Dosimetry and outcomes in patients receiving radiotherapy for synchronous bilateral breast cancers

BackgroundSynchronous bilateral breast cancer (SBBC) is rare and there is little evidence describing organs at risk (OAR) and limits to the heart and lungs caused by radiotherapy (RT). Quantifying mean heart dose (MHD) and mean lung dose (MLD) from RT in this patient cohort may lead to better understanding of doses to OAR and resultant effects on clinical outcomes. The primary objective was to assess median MHD and MLD in SBBC, while secondary aims included analyses of 1) factors associated with MHD and MLD, 2) V5 and V20 values and 3) factors associated with clinical outcomes.MethodsPatients planned for adjuvant bilateral whole breast/chest wall (WB) RT from a single institution treated in 2011-2018 were included. Median MHD and MLD (Gy) were stratified by hypofractionated (42.56 Gy/16 fractions, HFRT) and conventional fractionation (50 Gy/ 25 fractions, CFRT) and summarized separately based on the following treatments: 1) locoregional RT, WB tangential RT either 2) no boost 3) sequential boost or 4) simultaneous integrated boost. MHD, MLD, lung V5 and V20 values, and demographics were collected. Linear regression analyses identified factors associated with MHD and MLD and factors associated with clinical outcomes.ResultsA total of 88 patients were included. The median MHD for HFRT and CFRT was 1.99 Gy and 2.94 Gy, respectively. The median MLD for HFRT and CFRT was 6.00 Gy and 10.08 Gy, respectively. MHD and MLD were significantly associated with the occurrence of a cardiac or pulmonary event post-radiation. Patients who had a mastectomy or tumoral muscle involvement were more likely to develop a local recurrence, metastasis or new primary while patients who had a lumpectomy or tumor with a positive estrogen receptor status were less likely to experience these events.ConclusionsFurther investigation should be conducted to identify SBBC RT techniques that mitigate dose to OARs to improve clinical outcomes in bilateral breast patients.     

Dosimetric Impact of Image-Guided Radiotherapy in Liver Stereotactic Radiotherapy

IntroductionIn the absence of volumetric image-guided radiotherapy (IGRT) with or without intravenous contrast, IGRT with two-dimensional (2D) imaging can improve the accuracy and precision of radiation delivery by correcting the largest sources of geometric uncertainty, facilitating the delivery of higher doses to the tumor and/or reduced doses to normal tissues. The purpose of this work was to estimate dosimetric impact of 2D IGRT for patients undergoing breath hold liver stereotactic body radiotherapy (SBRT).Materials/MethodsOffline residual offsets were determined using orthogonal image pairs acquired with patients positioned with external setup marks (non-IGRT) and following IGRT and repositioning (IGRT) for 30 patients treated with 6-fraction liver SBRT. The diaphragm was used as a surrogate for the liver for craniocaudal positioning, and the vertebral bodies for anterioposterior and right-left positioning, with a 3-mm threshold. The planned dose distributions were shifted by the measured IGRT and non-IGRT offsets. Total doses to target volumes and organs at risk (OAR) were calculated and compared to the prescribed plans.ResultsA total of 643 images (416-MV electronic portal images; 227 kV cone beam computed tomography projection images) were evaluated. Residual non-IGRT offsets frequently exceeded 3 mm (72%), resulting in clinically significant variations from the prescribed minimum planning target volume dose (mean change –6.5 Gy; P =.0150). The population mean reductions in minimum gross tumor volume doses (standard deviation (σ) to 0.5 mL with were 7.2 Gy (6.3) and 4.7 Gy (6.1) for non-IGRT and IGRT, respectively. The mean population increase in maximum OAR dose (to 0.5 mL) was largest for bowel (2.7 Gy, σ = 5.5 Gy) for non-IGRT.ConclusionsIGRT significantly improves concordance of delivered doses with planned doses for liver target volumes and OARs.     

