Volumetric Modulated Arc Therapy Dose Research Articles

Influence of Respiratory Motion on Dose Distribution in Gastric Mucosa-associated Lymphoid Tissue Lymphoma Radiotherapy.

The present study investigated the effect of respiratory motion on planned radiotherapy (RT) dose for gastric mucosa-associated lymphoid tissue (MALT) lymphoma using four-dimensional dose (4D-dose) accumulation. 4D-computed tomography (4D-CT) images of 10 patients with gastric MALT lymphomas were divided into 10 respiratory phases. Further, the 3D-dose was calculated using 3D conformal RT (3D-CRT) and volumetric modulated arc therapy (VMAT) plans based on the average intensity projection (AIP) images. Then, both plans were recalculated according to each phase image. Moreover, the dose distributions in each phase were transferred to the AIP images using deformable image registration. The 4D-dose distribution was calculated by summing the doses of each phase, and it was compared with the dosimetric parameters of the 3D-dose distribution. For 3D-CRT, the D95 and D99 of the 4D-dose in the planning target volume (PTV) were significantly lower than those of the 3D-dose, with mean differences of 0.2 (p=0.009) and 0.1 Gy (p=0.021), respectively. There were no significant differences in the other PTV and organ-at-risk dosimetric parameters of 3D-CRT or in any dosimetric parameters of VMAT between the 3D- and 4D-dose distributions. The effect of respiratory motion on the planned 3D-CRT and VMAT dose distributions for gastric MALT lymphoma is minimal and clinically negligible.

Comparison of Dose Distribution to Target Volume and Organs at Risk using 3D CRT, IMRT, and VMAT Techniques in Glioblastoma Cases

The average lifetime risk of secondary cancer after irradiating glioblastoma radiation therapy to healthy organs shows that the technique of volumetric modulated arc therapy (VMAT) has a smaller risk of developing secondary cancer compared to intensity modulated radiation therapy (IMRT) and three-dimensional radiation therapy (3DCRT). Parameters of dose distribution, namely the target volume and dose received by the organs at risk (OAR), are used to compare these three techniques. The distribution of doses from TPS results in the form of DVH and isodose in the 3D CRT, IMRT, and VMAT techniques shows that the distribution of doses to healthy organs around the glioblastoma irradiation area is in the safe category and is still within tolerance limits. With VMAT, the PTV and CTV dose results were more optimal compared to the 3D CRT and IMRT techniques. 3D CRT showed a PTV dose value of 5551.8 cGy and a CTV of 5515.3 cGy. IMRT shows a PTV dose value of 6035.0 cGy and a CTV of 6018.8 cGy. VMAT shows PTV dose values of 6101.8 cGy and CTV of 6044.7 cGy. It can be seen that the distribution of doses to healthy organs in general in the VMAT technique is more optimal than the IMRT and 3D CRT techniques in protecting OAR.

Sub-second photon dose prediction via transformer neural networks.

Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity andspeed. We present a deep learning algorithm that, exploiting synergies between transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds. The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local features of the data. We train iDoTA on a dataset of 1700 beam dose distributions, using 11 clinical volumetric modulated arc therapy (VMAT) plans (from prostate, lung, and head and neck cancer patients with 194-354 beams per plan) to assess its accuracy andspeed. iDoTA predicts individual photon beams in ≈50 ms with a high gamma pass rate of (2 mm, 2%). Furthermore, estimating full VMAT dose distributions in 6-12 s, iDoTA achieves state-of-the-art performance with a (2 mm, 2%) pass rate and an average relative dose error of 0.75 ± 0.36%. Offering the millisecond speed prediction per beam angle needed in online and real-time adaptive treatments, iDoTA represents a new state of the art in data-driven photon dose calculation. The proposed model can massively speed-up current photon workflows, reducing calculation times from few minutes to just a fewseconds.

