MR-CT image fusion method of intracranial tumors based on Res2Net.

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.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. METHODS: Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. RESULTS: In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001). CONCLUSION: Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.

Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy.

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.

Radiotherapy for Meningioma.

Meningiomas are the most common primary intracranial brain tumor, and have a heterogeneous biology and an unmet need for targeted treatment options. Existing treatments for meningiomas are limited to surgery, radiotherapy, or a combination of these depending on clinical and histopathological features. Treatment recommendations for meningioma patients take into consideration radiologic features, tumor size and location, and medical comorbidities, all of which may influence the ability to undergo complete resection. Ultimately, outcomes for meningioma patients are dictated by extent of resection and histopathologic factors, such as World Health Organization (WHO) grade and proliferation index. Radiotherapy is a critical component of meningioma treatment as either a definitive intervention using stereotactic radiosurgery or external beam radiotherapy, or in the adjuvant setting for residual disease or for adverse pathologic factors, such as high WHO grade. In this chapter, we provide a comprehensive review of radiotherapy treatment modalities, therapeutic considerations, radiation planning, and clinical outcomes for meningioma patients.

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information.

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.

Feasibility of 4D CT simulation with synchronized intravenous contrast injection in hepatocellular carcinoma.

BACKGROUND: Delivering Stereotactic Body Radiotherapy (SBRT) for Hepatocellular Carcinoma (HCC) is challenging mainly for two reasons: first, motion of the liver occurs in six degrees of freedom and, second, delineation of the tumor is difficult owing to a similar density of HCC to that of the adjoining healthy liver tissue in a non-contrast CT scan. To overcome both these challenges simultaneously, we performed a feasibility study to synchronize intravenous contrast to obtain an arterial and a delayed phase 4D CT. MATERIALS AND METHODS: We included seven HCC patients of planned for SBRT. 4D CT simulation was performed with synchronized intravenous contrast based on the formula TSCAN DELAY = T peak - (L0/Detector Coverage × Cine Duration in Seconds). This was followed by a delayed 4D CT scan. RESULTS: We found that, with our protocol, it is feasible to obtain a 4DCT with an arterial and a delayed phase making it comparable to a diagnostic multi-phase CT. The peak HU of the 4D scan and diagnostic CT were similar (mean peak HU 134.2 vs 143.1, p value = 0.58 N.S). Whereas in comparison with a non-contrast CT a significant rise in the peak HU was seen (mean peak 134.2 vs 61.4 p value = .00003). CONCLUSION: A synchronized contrast 4D CT simulation for HCC is safe and feasible. It results in good contrast enhancement comparable to a diagnostic 3D contrast CT scan.

[Application and Development Trend of Medical Image Automatic Segmentation Technology in Radiation Therapy].

The development of medical image segmentation technology has been briefly reviewed. The applications of auto-segmentation of organs at risk and target volumes based on Atlas and deep learning in the field of radiotherapy have been introduced in detail, respectively. Then the development direction and product model for general automatic sketching tools or systems based on solid clinical data are discussed.

Consistency between Targets Delineated by Angiography, Computed Tomography, and Magnetic Resonance Imaging in Stereotactic Radiosurgery for Arteriovenous Malformation.

BACKGROUND: Target identification is important for radiosurgery for arteriovenous malformations (AVMs). Targets defined by different imaging modalities may be inconsistent in practice. OBJECTIVES: The goal of this study is to review and analyze the consistency between targets defined by different imaging modalities in radiosurgery for AVMs. METHODS: From March 2007 to June 2011, AVM patients for radiosurgery whose targets were delineated by angiography/computed tomography (CT)/magnetic resonance imaging (MRI) were reviewed. Spetzler-Martin grades, hemorrhage history, and treatment volumes were checked. Dice similarity coefficients (DSCs) between targets were calculated and analyzed. RESULTS: Twenty-three patients were enrolled. The mean DSCs were between 0.37 and 0.51 for targets by different modalities. There was no significant difference in DSCs regarding Spetzler-Martin grades and hemorrhage history. For CT-delineated target volumes <3 cm3, MRI-delineated target volumes <5 cm3, and angiography-delineated target volumes <2 cm3, the DSCs between the different image modalities were significantly decreased. CONCLUSIONS: Consistency between targets delineated using different image modalities was likely to be unsatisfactory and worsen significantly in niduses with volumes <5 cm3. An iterative multimodality approach to confirm the delineated targets of AVMs is suggested to be indispensable for robust treatment in radiosurgery.

Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia.

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.

Exclusion of non-Involved uterus from the target volume (EXIT-trial): An individualized treatment for locally advanced cervical cancer using modern radiotherapy and imaging techniques followed by completion surgery.

Background and purpose: Chemoradiotherapy followed by brachytherapy is the standard of care for locally advanced cervical cancer (LACC). In this study, we postulate that omitting an iconographical unaffected uterus (+12 mm distance from the tumour) from the treatment volume is safe and that no tumour will be found in the non-targeted uterus (NTU) leading to reduction of high-dose volumes of surrounding organs at risk (OARs). Material and Methods: In this single-arm phase 2 study, two sets of target volumes were delineated: one standard-volume (whole uterus) and an EXIT-volume (exclusion of non-tumour-bearing parts of the uterus with a minimum 12 mm margin from the tumour). All patients underwent chemoradiotherapy targeting the EXIT-volume, followed by completion hysterectomy. In 15 patients, a plan comparison between two treatment plans (PTV vs PTV_EXIT) was performed. The primary endpoint was the pathological absence of tumour involvement in the non-targeted uterus (NTU). Secondary endpoints included dosimetric impact of target volume reduction on OARs, acute and chronic toxicity, overall survival (OS), locoregional recurrence-free survival (LRFS), and progression-free survival (PFS). Results: In all 21 (FIGO stage I: 2; II: 14;III: 3; IV: 2) patients the NTU was pathologically negative. Ssignificant reductions in Dmean in bladder, sigmoid and rectum; V15Gy in sigmoid and rectum, V30Gy in bladder, sigmoid and rectum; V40Gy and V45Gy in bladder, bowel bag, sigmoid and rectum; V50Gy in rectum were achieved. Median follow-up was 54 months (range 7-79 months). Acute toxicity was mainly grade 2 and 5 % grade 3 urinary. The 3y- OS, PFS and LRFS were respectively 76,2%, 64,9% and 81 %. Conclusion: MRI-based exclusion of the non-tumour-bearing parts of the uterus at a minimum distance of 12 mm from the tumour out of the target volume in LACC can be done without risk of residual disease in the NTU, leading to a significant reduction of the volume of surrounding OARS treated to high doses.

A Prospective Study Measuring Resident and Faculty Contour Concordance: A Potential Tool for Quantitative Assessment of Residents' Performance in Contouring and Target Delineation in Radiation Oncology Residency.

PURPOSE/OBJECTIVE(S): 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. MATERIALS/METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Stereotactic body radiation therapy for spinal metastases: A new standard of care.

Advancements in systemic therapies for patients with metastatic cancer have improved overall survival and, hence, the number of patients living with spinal metastases. As a result, the need for more versatile and personalized treatments for spinal metastases to optimize long-term pain and local control has become increasingly important. Stereotactic body radiation therapy (SBRT) has been developed to meet this need by providing precise and conformal delivery of ablative high-dose-per-fraction radiation in few fractions while minimizing risk of toxicity. Additionally, advances in minimally invasive surgical techniques have also greatly improved care for patients with epidural disease and/or unstable spines, which may then be combined with SBRT for durable local control. In this review, we highlight the indications and controversies of SBRT along with new surgical techniques for the treatment of spinal metastases.

The impact of the new ESTRO-ACROP target volume delineation guidelines for postmastectomy radiotherapy after implant-based breast reconstruction on breast complications.

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.

Development and comprehensive evaluation of a national DBCG consensus-based auto-segmentation model for lymph node levels in breast cancer radiotherapy.

BACKGROUND AND PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Efficacy and safety of FDG-PET for determining target volume during intensity-modulated radiotherapy for head and neck cancer involving the oral level.