Stability of Intracavitary Applicator Placement for HDR Brachytherapy of Cervix Cancer

Introduction/BackgroundCervical cancer is often treated with a combination of external beam radiation therapy and high-dose-rate intracavitary brachytherapy. An intrauterine ring and tandem applicator is used for intracavitary brachytherapy. The dose is prescribed to the high-risk clinical target volume. The goals of this study were to investigate the stability of intracavitary applicator placement during patient transfer and to evaluate the dosimetric impact of displacement.MethodsFourteen patients with cervical cancer were analyzed. Three sets of orthogonal fluoroscopic radiographs were obtained in the high-dose-rate suite after the insertion and before treatment: pre–computed tomography (CT) fluoroscopic radiograph with patient in the lithotomy position, pre-CT fluoroscopic radiograph with patient in the legs down position, and post-CT fluoroscopic radiograph with patient in the legs down position. Applicator position after CT was compared with the pre-CT radiographs to determine if the position changed during patient transfer. The displacement was measured in the anterior-posterior, medio-lateral, and superior-inferior directions, as well as the degree of pitch, roll, and yaw. To study the impact of applicator shifts on dose to organs at risk (OARs), the ring and tandem applicator was shifted virtually in the BrachyVision treatment planning system. The OARs studied included the small bowel, sigmoid colon, rectum, and bladder. Five millimeter shifts were made in the superior-inferior, medio-lateral, and anterior-posterior direction. Three degree rotations were made in the pitch, yaw, and roll directions. Applicator shifts were analyzed in only one direction at a time. The dosimetric impact on OARs was evaluated by comparing the original and shifted/rotated plans to dose-volume histogram–based criteria.ResultsThe average displacements were 1.9 ± 0.5 mm laterally, 3.0 ± 0.6 mm longitudinally, and 9.5 ± 1.5 mm anterior-posterior. The average applicator rotation on the posterior-anterior radiograph was 1.0 ± 0.2° and 2.6 ± 0.6° on the lateral radiograph. Five millimeter anterior-posterior shifts had the greatest effect on dose to OARs. On average, 5 mm anterior shifts had the greatest effect on the small bowel dose, where there was a 13.7% (79.6 cGy) increase in D2cc. Five millimeter anterior shifts also affected bladder dose, with a 36.5% (141.1 cGy) increase in D2cc. Five millimeter POST shifts increased the rectal D2cc by 28.6% (168.7 cGy). Other directional shifts had negligible effects on dose. The largest effect on OAR dose arising from rotations was to the sigmoid colon, when the applicator rotated in the POST pitch direction. As a result, the dose increased by 4.7% (7.6 cGy). All other rotations had minimal impact on OAR doses.ConclusionPatient transfer resulted in applicator shifts and rotations that had a measurable effect on dose to OARs. The displacements were the result of either a direct shift or rotation of the applicator. Additional tracking of these shifts and rotations may clarify the sources of these unwanted motions and suggest possible mitigation strategies.     

残差U-net与先验知识协同模型自动勾画宫颈癌术后临床靶区和危及器官北大核心CSCD

目的搭建残差U-net(RU)网络与先验知识协同(RPKC)自动勾画模型,评估其自动勾画宫颈癌术后患者临床靶区(CTV)和危及器官(OAR)的准确性。方法基于48例(训练集)宫颈癌术后定位CT训练RPKC模型。以临床医师勾画的CTV及OAR为标准,采用戴斯相似系数(DSC)和第95百分位豪斯多夫距离(HD95)评估RPKC模型与RU模型勾画另20例宫颈癌术后患者(测试集)CTV及OAR(包括肠袋、直肠、膀胱、骨盆及双侧股骨头)的准确性。结果RPKC模型自动勾画上述结构的DSC均高于RU模型,其中CTV及肠袋勾画效果差异有统计学意义(P均<0.05);除直肠外,RPKC模型自动勾画的HD95均低于RU模型,二者勾画CTV效果差异差异有统计学意义(P<0.05)。结论RPKC模型能更准确地勾画宫颈癌术后CTV和OAR,有助于提高深度学习自动勾画的临床实用性。     

FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods.     

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.     

