Optimization of collimator angle combined island blocking with parked gap achieves superior normal tissue sparing in SBRT planning of multiple liver lesions.

PURPOSE: To propose an efficient collimator angle optimization method by combining island blocking (IB) and parked gap (PG) problem to reduce the radiotherapy dose for normal tissue. The reduction will be done with single-isocenter multi-lesion volumetric modulated arc therapy (VMAT) for the stereotactic body radiation therapy (SBRT) of liver cancer. METHODS: A novel collimator angle optimization algorithm was developed based on the two-dimensional projection of targets on a beam's eye view (BEV) plane as a function of gantry and collimator angle. This optimization algorithm minimized the sum of the combined IB and PG (IB & PG) areas from all gantry angles for each arc. For comparison, two SBRT plans were respectively generated for each of the 20 retrospective liver cancer cases with multiple lesions. One plan was optimized using the IB & PG algorithm, and the other plan was optimized with a previously reported optimization algorithm that only considered the IB area. Plans were then evaluated and compared using typical dosimetric metrics. RESULTS: With the comparable target coverage, IB & PG plans had significantly lower D500cc , D700cc , mean dose (Dmean ), and V15 of normal liver tissues when compared to IB plans. The median percent reductions were 3.32% to 5.36%. The D1cc , D5cc , and Dmean for duodenum and small intestine in IB & PG plans were significantly reduced in a range from 7.60% up to 16.03%. Similarly, the median integral dose was reduced by 3.73%. Furthermore, the percentage of normal liver Dmean sparing when IB & PG plans compared to IB plans, was found to be positively correlated (ρ = 0.669, P = 0.001) with the inter-target distance. CONCLUSION: The proposed IB & PG algorithm has been demonstrated to outperform the IB algorithm in almost all normal tissue sparing, and the magnitude of liver sparing was positively correlated with inter-target distance.

Dosimetric comparison in sparing normal tissue dosage by using auto-SBRT planning in oligo liver tumors.

Purpose: The study aimed to compare the dosimetric distribution of VMAT plans by increasing the number of half arcs in liver SBRT and investigate the effect by using automatic plan software in plan optimization. Method: Thirty-one patients with oligo liver tumors were randomly selected. VMAT treatment plans with different numbers of coplanar half arcs were generated. Result: Adding arcs significantly increased the PTV, D2%, D50%, and CI, but sacrificed the plan homogeneity. It also decreased the maximum dose of normal tissues such as the stomach, duodenum, and spinal cord and reduced Dmean, D500cc, and D700cc for the liver. Nevertheless, the diminishing effect gradually decayed into three arcs. Meanwhile, the addition of arcs substantially extended the beam-on time. Conclusion: In the context of SBRT for oligo liver tumors, increasing the number of coplanar half arcs will improve PTV conformity and offer better protection for OARs, albeit at the expense of increased treatment duration. Considering the trade-off between plan quality and treatment efficiency, a three-arc plan may be more suitable for clinical implementation.

Automated treatment planning for liver cancer stereotactic body radiotherapy

Purpose To evaluate the quality of fully automated stereotactic body radiation therapy (SBRT) planning based on volumetric modulated arc therapy, which can reduce the reliance on historical plans and the experience of dosimetrists. Methods Fully automated re-planning was performed on twenty liver cancer patients, automated plans based on automated SBRT planning (ASP) program and manual plans were conducted and compared. One patient was randomly selected and evaluate the repeatability of ASP, ten automated and ten manual SBRT plans were generated based on the same initial optimization objectives. Then, ten SBRT plans were generated for another selected randomly patient with different initial optimization objectives to assess the reproducibility. All plans were clinically evaluated in a double-blinded manner by five experienced radiation oncologists. Results Fully automated plans provided similar planning target volume dose coverage and statistically better organ at risk sparing compared to the manual plans. Notably, automated plans achieved significant dose reduction in spinal cord, stomach, kidney, duodenum, and colon, with a median dose of D2% reduction ranging from 0.64 to 2.85 Gy. R50% and Dmean of ten rings for automated plans were significantly lower than those of manual plans. The average planning time for automated and manual plans was 59.8 ± 7.9 min vs. 127.1 ± 16.8 min (− 67.3 min). Conclusion Automated planning for SBRT, without relying on historical data, can generate comparable or even better plan quality for liver cancer compared with manual planning, along with better reproducibility, and less clinically planning time (AU)

Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation.

Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time and effort to acquire. For cancers of low-incidence, such as cervical cancer, it is often a luxury to collect an adequate amount of labeled data to train a well-performing deep learning (DL) model. To mitigate this problem, in this paper, we resort to the unsupervised domain adaptation (UDA) strategy to achieve accurate dose prediction for cervical cancer (target domain) by leveraging the well-labeled high-incidence rectal cancer (source domain). Specifically, we introduce the cross-attention mechanism to learn the domain-invariant features and develop a cross-attention transformer-based encoder to align the two different cancer domains. Meanwhile, to preserve the target-specific knowledge, we employ multiple domain classifiers to enforce the network to extract more discriminative target features. In addition, we employ two independent convolutional neural network (CNN) decoders to compensate for the lack of spatial inductive bias in the pure transformer and generate accurate dose maps for both domains. Furthermore, to enhance the performance, two additional losses, i.e. a knowledge distillation loss (KDL) and a domain classification loss (DCL), are incorporated to transfer the domain-invariant features while preserving domain-specific information. Experimental results on a rectal cancer dataset and a cervical cancer dataset have demonstrated that our method achieves the best quantitative results with [Formula: see text], [Formula: see text], and HI of 1.446, 1.231, and 0.082, respectively, and outperforms other methods in terms of qualitative assessment.

A Transformer-Embedded Multi-Task Model for Dose Distribution Prediction.

Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on experience, causing it extremely subjective and time-consuming to obtain a clinically acceptable plan. To this end, we introduce a transformer-embedded multi-task dose prediction (TransMTDP) network to automatically predict the dose distribution in radiotherapy. Specifically, to achieve more stable and accurate dose predictions, three highly correlated tasks are included in our TransMTDP network, i.e. a main dose prediction task to provide each pixel with a fine-grained dose value, an auxiliary isodose lines prediction task to produce coarse-grained dose ranges, and an auxiliary gradient prediction task to learn subtle gradient information such as radiation patterns and edges in the dose maps. The three correlated tasks are integrated through a shared encoder, following the multi-task learning strategy. To strengthen the connection of the output layers for different tasks, we further use two additional constraints, i.e. isodose consistency loss and gradient consistency loss, to reinforce the match between the dose distribution features generated by the auxiliary tasks and the main task. Additionally, considering many organs in the human body are symmetrical and the dose maps present abundant global features, we embed the transformer into our framework to capture the long-range dependencies of the dose maps. Evaluated on an in-house rectum cancer dataset and a public head and neck cancer dataset, our method gains superior performance compared with the state-of-the-art ones. Code is available at https://github.com/luuuwen/TransMTDP.

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification.

Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits. Nevertheless, these methods require certain auxiliary inputs besides CT images, such as segmentation masks of the tumor and organs at risk (OARs), which limits their prediction efficiency and application potential. To address this issue, we design a novel approach named as TransDose for dose distribution prediction that treats CT images as the unique input in this paper. Specifically, instead of inputting the segmentation masks to provide the prior anatomical information, we utilize a super-pixel-based graph convolutional network (GCN) to extract category-specific features, thereby compensating the network for the necessary anatomical knowledge. Besides, considering the strong continuous dependency between adjacent CT slices as well as adjacent dose maps, we embed the Transformer into the backbone, and make use of its superior ability of long-range sequence modeling to endow input features with inter-slice continuity message. To our knowledge, this is the first network that specially designed for the task of dose prediction from only CT images without ignoring necessary anatomical structure. Finally, we evaluate our model on two real datasets, and extensive experiments demonstrate the generalizability and advantages of our method.

Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy.