PURPOSE: To determine the efficacy and safety of target volume determination by 18F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) for intensity-modulated radiation therapy (IMRT) for locally advanced head and neck squamous cell carcinoma (HNSCC) extending into the oral cavity or oropharynx. METHODS: We prospectively treated 10 consecutive consenting patients with HNSCC using IMRT, with target volumes determined by PET-CT. Gross tumor volume (GTV) and clinical target volume (CTV) at the oral level were determined by two radiation oncologists for CT, magnetic resonance imaging (MRI), and PET-CT. Differences in target volume (GTVPET, GTVCT, GTVMRI, CTVPET, CTVCT, and CTVMRI) for each modality and the interobserver variability of the target volume were evaluated using the Dice similarity coefficient and Hausdorff distance. Clinical outcomes, including acute adverse events (AEs) and local control were evaluated. RESULTS: The mean GTV was smallest for GTVPET, followed by GTVCT and GTVMRI. There was a significant difference between GTVPET and GTVMRI, but not between the other two groups. The interobserver variability of target volume with PET-CT was significantly less than that with CT or MRI for GTV and tended to be less for CTV, but there was no significant difference in CTV between the modalities. Grade ≤ 3 acute dermatitis, mucositis, and dysphagia occurred in 55%, 88%, and 22% of patients, respectively, but no grade 4 AEs were observed. There was no local recurrence at the oral level after a median follow-up period of 37 months (range, 15-55 months). CONCLUSIONS: The results suggest that the target volume determined by PET-CT could safely reduce GTV size and interobserver variability in patients with locally advanced HNSCC extending into the oral cavity or oropharynx undergoing IMRT. Trial registration UMIN, UMIN000033007. Registered 16 jun 2018, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000037631.

Evaluating AI-generated CBCT-based synthetic CT images for target delineation in palliative treatments of pelvic bone metastasis at conventional C-arm linacs.

PURPOSE: One-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients' comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy. MATERIALS AND METHODS: CBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins. RESULTS: Radiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT. CONCLUSION: The evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs.

Comparative dosimetric study of radiotherapy in high-grade gliomas based on the guidelines of EORTC and NRG-2019 target delineation.

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. Clinical trial registration: 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.

Implications of using the clinical target distribution as voxel-weights in radiation therapy optimization.

Objective.Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposedclinical target distribution(CTD) is an alternative to the conventionalclinical target volume(CTV), with initial promise. Previously, using the CTD in planning has primarily been evaluated in comparison to a conventionally defined CTV. We propose to compare the CTD approach against CTV margins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant.Approach.First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with the CTD approach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both one- and three-dimensional phantoms, we compare a set of CTD plans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTV margin size.Main results.The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, the CTD is better at maintaining sufficiently high dose toward the center of the target.Significance.We conclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.

Key changes in the future clinical application of ultra-high dose rate radiotherapy.

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.

Clinical treatment considerations in the intensity-modulated radiotherapy era for parotid lymph node metastasis in patients with nasopharyngeal carcinoma.

PURPOSE: No specific irradiation guidelines have been proposed for parotid lymph node (PLN) metastasis in patients with nasopharyngeal carcinoma (NPC). This study aimed to explore the dose prescription and target delineation for PLN metastasis in patients with NPC. METHODS: With the NPC database from a big-data platform, 10,685 patients with primarily diagnosed, non-distant metastatic, histologically proven NPC and treated with intensity modulated radiotherapy (IMRT) at our center from 2008 to 2019 were reviewed and those with PLN metastasis were enrolled in this study. Dosimetry parameters were collected from the dose-volume histograms (DVH). The primary endpoint was overall survival (OS). Least absolute shrinkage and selection operator regression (LASSO) was operated for variable selection. Multivariate Cox regression analysis was applied to identify the independent prognostic factors. RESULTS: PLN metastases were identified in 275/10685 (2.5%) patients. Of 367 positive PLN, 199 were in superficial intra-parotid, followed by 70 in deep intra-parotid, 54 in subparotid and 44 in subcutaneous pre-auricular. Better survival outcome was observed in PLN-radical IMRT group, compared with PLN-sparing group. In 190 patients received PLN-radical IMRT, multivariate analysis revealed that D95% of level VIII > 55 Gy was an independent beneficial prognostic factor for overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and parotid relapse-free survival (PRFS). CONCLUSION: Based on the distribution pattern of PLN metastasis in NPC and the result of dose-finding study, involving the ipsilateral level VIII into low-risk clinical target volume (CTV2) is recommended for NPC with PLN metastasis.