引入靶区外扩预测放射治疗中危及器官自动分割的平均剂量偏差

目的 观察引入靶区外扩预测放射治疗(放疗)中自动分割危及器官(OAR)的平均剂量偏差的价值。方法 将100例接受放疗的直肠癌患者随机分为训练集(n=30)和测试集(n=70)。对训练集手动分割CT图中的靶区,之后分别对膀胱、小肠和双侧股骨头4个OAR进行手动和自动分割。根据自动分割的OAR设计放疗计划,得到对应的剂量分布;利用Python程序统计每个OAR与靶区外扩环重叠区域内的剂量平均值,以之作为代表剂量,用于预测测试集手动与自动分割平均剂量的差异,比较预测平均剂量与实际平均剂量的差异。再次随机将100例分为训练集、测试集各50例,重复上述过程。结果 首次预测显示,测试集70例中,69例膀胱预测与实际剂量差异均<0.5 Gy,69例小肠预测与实际剂量差异均<3 Gy,全部70例双侧股骨头预测与实际剂量差异均<0.5 Gy;对于膀胱、小肠和左、右侧股骨头,预测与实际平均剂量差异的一致性相关系数(CCC)分别为0.96、0.86、0.81和0.69。第2次预测显示,测试集50例中,46例膀胱的预测与实际剂量差异均<0.5 Gy,49例小肠的预测和实际剂量差异均<3 Gy,所有病例双侧股骨头的预测和实际剂量差异均<0.5 Gy;对于膀胱、小肠和左、右侧股骨头,预测与实际平均剂量差异的CCC分别为0.97、0.90、0.82和0.78。结论 引入靶区外扩可有效预测直肠癌放疗中自动分割OAR产生的剂量偏差。     

Comparing Breast Conservation Surgery Seromas Contoured by Radiation Therapists versus those Contoured by a Radiation Oncologist in Radiation Therapy Planning for Early-Stage Breast Cancer

IntroductionIn the management of early-stage breast cancer using radiation therapy, computed tomography (CT) simulation is used to identify the breast conservation surgery (BCS) seroma as a proxy for the tumour bed. The delineation or contouring of the seroma is generally a task performed by a radiation oncologist (RO). With increasing patient numbers and other demands placed on ROs, the scope of practice for radiation therapists (RTs) is continually expanding, and the need for skills transfer from one profession to another has been investigated in recent years.This study aims to compare the BCS seroma volumes contoured by RTs with those contoured by ROs to add evidence in support of expanding the RTs' role in the treatment planning process in the management of early-stage breast cancer.MethodsA study was undertaken using the CT-simulation (CT-sim) data sets of patients with early-stage breast cancer treated in 2013. The CT-sim data sets had BCS seromas contoured by 1 of 5 ROs as part of routine clinical management. This study involved 4 RTs who each used the patient information to identify and contour breast seromas on 50 deidentified CT-sim data sets. Metrics used to compare RT versus RO contours included volume size, overlap between volumes, and geographical distance from the centre of volumes.ResultsThere were 50 CT-sim data sets with 1 RO contour and 4 RT contours analysed. The contour volumes of the 4 RTs and the ROs were assessed. Although there were 50 CT-sim data sets presented to each RT, analysis was carried out on 45, 43, 46, and 45 CT-sim data sets. There were no comparisons made where contours were not delineated. The contour volumes of the 4 RTs and the ROs were assessed with an interclass correlation coefficient, with a result of excellent reliability (0.975, 95% [0.963, 0.985]). The DICE similarity coefficient was used to compare the overlap of each RT contour with the RO contour; the results were favourable with mean (95% CI) DSCs 0.685, 0.640, 0.678, and 0.681, respectively. Comparing the RT and RO geographical centre of the seroma volumes, good to excellent reliability between the RTs and ROs was demonstrated (95% CI mean RO vs RT distances (mm): 3.75, 4.99, 7.71, and 3.39). There was no statistically significant difference between the distances (P = 0.65).ConclusionBCS seromas contoured by RTs compared well with those contoured by an RO. This research has provided further evidence to support RTs in assuming additional contouring responsibilities in radiation therapy planning for patients with early-stage breast cancer.     