PURPOSE: Our purpose was to develop a deep learning model (AbsegNet) that produces accurate contours of 16 organs at risk (OARs) for abdominal malignancies as an essential part of fully automated radiation treatment planning. METHODS AND MATERIALS: Three data sets with 544 computed tomography scans were retrospectively collected. Data set 1 was split into 300 training cases and 128 test cases (cohort 1) for AbsegNet. Data set 2, including cohort 2 (n = 24) and cohort 3 (n = 20), were used to validate AbsegNet externally. Data set 3, including cohort 4 (n = 40) and cohort 5 (n = 32), were used to clinically assess the accuracy of AbsegNet-generated contours. Each cohort was from a different center. The Dice similarity coefficient and 95th-percentile Hausdorff distance were calculated to evaluate the delineation quality for each OAR. Clinical accuracy evaluation was classified into 4 levels: no revision, minor revisions (0% < volumetric revision degrees [VRD] ≤ 10%), moderate revisions (10% ≤ VRD < 20%), and major revisions (VRD ≥20%). RESULTS: For all OARs, AbsegNet achieved a mean Dice similarity coefficient of 86.73%, 85.65%, and 88.04% in cohorts 1, 2, and 3, respectively, and a mean 95th-percentile Hausdorff distance of 8.92, 10.18, and 12.40 mm, respectively. The performance of AbsegNet outperformed SwinUNETR, DeepLabV3+, Attention-UNet, UNet, and 3D-UNet. When experts evaluated contours from cohorts 4 and 5, 4 OARs (liver, kidney_L, kidney_R, and spleen) of all patients were scored as having no revision, and over 87.5% of patients with contours of the stomach, esophagus, adrenals, or rectum were considered as having no or minor revisions. Only 15.0% of patients with colon and small bowel contours required major revisions. CONCLUSIONS: We propose a novel deep-learning model to delineate OARs on diverse data sets. Most contours produced by AbsegNet are accurate and robust and are, therefore, clinically applicable and helpful to facilitate radiation therapy workflow.

Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation.

Accurate segmentation of multiple abdominal organs from Computed Tomography (CT) images plays an important role in computer-aided diagnosis, treatment planning and follow-up. Currently, 3D Convolution Neural Networks (CNN) have achieved promising performance for automatic medical image segmentation tasks. However, most existing 3D CNNs have a large set of parameters and huge floating point operations (FLOPs), and 3D CT volumes have a large size, leading to high computational cost, which limits their clinical application. To tackle this issue, we propose a novel framework based on lightweight network and Knowledge Distillation (KD) for delineating multiple organs from 3D CT volumes. We first propose a novel lightweight medical image segmentation network named LCOV-Net for reducing the model size and then introduce two knowledge distillation modules (i.e., Class-Affinity KD and Multi-Scale KD) to effectively distill the knowledge from a heavy-weight teacher model to improve LCOV-Net's segmentation accuracy. Experiments on two public abdominal CT datasets for multiple organ segmentation showed that: 1) Our LCOV-Net outperformed existing lightweight 3D segmentation models in both computational cost and accuracy; 2) The proposed KD strategy effectively improved the performance of the lightweight network, and it outperformed existing KD methods; 3) Combining the proposed LCOV-Net and KD strategy, our framework achieved better performance than the state-of-the-art 3D nnU-Net with only one-fifth parameters. The code is available at https://github.com/HiLab-git/LCOVNet-and-KD.

Automated treatment planning for liver cancer stereotactic body radiotherapy.

PURPOSE: To evaluate the quality of fully automated stereotactic body radiation therapy (SBRT) planning based on volumetric modulated arc therapy, which can reduce the reliance on historical plans and the experience of dosimetrists. METHODS: Fully automated re-planning was performed on twenty liver cancer patients, automated plans based on automated SBRT planning (ASP) program and manual plans were conducted and compared. One patient was randomly selected and evaluate the repeatability of ASP, ten automated and ten manual SBRT plans were generated based on the same initial optimization objectives. Then, ten SBRT plans were generated for another selected randomly patient with different initial optimization objectives to assess the reproducibility. All plans were clinically evaluated in a double-blinded manner by five experienced radiation oncologists. RESULTS: Fully automated plans provided similar planning target volume dose coverage and statistically better organ at risk sparing compared to the manual plans. Notably, automated plans achieved significant dose reduction in spinal cord, stomach, kidney, duodenum, and colon, with a median dose of D2% reduction ranging from 0.64 to 2.85 Gy. R50% and Dmean of ten rings for automated plans were significantly lower than those of manual plans. The average planning time for automated and manual plans was 59.8 ± 7.9 min vs. 127.1 ± 16.8 min (- 67.3 min). CONCLUSION: Automated planning for SBRT, without relying on historical data, can generate comparable or even better plan quality for liver cancer compared with manual planning, along with better reproducibility, and less clinically planning time.