An ultra-fast deep-learning-based dose engine for prostate VMAT via knowledge distillation framework with limited patient data

Objective. Deep-learning (DL)-based dose engines have been developed to alleviate the intrinsic compromise between the calculation accuracy and efficiency of the traditional dose calculation algorithms. However, current DL-based engines typically possess high computational complexity and require powerful computing devices. Therefore, to mitigate their computational burdens and broaden their applicability to a clinical setting where resource-limited devices are available, we proposed a compact dose engine via knowledge distillation (KD) framework that offers an ultra-fast calculation speed with high accuracy for prostate Volumetric Modulated Arc Therapy (VMAT). Approach. The KD framework contains two sub-models: a large pre-trained teacher and a small to-be-trained student. The student receives knowledge transferred from the teacher for better generalization. The trained student serves as the final engine for dose calculation. The model input is patient computed tomography and VMAT dose in water, and the output is DL-calculated patient dose. The ground-truth \\dose was computed by the Monte Carlo module of the Monaco treatment planning system. Twenty and ten prostate cases were included for model training and assessment, respectively. The model’s performance (teacher/student/student-only) was evaluated by Gamma analysis and inference efficiency. Main results. The dosimetric comparisons (input/DL-calculated/ground-truth doses) suggest that the proposed engine can effectively convert low-accuracy doses in water to high-accuracy patient doses. The Gamma passing rate (2%/2 mm, 10% threshold) between the DL-calculated and ground-truth doses was 98.64 ± 0.62% (teacher), 98.13 ± 0.76% (student), and 96.95 ± 1.02% (student-only). The inference time was 16 milliseconds (teacher) and 11 milliseconds (student/student-only) using a graphics processing unit device, while it was 936 milliseconds (teacher) and 374 milliseconds (student/student-only) using a central processing unit device. Significance. With the KD framework, a compact dose engine can achieve comparable accuracy to that of a larger one. Its compact size reduces the computational burdens and computing device requirements, and thus such an engine can be more clinically applicable.

A Dosimetric Analysis of Modified Volumetric Modulated Arc Therapy for Bone Marrow Sparing Radiotherapy in Cervical Cancer-An alternative Approach to Conventional VMAT

External beam radiotherapy remains the primary treatment modality in cervical cancer. Nowadays Intensity Modulated Radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are increasingly being used to reduce normal tissue toxicity. The drawback of conventional VMAT is that a considerable volume of pelvic bone marrow receives a low dose. We analyzed whether there was a way to reduce the volume of the low dose regions of bone marrow, and assessed the potential benefit of conventional-4Arc (C-4Arc VMAT), and Modified-4Arc (M-4Arc VMAT) over the conventional 2 ARC VMAT. Twelve clinically proven locally advanced cervical cancer patients treated with concurrent chemo-radiotherapy by Conventional VMAT (RapidArc) in dual rotation mode (C-2Arc VMAT) were selected for this study.C-4Arc VMAT and M-4Arc VMAT dose plans were generated for these twelve patients and these three different types of plans were evaluated for the quality and compared dosimetrically. M-4Arc VMAT designs exhibited a greater bone marrow sparing when compared with conventional VMATs with respect to volume receiving 5Gy to 35Gy without compromising PTV dose coverage. M-4Arc VMAT plans, the bone marrow volume receiving 30 Gy (V30Gy),40Gy (V40Gy), and mean doses were lower than the C- 4 Arc plan and a similar result was observed for V50(Gy) also when comparing with the standard 2 Arc plan. In modified VMAT plans, the rectum and bladder dose volumes were lower than standard VMAT. Similarly, the bowel bag V35(Gy), V40(Gy), V50(Gy), mean doses. The right and left femoral head doses were reduced significantly when compared to conventional VMAT plans. The M-4Arc VMAT plans are better than the C-2Arc and C-4Arc VMAT plans for reducing the dose to bone marrow by limiting the MLC field width travel.

A polar-coordinate-based pencil beam algorithm for VMAT dose computation with high-resolution gantry angle sampling.