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy

In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has been surgically removed, so the CTV encompasses the microscopic spread of tumor cells, which cannot be visualized in clinical images like computed tomography or magnetic resonance imaging. In current clinical practice, physicians’ segment CTVs manually based on their relationship with nearby organs and other clinical information, but this allows large inter-physician variability. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has yielded suboptimal results. We propose using deep learning to accurately segment post-operative prostate CTVs. The model proposed is trained using labels that were clinically approved and used for patient treatment. To segment the CTV, we segment nearby organs first, then use their relationship with the CTV to assist CTV segmentation. To ease the encoding of distance-based features, which are important for learning both the CTV contours’ overlap with the surrounding OARs and the distance from their borders, we add distance prediction as an auxiliary task to the CTV network. To make the DL model practical for clinical use, we use Monte Carlo dropout (MCDO) to estimate model uncertainty. Using MCDO, we estimate and visualize the 95% upper and lower confidence bounds for each prediction which informs the physicians of areas that might require correction. The model proposed achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout test dataset, much better than established methods, such as atlas-based methods (DSC<0.7). The predicted contours agree with physician contours better than medical resident contours do. A reader study showed that the clinical acceptability of the automatically segmented CTV contours is equal to that of approved clinical contours manually drawn by physicians. Our deep learning model can accurately segment CTVs with the help of surrounding organ masks. Because the DL framework can outperform residents, it can be implemented practically in a clinical workflow to generate initial CTV contours or to guide residents in generating these contours for physicians to review and revise. Providing physicians with the 95% confidence bounds could streamline the review process for an efficient clinical workflow as this would enable physicians to concentrate their inspecting and editing efforts on the large uncertain areas.     

Asymmetric multi-task attention network for prostate bed segmentation in computed tomography images

Post-prostatectomy radiotherapy requires accurate annotation of the prostate bed (PB), i.e., the residual tissue after the operative removal of the prostate gland, to minimize side effects on surrounding organs-at-risk (OARs). However, PB segmentation in computed tomography (CT) images is a challenging task, even for experienced physicians. This is because PB is almost a “virtual” target with non-contrast boundaries and highly variable shapes depending on neighboring OARs. In this work, we propose an asymmetric multi-task attention network (AMTA-Net) for the concurrent segmentation of PB and surrounding OARs. Our AMTA-Net mimics experts in delineating the non-contrast PB by explicitly leveraging its critical dependency on the neighboring OARs (i.e., the bladder and rectum), which are relatively easy to distinguish in CT images. Specifically, we first adopt a U-Net as the backbone network for the low-level (or prerequisite) task of the OAR segmentation. Then, we build an attention sub-network upon the backbone U-Net with a series of cascaded attention modules, which can hierarchically transfer the OAR features and adaptively learn discriminative representations for the high-level (or primary) task of the PB segmentation. We comprehensively evaluate the proposed AMTA-Net on a clinical dataset composed of 186 CT images. According to the experimental results, our AMTA-Net significantly outperforms current clinical state-of-the-arts (i.e., atlas-based segmentation methods), indicating the value of our method in reducing time and labor in the clinical workflow. Our AMTA-Net also presents better performance than the technical state-of-the-arts (i.e., the deep learning-based segmentation methods), especially for the most indistinguishable and clinically critical part of the PB boundaries. Source code is released at https://github.com/superxuang/amta-net.     

CT图像中肺实质的自动分割

目的 为解决肺实质分割中肺部结节及高密度血管易遗漏的问题,提出一种自动肺实质分割方法.方法 首先利用二维区域生长反操作、连通区域判别等方法提取肺实质区域;然后利用行扫描法定位肺区边界点;最后通过对边界点参数分析,定位受肿瘤侵占的边界点,利用曲线拟合修复受损边界.结果 通过对多组胸部CT图像的分割,验证了算法的有效性;与几种常见边界修复算法对比,验证了行扫描边界修复算法的优越性.结论 本文提出的算法能将肿瘤包含到肺实质区域,确保分割的完整性、准确性、实时性.     