Construction of cuproptosis-related gene signature to predict the prognosis and immunotherapy efficacy of patients with bladder cancer through bioinformatics analysis and experimental validation.

Background: A new form of cell death, copper-dependent cell death (termed cuproptosis), was illustrated in a recent scientific study. However, the biological function or prognostic value of cuproptosis regulators in bladder cancer (BLCA) remains unknown. Materials and Methods: Sequencing data obtained from BLCA samples in TCGA and GEO databases were preprocessed for analysis. Biological function and immune cell infiltration levels evaluated by gene set variation analysis (GSVA) were employed to calculate enrichment scores. Iteration least absolute shrinkage and selection operator (LASSO) and COX regression model were employed to select feature genes and construct a novel cuproptosis-related (CR) score signature. The genomics of drug sensitivity in cancer (GDSC) and tumor immune dysfunction and exclusion (TIDE) analysis were used to predict the chemotherapy and immunotherapy efficacy for BLCA patients. The relative expression of the genes involved in the signature was also verified by real-time quantitative PCR (qRT-PCR) in cell lines and tissues. Results: Expression abundance and the prognostic value of cuproptosis regulators proved that cuproptosis might play a vital part in the carcinogenesis of BLCA. GSVA revealed that cuproptosis regulators might be associated with metabolism and metastasis-related pathways such as TGF-ß, protein secretion, oxidative Phosphorylation, MYC targets, MTORC1, and adipogenesis pathways. CR scores could predict the prognosis and evaluate the chemotherapy and immunotherapy efficacies of BLCA. CR scores were positively correlated with EMT, MYC, MTORC1, HEDGEHOG, and E2F signaling pathways; meanwhile, they were negatively correlated with several immune cell infiltration levels such as CD8+ T cells, γδT cells, and activated dendritic cells. Several GEO datasets were used to validate the power of prognostic prediction, and a nomogram was also established for clinical use. The expressions of DDX10, RBM34, and RPL17 were significantly higher in BLCA cell lines and tissues in comparison with those in the corresponding normal controls. Conclusion: Cuproptosis might play an essential role in the progression of BLCA. CR scores could be helpful in the investigation of prognostic prediction and therapeutic efficacy and could make contributions to further studies in BLCA.

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image.

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-scale Whole abdominal ORgan Dataset (WORD) for algorithm research and clinical application development. This dataset contains 150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotations, which may be the largest dataset with whole abdominal organ annotation. Several state-of-the-art segmentation methods are evaluated on this dataset. And we also invited three experienced oncologists to revise the model predictions to measure the gap between the deep learning method and oncologists. Afterwards, we investigate the inference-efficient learning on the WORD, as the high-resolution image requires large GPU memory and a long inference time in the test stage. We further evaluate the scribble-based annotation-efficient learning on this dataset, as the pixel-wise manual annotation is time-consuming and expensive. The work provided a new benchmark for the abdominal multi-organ segmentation task, and these experiments can serve as the baseline for future research and clinical application development.

CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network.

Purpose: Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically. Materials and Methods: We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients. Results: Experimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas). Conclusion: The proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT.

Treatment plan comparison of volumetric-modulated arc therapy to intensity-modulated radiotherapy in lung stereotactic body radiotherapy using either 6- or 10-MV photon energies.

PURPOSE: The aim of this study was to dosimetrically compare volumetric-modulated arc therapy (VMAT) with intensity-modulated radiotherapy (IMRT) techniques using either 6- or 10-MV photon beam energies in lung stereotactic body radiation therapy (SBRT) plans. METHODS: Thirty patients with primary or metastatic lung tumors eligible for SBRT were randomly selected. VMAT and IMRT treatment plans using either 6- or 10-MV photon energies were generated through automatic SBRT planning software in the RayStation treatment planning system. RESULTS: For planning target volume, there was no difference in D95% for all plans, whereas D2% and D50% were significantly increased by 5.22%-5.98% and 2.47%-2.59%, respectively, using VMAT6/10-MV plans compared to IMRT6/10-MV plans. When comparing the Dmax of organs at risk (OARs), VMAT6/10-MV was 18.32%-47.95% lower than IMRT6/10-MV for almost all OARs. VMAT6/10-MV obviously decreased Dmean , V5Gy , V10Gy , and V20Gy of whole lung by 9.68%-20.92% than IMRT6/10-MV . Similar results were found when comparing VMAT6-MV with IMRT10-MV or VMAT10-MV with IMRT6-MV . The differences in the D2% , heterogeneity index, and conformity index between 6- and 10-MV plans are not statistically significant. Plans using 6-MV performed 4.68%-8.91% lower levels of Dmax of spinal cord, esophagus, great vessels, and trachea and proximal bronchial tree than those using 10-MV plans. Similarly, Dmean , V5Gy , V10Gy , and V20Gy of whole lung were also reduced by 2.79%-5.25% using 6-MV. For dose fall-off analysis, the D2cm and R50% of VMAT6/10-MV were lower than those of IMRT6/10-MV . Dose fall-off curve based on 10 rings was steeper for VMAT plans than IMRT plans regardless of the energy used. CONCLUSIONS: For lung SBRT plans, VMAT-based plans significantly reduced OARs dose and steepened dose fall-off curves compared to IMRT-based plans. A 6-MV energy level was a better choice than 10-MV for lung SBRT. In addition, the dose differences between different techniques were more obvious than those between different energy levels.