Most commercially available treatment planning systems (TPSs) approximate the continuous delivery of volumetric modulated arc therapy (VMAT) plans with a series of discretized static beams for treatment planning, which can make VMAT dose computation extremely inefficient. In this study, we developed a polar-coordinate-based pencil beam (PB) algorithm for efficient VMAT dose computation with high-resolution gantry angle sampling that can improve the computational efficiency and reduce the dose discrepancy due to the angular under-sampling effect. 6 MV pencil beams were simulated on a uniform cylindrical phantom under an EGSnrc Monte Carlo (MC) environment. The MC-generated PB kernels were collected in the polar coordinate system for each bixel on a fluence map and subsequently fitted via a series of Gaussians. The fluence was calculated using a detectors' eye view with off-axis and MLC transmission factors corrected. Doses of VMAT arc on the phantom were computed by summing the convolution results between the corresponding PB kernels and fluence for each bixel in the polar coordinate system. The convolution was performed using fast Fourier transform to expedite the computing speed. The calculated doses were converted to the Cartesian coordinate system and compared with the reference dose computed by a collapsed cone convolution (CCC) algorithm of the TPS. A heterogeneous phantom was created to study the heterogeneity corrections using the proposed algorithm. Ten VMAT arcs were included to evaluate the algorithm performance. Gamma analysis and computation complexity theory were used to measure the dosimetric accuracy and computational efficiency, respectively. The dosimetric comparisons on the homogeneous phantom between the proposed PB algorithm and the CCC algorithm for 10 VMAT arcs demonstrate that the proposed algorithm can achieve a dosimetric accuracy comparable to that of the CCC algorithm with average gamma passing rates of 96% (2%/2mm) and 98% (3%/3mm). In addition, the proposed algorithm can provide better computational efficiency for VMAT dose computation using a PC equipped with a 4-core processor, compared to the CCC algorithm utilizing a dual 10-core server. Moreover, the computation complexity theory reveals that the proposed algorithm has a great advantage with regard to computational efficiency for VMAT dose computation on homogeneous medium, especially when a fine angular sampling rate is applied. This can support a reduction in dose errors from the angular under-sampling effect by using a finer angular sampling rate, while still preserving a practical computing speed. For dose calculation on the heterogeneous phantom, the proposed algorithm with heterogeneity corrections can still offer a reasonable dosimetric accuracy with comparable computational efficiency to that of the CCC algorithm. We proposed a novel polar-coordinate-based pencil beam algorithm for VMAT dose computation that enables a better computational efficiency while maintaining clinically acceptable dosimetric accuracy and reducing dose error caused by the angular under-sampling effect. It also provides a flexible VMAT dose computation structure that allows adjustable sampling rates and direct dose computation in regions of interest, which makes the algorithm potentially useful for clinical applications such as independent dose verification for VMAT patient-specific QA.

Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer

Radiotherapy treatment planning (RTP) is time-consuming and labor-intensive since medical physicists must devise treatment plans carefully to reduce damage to tissues and organs for patients. Previously, we proposed the volume-based algorithm (VBA) method, providing optimal partial arcs (OPA) angle to achieve the low-dose volume of lungs in dynamic arc radiotherapy. This study aimed to implement the VBA for esophageal cancer (EC) patients and compare the lung dose and delivery time between full arcs (FA) without using VBA and OPA angle using VBA in volumetric modulated arc therapy (VMAT) plans. We retrospectively included 30 patients diagnosed with EC. RTP of each patient was replanned to 4 VMAT plans, including FA plans without (FA-C) and with (FA + C) dose constraints of OARs and OPA plans without (OPA-C) and with (OPA + C) dose constraints of OARs. The prescribed dose was 45 Gy. The OARs included the lungs, heart, and spinal cord. The dose distribution, dose-volume histogram, monitor units (MUs), delivery time, and gamma passing rates were analyzed. The results showed that the lung V5 and V10 in OPA + C plans were significantly lower than in FA + C plans (p < 0.05). No significant differences were noted in planning target volume (PTV) coverage, lung V15, lung V20, mean lung dose, heart V30, heart V40, mean heart dose, and maximal spinal cord dose between FA + C and OPA + C plans. The delivery time was significantly longer in FA + C plans than in OPA + C plans (237 vs. 192 s, p < 0.05). There were no significant differences between FA + C and OPA + C plans in gamma passing rates. We successfully applied the OPA angle based on the VBA to clinical EC patients and simplified the arc angle selection in RTP. The VBA could provide a personalized OPA angle for each patient and effectively reduce lung V5, V10, and delivery time in VMAT.