区域生长法结合多竞争最小二乘拟合算法去除乳腺X线摄影图像中胸大肌影

目的 评价区域生长法结合多竞争最小二乘拟合算法去除数字乳腺X线摄影（MG）图像中胸大肌影的价值。方法 分层抽样法随机抽取244例MG数据,对图像进行轮廓选择、增强数据特征、胸大肌边界轮廓粗定位和去噪处理;结合最小二乘法改进区域生长法,拟合胸大肌的边界轮廓函数,使用最优轮廓函数制作胸大肌掩膜图,计算预测图与人工勾画图交并比（IOU）及像素精度（PA）,评价其去除MG图像中的胸大肌影的价值。结果 基于上述方法所获胸大肌轮廓较为平滑,较少漏分割或过度分割,结果误差较小;还原胸大肌边界轮廓与手动分割结果非常接近,平均IOU为（89.76±4.28）%,平均PA为（89.98±3.91）%。结论 结合区域生长法与多竞争最小二乘拟合算法可用于去除MG图像中的胸大肌影。     

Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method’s clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.     

Three‐Dimensional Region‐Based Segmentation for Breast Tumors on Sonography

Because malignant and benign breast tumors show different shapes and sizes on sonography, information about tumor shapes and sizes is important for clinical diagnosis. Since sonograms include noise and tissue texture, accurate clinical diagnosis is highly dependent on clinical experience and expertise. However, manually sketching a 3‐dimensional (3D) breast tumor contour is a time‐consuming and complicated task. Automatic contouring, which provides a contour similar to that of manual sketching of a breast tumor on sonography, may improve diagnostic accuracy. This study presents an efficient method for automatically detecting 3D contours of breast tumors on 3D sonography. The proposed method applies a voxel nearest neighbor filter, a Wiener filter, and an unsharp filter to enhance contrast and reduce noise. After a 3D region‐growing algorithm is used to obtain the contour of the breast tumor, postprocessing of the extracted contour is performed to diminish the shadow region of the tumor. This study evaluated 20 tumor cases comprising 10 benign and 10 malignant cases. The results of computer simulation reveal that the proposed 3D segmentation method provides robust contouring for breast sonograms. This approach consistently obtains contours similar to those obtained by manual contouring of a breast tumor and can reduce the time needed to sketch precise contours.     

Boundary loss for highly unbalanced segmentation

Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions. This can mitigate the difficulties of highly unbalanced problems because it uses integrals over the interface between regions instead of unbalanced integrals over the regions. Furthermore, a boundary loss complements regional information. Inspired by graph-based optimization techniques for computing active-contour flows, we express a non-symmetric L₂ distance on the space of contours as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations and comparisons on different unbalanced problems, showing that our boundary loss can yield significant increases in performances while improving training stability. Our code is publicly available¹.     

Evaluation of the dosimetric influence of interfractional 6D setup error in hypofractionated prostate cancer treated with IMRT and VMAT using daily kV-CBCT

IntroductionProstate cancer is one of the most common malignant tumors in men and is usually treated with advanced intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). Significant uncorrected interfractional 6-Dimensional setup errors could impact the delivered dose. The aim of this study was to assess the dosimetric impact of 6D interfractional setup errors in hypofractionated prostate cancer using daily kilovoltage cone-beam computed tomography (kV-CBCT).MethodsThis retrospective study comprised twenty prostate cancer patients treated with hypofractionated IMRT (8) and VMAT (12) with daily kV-CBCT image guidance. Interfraction 6D setup errors along lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 400 CBCTs. For targets and organs at risk (OARs), the dosimetric impact of rotational error (R_Error), translational error (T_Error), and translational plus rotational error (T+R_Error) were evaluated on kV-CBCT images.ResultsThe single fraction maximum T_Error ranged from 12–20 mm, and the R_Error ranged from 2.8⁰–3.0⁰. The maximum mean absolute dose variation ΔD in D_98% (dose to 98% volume) of CTV-55 and PTV-55 was -0.66±0.82 and -5.94±3.8 Gy, respectively, in the T+R_Error. The maximum ΔD (%) for D_98% and D_0.035cc in CTV-55 was -4.29% and 2.49%, respectively, while in PTV-55 it was -24.9% and 2.36%. The mean dose reduction for D_98% in CTV-55 and D_98% and D_95% in PTV-55 was statistically significant (p<0.05) for T_Error and T+R_Error. The mean dose variation for D_mean and D_50% in the rectum was statistically significant (p<0.05) for T_Error and T+R_Error.ConclusionThe uncorrected interfractional 6D setup error results in significant target underdosing and OAR overdosing in prostate cancer. This emphasizes the need to correct interfractional 6D setup errors daily in IMRT and VMAT.     