Automatic Delineation of Gross Tumor Volume Based on Magnetic Resonance Imaging by Performing a Novel Semisupervised Learning Framework in Nasopharyngeal Carcinoma.

PURPOSE: We aimed to validate the accuracy and clinical value of a novel semisupervised learning framework for gross tumor volume (GTV) delineation in nasopharyngeal carcinoma. METHODS AND MATERIALS: Two hundred fifty-eight patients with magnetic resonance imaging data sets were divided into training (n = 180), validation (n = 20), and testing (n = 58) cohorts. Ground truth contours of nasopharynx GTV (GTVnx) and node GTV (GTVnd) were manually delineated by 2 experienced radiation oncologists. Twenty percent (n = 36) labeled and 80% (n = 144) unlabeled images were used to train the model, producing model-generated contours for patients from the testing cohort. Nine experienced experts were invited to revise model-generated GTV in 20 randomly selected patients from the testing cohort. Six junior oncologists were asked to delineate GTV in 12 randomly selected patients from the testing cohort without and with the assistance of the model, and revision degrees were compared under these 2 modes. The Dice similarity coefficient (DSC) was used to quantify the accuracy of the model. RESULTS: The model-generated contours showed a high accuracy compared with ground truth contours, with an average DSC score of 0.83 and 0.80 for GTVnx and GTVnd, respectively. There was no significant difference in DSC score between T1-2 and T3-4 patients (0.81 vs 0.83; P = .223), or between N1-2 and N3 patients (0.80 vs 0.79; P = .807). The mean revision degree was lower than 10% in 19 (95%) patients for GTVnx and in 16 (80%) patients for GTVnd. With assistance of the model, the mean revision degree for GTVnx and GTVnd by junior oncologists was reduced from 25.63% to 7.75% and from 21.38% to 14.44%, respectively. Meanwhile, the delineating efficiency was improved by over 60%. CONCLUSIONS: The proposed semisupervised learning-based model showed a high accuracy for delineating GTV of nasopharyngeal carcinoma. It was clinically applicable and could assist junior oncologists to improve GTV contouring accuracy and save contouring time.

Multi-constraint generative adversarial network for dose prediction in radiotherapy.

Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to deliver an accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). To improve the effectiveness of the treatment planning, deep learning methods are widely adopted to predict dose distribution maps for clinical treatment planning. In this paper, we present a novel multi-constraint dose prediction model based on generative adversarial network, named Mc-GAN, to automatically predict the dose distribution map from the computer tomography (CT) images and the masks of PTV and OARs. Specifically, the generator is an embedded UNet-like structure with dilated convolution to capture both the global and local information. During the feature extraction, a dual attention module (DAM) is embedded to force the generator to take more heed of internal semantic relevance. To improve the prediction accuracy, two additional losses, i.e., the locality-constrained loss (LCL) and the self-supervised perceptual loss (SPL), are introduced besides the conventional global pixel-level loss and adversarial loss. Concretely, the LCL tries to focus on the predictions of locally important areas while the SPL aims to prevent the predicted dose maps from the possible distortion at the feature level. Evaluated on two in-house datasets, our proposed Mc-GAN has been demonstrated to outperform other state-of-the-art methods in almost all PTV and OARs criteria.

