume in T1 MR sequences or increase in edema in T2/FLAIR sequences is reported as progression. Recurrence is defined as new emerged lesions apart from resection cavity or known postoperative residual lesion. The fused images are evaluated dosimetrically to calculate D95 (Dose of %95 volume), D50 (Dose of %50 volume), V%95 (volume receiving % 95 of planned dose) of recurrent area. Results Median age of patients was 59 (28 -78) years with a median survival of 17 (5 - 66) months in 17 (5 -64) months of median follow up. Median overall survival was found to be 17 (5 - 66) months. GTR, subtotal resection (STR) and biopsy were performed in 19, 10 and 2 patients respectively. All but one patient had residual mass in the postroperative images. During follow up 1 patient progressed whereas 16 patient was stable. Recurrence was detected in 14 patients. Whole volume of recurred lesions was in PTV60 in 12 patients. In the remaining 2 patients, volume of recurrent lesion in PTV60 were 98.7 and 61.8 % respectively. Mean recurrent volume was found 11.14 (0.7 - 48) cc. The mean of maximum, minimum and mean doses were 6246 cGy (6043 - 6439), 5805 cGy (3574 - 6098) and 6106 cGy (5906 - 6223) respectively. Conclusion In our study 95% of the recurrent lesions were in PTV 60. In our opinion, the contribution of 46 Gy to edema, especially for patients with a large operation cavity and residual lesion which could cause high normal tissue toxicity is controversial. Therefore, single phase treatment is reasonable in these patients. [ABSTRACT FROM AUTHOR]

Introduction: The current standard treatment for high - grade glioma (HGG) Typically includes maximal surgical resection and adjuvant radiotherapy, with or without concurrent chemotherapy, followed by adjuvant chemotherapy. There is still some debate about the target delineation of HGG, and it continues to be a subject of investigation. This study aimed to assess the feasibility, safety, and efficacy of using a smaller margin of radiotherapy than what is recommended in the latest ESTRO/ACROP guideline for HGG. Methods: In this multicenter, randomized, open - label, controlled trial, patients aged 18 to 75 years with grade 3 and 4 gliomas were enrolled following surgery. Eligible patients were randomly assigned to either the standard group, based on RTOG guidelines, or the intervention group, which utilized a smaller margin of 1 cm. They received a total dose of 60 Gy in two phases according to the RTOG protocol. After chemoradiation, patients underwent brain MRI every three months during follow - up. The recurrence pattern was determined by the 95% isodose line on the CT scan used for treatment planning at the time of imaging progression. Results: A total of 258 patients were randomly assigned to two groups. Both groups were similar in terms of age, gender, radiotherapy technique, IDH mutation status, type of surgery, surgery - radiotherapy interval, duration of adjuvant chemotherapy, GTV60 volume, and the volume of GTV46. Grade 4 tumors were more prevalent in the control group (31.3% vs. 18.8%, p = 0.02). There was no significant difference in the in - field recurrence rates between the two groups (intervention: 84% vs. control: 83.8%, p = 0.829). Conclusions: Adjuvant radiotherapy of HGG with smaller margins does not compromise the recurrence pattern of the tumor. Therefore, it is safe to recommend a smaller margin in order to spare more normal brain tissue. [ABSTRACT FROM AUTHOR]