Investigation of Halcyon multi-leaf collimator model in Eclipse treatment planning system: A focus on the VMAT dose calculation with the Acuros XB algorithm.

PurposeThe dual‐layer multi‐leaf collimator (MLC) in Halcyon involves further complexities in the dose calculation process, because the leaf‐tip transmission varies according to the leaf trailing pattern. For the volumetric modulated arc therapy (VMAT) treatment, the prescribed dose for the target volume can be sensitive to the leaf‐tip transmission change. This report evaluates the dosimetric consequence due to the uncertainty of the dual‐layer MLC model in Eclipse through the dose verifications for clinical VMAT. Additionally, the Halcyon leaf‐tip model is empirically adjusted for the VMAT dose calculation with the Acuros XB.Materials and methodsFor this evaluation, an in‐house program that analyzes the leaf position in each layer was developed. Thirty‐two clinical VMAT plans were edited into three leaf sequences: dual layer (original), proximal single layer, or distal single layer. All leaf sequences were verified using Delta4 according to the dose difference (DD) and the global gamma index (GI). To improve the VMAT dose calculation accuracy, the dosimetric leaf gap (DLG) was adjusted to minimize the DD in single‐layer leaf sequences.ResultsThe mean of DD were −1.35%, −1.20%, and −1.34% in the dual‐layer, proximal single‐layer, and distal single‐layer leaf sequences, respectively. The changes in the mean of DD between leaf sequences were within 0.2%. However, the calculated doses differed from the measured doses by approximately 1% in all leaf sequences. The tuned DLG was increased by 0.8 mm from the original DLG in Eclipse. When the tuned DLG was used in the dose calculation, the mean of DD neared 0% and GI with a criterion of 2%/2 mm yielded a pass rate of more than 98%.ConclusionNo significant change was confirmed in the dose calculation accuracy between the leaf sequences. Therefore, it is suggested that the dosimetric consequence due to the leaf trailing was negligibly small in clinical VMAT plans. The DLG tuning for Halcyon can be useful for reducing the dose calculation uncertainties in Eclipse VMAT and required in the commissioning for Acuros XB.

Difference in VMAT dose distribution for prostate cancer with/without rectal gas removal and/or adaptive replanning.

We investigated differences in the volumetric-modulated arc therapy (VMAT) dose distribution in prostate cancer patients treated by rectal gas removal and/or adaptive replanning. Cone-beam computed tomography (CBCT) scans were performed daily for 22 treatments in eight prostate cancer patients with excessive rectal gas, and the CBCT images were analyzed. Rectal gas removal was performed, and irradiation was delivered after prostate matching. We compared dose-volume histograms for the daily CBCT images before and after rectal gas removal. Plan A was the original plan on CBCT images before rectal gas removal. Plan B was a single reoptimized plan on CBCT images before rectal gas removal. Plan C was the original plan on CBCT images after rectal gas removal. Plan D was a single reoptimized plan on CBCT images after rectal gas removal. D95 of the planning target volume (PTV) minus the rectum of Plan C (94.7% ± 6.6%) was significantly higher than that of Plan A (88.5% ± 10.4%). All dosimetric parameters of Plan C were improved by rectal gas removal compared with Plan A, regardless of the initial rectal gas volume. Dosimetric parameters of PTV minus the rectum of Plan B were significantly improved compared with Plan C. Additionally, the V78 of the rectal wall of Plan B (0.2% ± 0.5%) was significantly improved compared with Plan C (3.9% ± 6.3%, p = 0.003). The dosimetric parameters of Plan D were not significantly different from Plan B. The dose distribution of prostate VMAT was improved by rectal gas removal and/or adaptive replanning. An adaptive replanning on daily CBCT images might be a better method than rectal gas removal for prostate cancer patients with excessive rectal gas.