A novel dosimetric metrics-based risk model to predict local recurrence in nasopharyngeal carcinoma patients treated with intensity-modulated radiation therapy.

BACKGROUND: To develop a risk model based on dosimetric metrics to predict local recurrence in nasopharyngeal carcinoma (NPC) patients treated with intensive modulated radiation therapy (IMRT). METHODS: 493 consecutive patients were included, among whom 44 were with local recurrence. One-to-two propensity score matching (PSM) was used to balance variables between recurrent and non-recurrent groups. Dosimetric metrics were extracted, and critical dosimetric predictors of local recurrence were identified by Cox regression model. Moreover, recurrent sites and patterns were examined by transferring the recurrent tumor to the pretreatment planning computed tomography. RESULTS: After PSM, 44 recurrent and 88 non-recurrent patients were used for dosimetric analysis. The univariate analysis showed that eight dosimetric metrics and homogeneity index were significantly associated with local recurrence. The risk model integrating D5 and D95 achieved a C-index of 0.706 for predicting 3-year local recurrence free survival (LRFS). By grouping patients using median value of risk score, patients with risk score ˃ 0.885 had significantly lower 3-year LRFS (66.2% vs. 86.4%, p = 0.023). As for recurrent features, the proportion of relapse in nasopharynx cavity, clivus, and pterygopalatine fossa was 61.4%, 52.3%, and 40.9%, respectively; and in field, marginal, and outside field recurrence constituted 68.2%, 20.5% and 11.3% of total recurrence, respectively. CONCLUSIONS: The current study developed a novel risk model that could effectively predict the LRFS in NPC patients. Additionally, nasopharynx cavity, clivus, and pterygopalatine fossa were common recurrent sites and in field recurrence remained the major failure pattern of NPC in the IMRT era.

Influence of target dose heterogeneity on dose sparing of normal tissue in peripheral lung tumor stereotactic body radiation therapy.

OBJECTIVE: To evaluate the influence of target dose heterogeneity on normal tissue dose sparing for peripheral lung tumor stereotactic body radiation therapy (SBRT). METHODS: Based on the volumetric-modulated arc therapy (VMAT) technique, three SBRT plans with homogeneous, moderate heterogeneous, and heterogeneous (HO, MHE, and HE) target doses were compared in 30 peripheral lung tumor patients. The prescription dose was 48 Gy in 4 fractions. Ten rings outside the PTV were created to limit normal tissue dosage and evaluate dose falloff. RESULTS: When MHE and HE plans were compared to HO plans, the conformity index of the PTV was increased by approximately 0.08. The median mean lung dose (MLD), V5, V10, V20 of whole lung, D2%, D1cc, D2cc of the rib, V30 of the rib, D2% and the maximum dose (Dmax) of the skin, and D2% and Dmax of most mediastinal organs at risk (OARs) and spinal cord were reduced by up to 4.51 Gy or 2.8%. Analogously, the median Dmax, D2% and mean dose of rings were reduced by 0.71 to 8.46 Gy; and the median R50% and D2cm were reduced by 2.1 to 2.3 and 7.4% to 8.0%, respectively. Between MHE and HE plans there was little to no difference in OARs dose and dose falloff beyond the target. Furthermore, the dose sparing of rib V30 and the mean dose of rings were negatively correlated with the rib and rings distance from tumor, respectively. CONCLUSIONS: For peripheral lung tumor SBRT, target conformity, normal tissue dose, and dose falloff around the target could be improved by loosening or abandoning homogeneity. While there was negligible further dose benefit for the maximum target dose above 125% of the prescription, dose sparing of normal tissue derived from a heterogeneous target decreased as the distance from the tumor increased.

Description and evaluation of a new volumetric-modulated arc therapy plan complexity metric.