Automated Delineation of the Normal Urinary Bladder on Planning CT and Cone Beam CT

PurposeTo quantify the volumetric effect of delineation variability when using manual versus semiautomated tools to contour the normal bladder on planning computed tomography (CT) and cone beam CT.MethodsFollowing research ethics board approval, 10 prostate cancer patients were selected. For each patient, one pretreatment cone beam CT (CBCT) was randomly selected from the first treatment week and registered to the planning CT (planCT). Model-based auto adaptation was used to delineate the outer bladder (OB) surface for the planCT. That contour was then propagated and manually adapted onto the CBCT. A second observer delineated OB for the planCT and CBCT using typical manual methods. These delineation procedures were repeated four times on each image set, with observers blinded to the previous contours. Metrics of volumetric, geometric, and overlap concordance were used to compare the manual and automated OB contours.ResultsThe mean pairwise difference between the manual and model-based planCT volumes was 4 cm³ (2%), and the model-based contours exhibited approximately half the observer variation of the manual ones (3 cm³, 2%). The mean of pairwise differences between the manual and propagated CBCT volumes was 13 cm³ (8%), but the propagated contours exhibited approximately half the observer related volume variation (11 cm³, 6%). Small CBCT bladder volumes displayed larger observer variation with manual methods (r², −0.640). Variability between the automated contours was significantly smaller than for the corresponding manual observations (P = .004 and .002, respectively). Metrics of three-dimensional overlap concordance indicated excellent agreement within and between the delineation methods. Automated CBCT contours were significantly smoother than the manual ones (surface sphericity index, 1.29 vs. 1.35; P = .03).ConclusionsVolumetric, geometric, and overlap metrics all indicated that planCT and CBCT automated OB contours fell within the range of manually delineated contours. The CBCT propagated contours were significantly smoother and associated with smaller intraobserver variability, compared with manual contours. Importantly, the findings from this research suggest that contour propagation may be more robust than manual delineation, especially in the presence of poor image quality.     

The Stacked-Ellipse Algorithm: An Ultrasound-Based 3-D Uterine Segmentation Tool for Enabling Adaptive Radiotherapy for Uterine Cervix Cancer

The stacked-ellipse (SE) algorithm was developed to rapidly segment the uterus on 3-D ultrasound (US) for the purpose of enabling US-guided adaptive radiotherapy (RT) for uterine cervix cancer patients. The algorithm was initialised manually on a single sagittal slice to provide a series of elliptical initialisation contours in semi-axial planes along the uterus. The elliptical initialisation contours were deformed according to US features such that they conformed to the uterine boundary. The uterus of 15 patients was scanned with 3-D US using the Clarity System (Elekta Ltd.) at multiple days during RT and manually contoured (n = 49 images and corresponding contours). The median (interquartile range) Dice similarity coefficient and mean surface-to-surface-distance between the SE algorithm and manual contours were 0.80 (0.03) and 3.3 (0.2) mm, respectively, which are within the ranges of reported inter-observer contouring variabilities. The SE algorithm could be implemented in adaptive RT to precisely segment the uterus on 3-D US.     