VMAT dose prediction in radiotherapy by using progressive refinement UNet

The prediction of dose distribution in volumetric modulated arc therapy (VMAT) plays a curial role in radiotherapy. Accurate VMAT dose prediction for prostate cancer is always a great challenge because of the complexity of the VMAT dose distributions and the low contrast of the organs and tissues in the male pelvic CT images. In this paper, a novel progressive refinement UNet (PRUNet) with rank loss is proposed to address the aforementioned problem. On the one hand, the proposed PRUNet extends the traditional UNet with a novel progressive refinement module to generate more realistic dose distributions. The progressive refinement module generates multiple dose predictions with different resolutions in one forward pass and refines the dose prediction with predicted details at finer levels from lower resolution to higher resolution. On the other hand, it turns out that the dosimetric metrics are sensitive to the order relation among the dose values in dose distributions. A new rank loss function is proposed to optimize the similarity between the order relations among the dose values in dose predictions and that in real dose distributions. The proposed PRUNet is trained in a multi-task framework to jointly learn the dose distribution and the order relation among dose values. To evaluate the performance of the proposed PRUNet, the VMAT dose distributions and anatomical information of 64 prostate cancer patients are collected. The proposed model generates more accurate dose distributions with better quantitative dosimetric metrics than the state of the art UNet models do.

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning.

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model.

Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions for prostate cancer patients. A modified U-Net was trained on triplets, a combination of three consecutive image slices and corresponding segmentations, from 160 patients, and compared to a baseline U-Net. Dose predictions from 17 test patients were transformed into deliverable treatment plans using a novel planning workflow. The model achieved a mean absolute dose error of 1.3%, 1.9%, 1.0% and ≤ 2.6% for clinical target volume (CTV) CTV_D100%, planning target volume (PTV) PTV_D98%, PTV_D95% and organs at risk (OAR) respectively, when compared to the clinical ground truth (GT) dose distributions. All predicted distributions were successfully transformed into deliverable treatment plans and tested on a phantom, resulting in a passing rate of 100% (global gamma, 3%, 2mm, 15% cutoff). The dose difference between deliverable treatment plans and GT dose distributions was within 4.4%. The difference between the baseline model and our improved model was statistically significant (p<0.05) for CVT_D100%, PTV_D98% and PTV_D95%. Triplet-based training improved VMAT dose distribution predictions when compared to 2D. Dose predictions were successfully transformed into deliverable treatment plans using our proposed treatment planning procedure. Our method may automate parts of the workflow for external beam prostate radiation therapy and improve the overall treatment speed and plan quality.

On the reduction of aperture complexity in kidney SABR.

BackgroundStereotactic ablative body radiotherapy (SABR) of primary kidney cancers is confounded by motion. There is a risk of interplay effect if the dose is delivered using volumetric modulated arc therapy (VMAT) and flattening filter‐free (FFF) dose rates due to target and linac motion. This study aims to provide an efficient way to generate plans with minimal aperture complexity.MethodsIn this retrospective study, 62 patients who received kidney SABR were reviewed. For each patient, two plans were created using internal target volume based motion management, on the average intensity projection of a four‐dimensional CT. In the first plan, optimization was performed using a knowledge‐based planning model based on delivered clinical plans in our institution. In the second plan, the optimization was repeated, with a maximum monitor unit (MU) objective applied in the optimization. Dose‐volume, conformity, and complexity metric (with the field edge metric and the modulation complexity score) were compared between the two plans. Results are shown in terms of median (first quartile — third quartile).ResultsSimilar dosimetry was obtained with and without the utilization of an objective on the MU. However, complexity was reduced by using the objective on the MUs (modulation complexity score = 0.55 (0.50–0.61) / 0.33 (0.29–0.36), P‐value < 10−10, with/without the MU objective). Reduction of complexity was driven by a larger aperture area (area aperture variability = 0.68 (0.64–0.73) / 0.42 (0.37–0.45), P‐value < 10−10, with/without the MU objective). Using the objective on the MUs resulted in a more spherical dose distribution (sphericity 50% isodose = 0.73 (0.69–0.75) / 0.64 (0.60–0.68), P‐value < 10−8, with/without the MU objective) reducing dose to organs at risk given respiratory motion.ConclusionsAperture complexity is reduced in kidney SABR by using an objective on the MU delivery with VMAT and FFF dose rate.