This study describes a new plan complexity metric for volumetric-modulated arc therapy (VMAT) and evaluates the relationship of this metric with the VMAT dosimetric accuracy. The new modulation complexity score for VMAT (NMCSv) that is based on the aperture shape and multi-leaf collimator (MLC) leaf travel is described. Its performance is evaluated through correlation and receiver operating characteristic (ROC) analyses with patient-specific gamma passing rates using 2 3-dimensional diode arrays. For comparison, the following metrics are evaluated using the same correlation analyses: average field width, average leaf travel, modulation complexity score, and leaf travel modulation complexity score. Spearman's rank correlation analysis is performed to examine any relationships between the complexity metrics and the patient-specific gamma passing rates. ROC curves are used to assess the performance of the plan metrics using a gamma passing rate of 3%/3 mm criterion with a 95% tolerance level. In both the diode arrays, the gamma passing rates (3%/3 mm and 2%/2 mm) for patient-specific dosimetric verification of VMAT plans are moderately or weakly correlated to all the complexity metrics. NMCSv demonstrates the highest correlation with the passing rates (r = 0.652, p < 0.001 for Delta4 and r = 0.499, p < 0.001 for ArcCheck) and the highest area under the curve value (0.809, p < 0.01 for Delta4 and 0.734, p < 0.01 for ArcCheck). While using the Delta4 system, NMCSv exhibits an excellent classification performance with area under the curves of 0.926 (sensitivity: 0.913; specificity: 0.860; p < 0.01) and 0.918 (sensitivity: 0.943; specificity: 0.720; p < 0.01) for rectal and cervical cancer plans, respectively. NMCSv as a novel potential clinical plan complexity metric is moderately correlated with the gamma passing rate. It demonstrates the best performance with respect to distinguishing the dosimetric accuracy of VMAT plans among the evaluated metrics. The classification performance of complexity metrics can be affected by various dosimetry verification devices and treatment sites.

Toxicity and dosimetric analysis of nasopharyngeal carcinoma patients undergoing radiotherapy with IMRT or VMAT: A regional center's experience.

OBJECTIVES: To observe the differences of dosimetric parameters and late toxicities in Nasopharyngeal Carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT), which may provide the selective basis about radiation technology in clinical practices. METHODS AND MATERIALS: Dosimetric parameters and late toxicities were collected and retrospectively analyzed from 627 NPC patients (stage as I-IVA/IVB) between January 2010 and December 2015. RESULTS: The median D2 of all targets and D50 of PGTVnd (regional lymph nodes) were lower in VAMT than those in IMRT, while the median D95 and D98 of PGTVnx (primary lesions) were higher in VMAT than those in IMRT (p < 0.05). Superior sparing of the organs at risk were observed in VMAT. The maximum dose of the brainstem, spinal cord, temporal lobes, temporomandibular joint, optic chiasm, and lens were lower in VMAT than those in IMRT, where the median dose reduction ranged from 0.56 to 3.56 Gy (p < 0.05). Meanwhile, the median parotid glands V30 in VMAT was reduced by approximately 2% compared to that in IMRT (p = 0.027). Regarding the late toxicities, ototoxicity, trismus, and temporal lobe injury were reduced by VMAT (p < 0.05). Furthermore, the late toxicities were correlative with the radiation dose of the corresponding OARs (p < 0.05). CONCLUSION: For NPC treatment plans, the VMAT might provide not only more favorable dose distributions of targets but also better sparing of normal tissue than observed in IMRT. Furthermore, VMAT possibly provides less treatment-related late toxicities such as ototoxicity, trismus, and temporal lobe injury.

Endothelial Autophagy: an Effective Target for Radiation-induced Cerebral Capillary Damage.

Toxicity to central nervous system tissues is the common side effects for radiotherapy of brain tumor. The radiation toxicity has been thought to be related to the damage of cerebral endothelium. However, because of lacking a suitable high-resolution vivo model, cellular response of cerebral capillaries to radiation remained unclear. Here, we present the flk:eGFP transgenic zebrafish larvae as a feasible model to study the radiation toxicity to cerebral capillary. We showed that, in living zebrafish larvae, radiation could induce acute cerebral capillary shrinkage and blood-flow obstruction, resulting brain hypoxia and glycolysis retardant. Although in vivo neuron damage was also observed after the radiation exposure, further investigation found that they didn't response to the same dosage of radiation in vitro, indicating that radiation induced neuron damage was a secondary-effect of cerebral vascular function damage. In addition, transgenic labeling and qPCR results showed that the radiation-induced acute cerebral endothelial damage was correlated with intensive endothelial autophagy. Different autophagy inhibitors could significantly alleviate the radiation-induced cerebral capillary damage and prolong the survival of zebrafish larvae. Therefore, we showed that radiation could directly damage cerebral capillary, resulting to blood flow deficiency and neuron death, which suggested endothelial autophagy as a potential target for radiation-induced brain toxicity.