Evaluation of the AAA Treatment Planning Algorithm for SBRT Lung Treatment: Comparison with Monte Carlo and Homogeneous Pencil Beam Dose Calculations

PurposeTo evaluate the dose calculation accuracy of the Varian Eclipse anisotropic analytical algorithm (AAA) for stereotactic body radiation therapy (SBRT), and to investigate the dosimetric consequences of not applying tissue heterogeneity correction on complex SBRT lung plans.Materials and MethodsNine cases of non–small-cell lung cancer (NSCLC) that were previously treated with SBRT at our center were selected for this study. Following Radiation Therapy Oncology Group 0236, the original plans were calculated using pencil beam without heterogeneity correction (PBNC). For this study, these plans were recalculated by applying tissue heterogeneity correction with the AAA algorithm and with the Monte Carlo (MC) method, keeping the number of monitor units the same as the original plans. Two kinds of plan comparison were made. First, the AAA calculations were compared with MC. Second, the treatment plans that were calculated with AAA were compared with the original PBNC calculations. The following dose-volume parameters were used for the comparison: V_100%; V_90%; the maximum, the minimum, and the mean planning target volume (PTV) doses (D_max, D_min, and D_mean, respectively); V_20Gy, V_15Gy, V_10Gy, V_5Gy; D_mean for the lung; and D_max for the critical organs.ResultsComparable results were obtained for AAA and MC calculations: except for Dmax, Dmin, and Dmean, the differences in the patient-average values of all of the PTV dose parameters were less than 2%. The largest average difference was observed for Dmin (3.8 ± 5.4%). Average differences in all the lung dose parameters were under 0.2%, and average differences in normal tissue Dmax were under 0.3 Gy, except for the skin dose. There were appreciable differences in the PTV and normal tissue dose-volume parameters when comparing AAA and PBNC calculations. Except for V_100% and V_90%, PBNC calculations on average underestimated the dose to the PTV. The largest discrepancy was in the PTV maximum dose, with a patient-averaged difference of 11.1 ± 4.6%.ConclusionsBased on our MC investigation, we conclude that the Eclipse AAA algorithm is sufficiently accurate for dose calculations of lung SBRT plans involving small 6-MV photon fields. Our results also demonstrate that, although dose calculations at the periphery of the PTV showed good agreement when comparing PBNC with both AAA and MC calculations, there is a potential to significantly underestimate the dose inside the PTV and doses to critical structures if tissue heterogeneity correction is not applied to lung SBRT plans.     

胰腺癌不同调强方式的剂量学比较

  目的  比较胰腺癌术后患者固定野调强放疗(fixed-field intensity-modulated radiotherapy, FF-IMRT)与容积调强放疗(volumetric modulated arc therapy, VMAT)的剂量学差异, 为临床选择合适的照射技术提供参考。  方法  2011年6月至12月在北京协和医院行放疗的10例胰腺癌术后患者, 分别根据其同一CT模拟定位图像设计FF-IMRT计划和VMAT计划, 处方剂量50 Gy/25次。分析剂量体积直方图曲线, 评估靶区、危及器官和正常组织的剂量分布, 并比较二者机器跳数(monitor units, MU)和治疗时间的差别。  结果  FF-IMRT计划和VMAT计划的靶区剂量分布差异无统计学意义(P > 0.05)。与FF-IMRT计划相比, VMAT计划中肝脏、胃、小肠、全身的V₅明显升高(P均 < 0.05), 而肝脏的V₁₀和V₂₀, 胃的V₁₀, 小肠的V₁₀、V₂₀、V₅₀, 左肾的V₂₀, 右肾的V₂₀、V₃₀、D_mean、D_max, 以及全身的V₁₀、V₂₀有不同程度下降(P均 < 0.05), 脊髓的D_max升高1.85 Gy(P=0.04)。FF-IMRT计划与VMAT计划的MU分别为619.60±117.18和492.70±51.56(t=3.18, P=0.01)。VMAT计划的MU较FF-IMRT计划减少了20.48%。  结论  胰腺癌患者选择VMAT计划, 可以在不降低计划水平上的剂量分布的前提下, 大大减少MU, 缩短治疗时间。