Angular dependence of the optically stimulated luminescent dosimeter

Objective To study the angular dependence of optically stimulated luminescent dosimeter (OSLD) with solid phantoms under SSDL radiation level 60Co radiation field, and to discuss the possibility of OSLD in volumetric modulated arc therapy (VMAT) and other rotating irradiation dose audit. Methods OSLDs were embedded in the two phantoms with the same size and material, respectively. The phantom 1 was set to make the first OSLD perpendicular to the beam, and the phantom 2 was set to make the second OSLD parallel to the beam. The OSLDs were irradiated at 8 angles: 0°, 45°, 90°, 135°, 180°, –45°, –90, –135°. The counts of the OSLSs were read and the response of each angle which normalized to 0° were calculated. Results When the OSLDs are perpendicular to the beam, the angular response is between –6.76% 〜+1.5%, with the maximum angular dependence at 90° and –90°. When the OSLDs are parallel to the beam, the angular response is between –1.74%〜+1.67%, below 2%. Conclusion It is better to correct the sensitivity of dosimeters by Element Correction Factors (ECF) for dose audit. Under the condition of rotating irradiation, OSLD shoud be set parallel to the beam, which can better reduce the influence of angular dependence and facilitate further application research of VMAT dose audit. 摘要： 目的 结合定制固体模体研宄光致发光剂量计(OSLD)在放疗水平 60Co标准辐射场中的角响应性能, 探讨OSLD在容积旋转调强放射治疗(VMAT)等旋转照射剂量核查中应用的可行性。 方法 将OSLD嵌入2个外观尺寸以 及材料均相同的定制固体模体, 模体1使OSLD与射线垂直, 模体2使OSLD与射线平行, 照射8个角度：0°、45°、 90°、135°、180°、–45°、–90°和–135°, 读取每个角度的OSLD计数, 计算每个角度相对于0°的角响应值。 结果 当OSLD 垂直于射线照射时, 角响应在–6.76%〜+1.5%, 90°和–90°角响应最大;当OSLD平行于射线照射时, 角响应在–1.74%〜 +1.67%, 均在± 2%以内。 结论 在实际工作中, 应先计算元件校正因子(ECF), 校正各剂量计对射线的灵敏度, 旋转 照射条件下, 将OSLD平行于射线入射方向放置, 能更好地降低角响应影响, 便于进一步开展VMAT剂量核查应用研宄。

Incidence and dosimetric predictive factors of late rectal toxicity after low-dose-rate brachytherapy combined with volumetric modulated arc therapy in high-risk prostate cancer at a single institution: Retrospective study

PurposeThe purpose of this study is to investigate the incidence of rectal toxicity and to identify the associated dosimetric predictive parameters after I-125 seed low-dose-rate brachytherapy (LDR-BT) combined with volumetric modulated arc therapy (VMAT) and dose constraints. Methods and MaterialsIn total, 110 patients with high-risk prostate cancer received 110 Gy LDR-BT, followed by 45 Gy VMAT. Rectal toxicity was recorded according to Common Terminology Criteria for Adverse Events v.4.03. The dosimetric factors associated with LDR-BT and VMAT were analyzed to determine their relationship with rectal toxicity. Receiver operating characteristic (ROC) curve analysis was performed for ≥ grade 2 (G2) rectal toxicity prediction. ResultsThe follow-up duration was 10.1–115.2 months (median 60.5 months). Seven patients had G2 rectal hemorrhage, and none of the patients had grade 3 rectal hemorrhage. In the univariate analysis, the rectal volume receiving 100% of the prescribed dose (rV100) (p < 0.001), the dose covering 2 cc of the rectum (rD2cc) during LDR-BT (p = 0.002), and the combined rD2cc during LDR-BT and VMAT (p = 0.001) were identified as predictors of G2 rectal hemorrhage. In the ROC curve analysis, the cutoff value was 0.46 cc for rV100, 74.0 Gy for rD2cc, and 86.8 GyEQD2 for combined rD2cc. ConclusionPredictors of late ≥ G2 rectal hemorrhage are rV100, rD2cc, and combined rD2cc. The incidence of rectal toxicity is low and acceptable in this setting and is highly dependent on the rectal dose of LDR-BT. The use of higher-quality LDR-BT and VMAT dose constraints may further reduce the rate of rectal hemorrhage.

