BackgroundAccurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability.This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer.MethodsOn-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures.ResultsThe atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively.ConclusionsIn this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.
Purpose: The purpose of this study was to develop automated planning for wholebrain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes. Methods: A dataset of 55 cases, including 40 training sets, five validation sets, and 10 test sets, was used to predict the static MLC shape. The digitally reconstructed radiograph (DRR) reconstructed from planning CT images as an input layer and the MLC shape as an output layer are connected one-to-one via the U-net modeling. The Dice similarity coefficient (DSC) was used as the loss function in the training and ninefold cross-validation. Dose-volume-histogram (DVH) curves were constructed for assessing the automatic MLC shaping performance. Deep-learning (DL) and manually optimized (MO) approaches were compared based on the DVH curves and dose distributions.Results: The ninefold cross-validation ensemble test results were consistent with DSC values of 94.6 ± 0.4 and 94.7 ± 0.9 in training and validation learnings, respectively. The dose coverages of 95% target volume were (98.0 ± 0.7)% and (98.3 ± 0.8)%, and the maximum doses for the lens as critical organ-at-risk were 2.9 Gy and 3.9 Gy for DL and MO, respectively. The DL technique shows the consistent results in terms of the DVH parameter except for MLC shaping prediction for dose saving of small organs such as lens.Conclusions: Comparable with the MO plan result, the WBRT plan quality obtained using the DL approach is clinically acceptable. Moreover, the DL approach enables WBRT auto-planning without the time-consuming manual MLC shaping and target contouring.whole-brain radiation therapy, deep learning, automatic planning | INTRODUCTIONMetastatic brain cancer is the most common type of intracranial tumor. 1 From common primary cancer sites such as lung, breast, and melanoma, brain metastasis occurs in 15% to 40% of the cancer patients. 2,3 The treatment of metastatic brain tumors depends on the number of metastatic tumors, extracranial tumor status, and performance status. [4][5][6] Currently, whole-brain radiation therapy (WBRT) is considered a well-established treatment for patients with multiple brain metastases.
The 2D in vivo rectal dosimetry using the ERB with EBT3 films was effective and might be clinically applicable for HDR brachytherapy for cervical and prostate cancers to monitor treatment accuracy and consistency as well as to predict rectal toxicity.
When a high density metallic implant is placed in the path of the proton beam, spatial heterogeneity can be caused due to artifacts in three dimensional (3D) computed tomography (CT) scans. These artifacts result in range uncertainty in dose calculation in treatment planning system (TPS). And this uncertainty may cause significant underdosing to the target volume or overdosing to normal tissue beyond the target. In clinical cases, metal implants must be placed in the beam path in order to preserve organ at risk (OARs) and increase target coverage for tumors. So we should introduce Ti-mesh. In this paper, we measured the lateral dose profile for proton beam using an EBT3 film to confirm dosimetric impact of Ti-mesh when the Ti-mesh plate was placed in the proton beam pathway. The effect of Ti-mesh on the proton beam was investigated by comparing the lateral dose profile calculated from TPS with the film-measured value under the same conditions.
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