Dosimetric comparison between interstitial brachytherapy and volumetric-modulated arc therapy for tumor bed boost in breast cancer.

PurposeTo dosimetrically compare high-dose-rate interstitial brachytherapy (HDR-BT) with volumetric-modulated arc therapy (VMAT) for tumor bed boost, following breast conservative treatment.Material and methods50 patients with early-stage breast cancer who underwent breast conservation surgery, followed by either HDR-BT (n = 25) of 15 Gy in 6 fractions over a period of 3 days, or VMAT dose of 16 Gy in 8 fractions (n = 25) for tumor bed boost, were retrospectively reviewed. All patients received whole breast irradiation of 46 Gy in 23 fractions. Dosimetric parameters for organs at risk (OARs), including ipsilateral and contralateral lungs, heart, contralateral breast, skin, and ribs, were evaluated with the help of dose-volume histograms (DVH).ResultsHeart sparing was similar in both modalities (left-sided breast irradiation, HDR-BT D2cc 20.5% vs. VMAT 30.2%, p-value = 0.243; right-sided breast irradiation, D2cc 6.5% vs. 4.4%, p-value = 0.165). Left-sided cases received higher dose to heart compared to right-sided patients. Interstitial brachytherapy resulted in significantly less dose to contralateral breast (D2cc 4.3% vs. 9.6%, p-value < 0.0001), ipsilateral lung (D2cc 27.6% vs. 73.2%, p-value < 0.0001), contralateral lung (D2cc 4.2% vs. 14.5%, p-value < 0.0001), ribs (D2cc 24.1% vs. 41.2%, p-value < 0.0001), and skin (D2cc 77.3% vs. 95%, p-value < 0.0001).ConclusionsHDR-BT-based tumor bed boost irradiation results in significantly lower doses to most organs at risk with similar heart sparing compared to VMAT.

Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours

ObjectiveDose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. Material and methodsData from 80 patients with neuroblastoma or Wilms’ tumour was included. Pencil beam scanning (PBS) (5 mm/ 3%) and volumetric-modulated arc therapy (VMAT) plans (5 mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation.The networks’ performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔDmean = DVMAT-DPBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔDmean. ResultsAverage DVH differences between planned and predicted dose distributions were ≤ |6%| for both modalities. The networks classified the organs-at-risk Dmean difference as a gain (ΔDmean > 0) with 98% precision. An identical modality selection based on planned compared to predicted ΔDmean was made for 18/20 patients. ConclusionDeep learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualisation might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable.

Comparison of dose distributions in target areas and organs at risk in conformal and VMAT techniques and dose verifications with the use of thermoluminescence dosimetry

Abstract The aim of the present study is to compare dose distributions and their verification in target areas and organs at risk (OAR) in conformal and volumetric modulated arc therapy (VMAT) techniques. Proper verification procedures allow the removal of the major sources of errors, such as incorrect application of a planning system, its insufficient or cursory commissioning, as well as an erroneous interpretation of the obtained results. Three target areas (head and neck, chest, and pelvic) were selected and the treatment was delivered based on plans made using collapsed cone convolution and Monte Carlo algorithms with 6-MV photon beams, adopting conformal and VMAT techniques, respectively. All the plans were prepared for the anthropomorphic phantom. Dose measurements were performed with TL detectors made of LiF phosphor doped with magnesium and titanium (LiF:Mg,Ti). This paper presents the results of TL measurements and calculated doses, as well as their deviations from the treatment planning system (TPS) in the three planned target areas. It was established that the algorithms subject to analysis differ, particularly in dose calculations for highly inhomogeneous regions (OAR). Aside from the need to achieve the dose intended for the tumour, the choice of irradiation technique in teleradiotherapy should be dictated by the degree of exposure to individual critical organs during irradiation. While nothing deviated beyond the bounds of what is acceptable by international regulatory bodies in plans from TPS, clinically one must be more cautious with the OAR areas.

Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

PurposeTo assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours. Materials and methodsThe study was conducted on 66 paediatric patients with Wilms’ tumour or neuroblastoma (age 4 ± 2 years) who underwent MR and computed tomography (CT) acquisition on the same day as part of the clinical protocol. MRI intensities were converted to CT Hounsfield units (HU) by means of a UNet-like neural network trained to generate synthetic CT (sCT) from T1- and T2-weighted MR images. The CT-to-sCT image similarity was evaluated by computing the mean error (ME), mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and Dice similarity coefficient (DSC). Synthetic CT dosimetric accuracy was verified against CT-based dose distributions for volumetric-modulated arc therapy (VMAT) and intensity-modulated pencil-beam scanning (PBS). Relative dose differences (Ddiff) in the internal target volume and organs-at-risk were computed and a three-dimensional gamma analysis (2 mm, 2%) was performed. ResultsThe average ± standard deviation ME was −5 ± 12 HU, MAE was 57 ± 12 HU, PSNR was 30.3 ± 1.6 dB and DSC was 76 ± 8% for bones and 92 ± 9% for lungs. Average Ddiff were <0.5% for both VMAT (range [−2.5; 2.4]%) and PBS (range [−2.7; 3.7]%) dose distributions. The average gamma pass-rates were >99% (range [85; 100]%) for VMAT and >96% (range [87; 100]%) for PBS. ConclusionThe deep learning-based model generated accurate sCT from planning T1w- and T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for paediatric patients with abdominal tumours.

Effects of gas-filled temporary breast tissue expanders on radiation dose from modulated rotational photon beams

Gas-filled temporary tissue expanders (TTEs), implanted to assist in post mastectomy breast reconstructions, are expected to produce increased dosimetric uncertainty in breast radiotherapy treatments, due to their containing both a substantial metallic component and a comparatively large volume of gas. This study therefore builds on previous investigations of the dosimetric effects of gas-filled TTEs in static photon and electron beams, by examining the effects of these implants on dose distributions from common modulated rotational treatment techniques; volumetric modulated arc therapy (VMAT) and helical tomotherapy (HT). Radiochromic film measurements were used to evaluate the accuracy of VMAT and HT dose calculations, for a humanoid phantom augmented with a sample Aeroform CO2-filled TTE (AirXpanders Inc, San Jose, USA) as well as purpose-designed and 3D printed “breast tissue.” Results showed that the TomoTherapy Hi-Art VoLO convolution-superposition algorithm (Accuray Inc, Sunnyvale, USA) produced comparatively accurate calculations of treatment dose within this complex phantom, including immediately anterior and posterior to the TTE. The Varian Eclipse Acuros (AXB) algorithm generally showed better agreement with the film measurement than the Varian Eclipse AAA algorithm (Varian Medical Systems, Palo Alto, USA), although the film measurements showed regions of 5% to 10% disagreement with both AAA and AXB in the dosimetrically-challenging region on the anterior side of the implant. Although the Aeroform CO2-filled TTE has substantial and obvious effects on the downstream dose from a static photon beam, the results of this study showed how inverse-planning of modulated rotational radiotherapy treatments can produce modulated fluence distributions that compensate for the dramatic density heterogeneities in the implant. Despite some disagreements with the planned dose, all film measurements showed that the use of inverse-planned modulated rotational photon beams resulted in comparatively homogeneous coverage of the radiotherapy target, in the complex patient-like phantom with a gas-filled TTE. Due to the importance of matching each planned fluence distribution to the density distribution within each TTE, careful use of available 3D imaging techniques is advisable, when modulated rotational radiotherapy treatments are delivered to patients with gas-filled TTEs.