DVHnet: A deep learning‐based prediction of patient‐specific dose volume histograms for radiotherapy planning

Chen, Xinyuan; Men, Kuo; Zhu, Ji; Yang, Bingning; Li, Minghui; Liu, Zhiqiang; Yan, Xingchen; Yi, Junlin; Dai, Jianrong

doi:10.1002/mp.14758

Cited by 15 publications

(10 citation statements)

References 36 publications

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“…Lately, automation, machine learning, and AI techniques have been introduced in the radiation therapy community and many systems are commercially available.Soon these techniques are expected to touch all aspects of the radiation therapy workflow: contouring, treatment planning, QA, and treatment delivery with the goal of improving quality, efficiency, and consistency. [31][32][33][34][35][36][37][38] Varian Ethos is one such commercial system that has introduced automation in different steps of radiation therapy. In this study, we evaluated the efficacy of the Ethos IOE algorithm and its ability to auto-generate clinically acceptable plans for prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Pokharel

Pacheco²,

Tanner³

2022

J Applied Clin Med Phys

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Varian Ethos, a new treatment platform, is capable of automatically generating treatment plans for initial planning and for online, adaptive planning, using an intelligent optimization engine (IOE). The primary purpose of this study is to assess the efficacy of Varian Ethos IOE for auto‐planning and intercompare different treatment modalities within the Ethos treatment planning system (TPS). A total of 36 retrospective prostate and proximal seminal vesicles cases were selected for this study. The prescription dose was 50.4 Gy in 28 fractions to the proximal seminal vesicles, with a simultaneous integrated boost of 70 Gy to the prostate gland. Based on RT intent, three treatment plans were auto‐generated in the Ethos TPS and were exported to the Eclipse TPS for intercomparison with the Eclipse treatment plan. When normalized for the same planning target volume (PTV) coverage, Ethos plans Dmax% were 108.1 ± 1.2%, 108.4 ± 1.6%, and 109.6 ± 2.0%, for the 9‐field IMRT, 12‐field IMRT, and 2‐full arc VMAT plans, respectively. This compared well with Eclipse plan Dmax% values, which was 108.8 ± 1.4%. OAR indices were also evaluated for Ethos plans using Radiation Therapy Oncology Group report 0415 as a guide and were found to be comparable to each other and the Eclipse plans. While all Ethos plans were comparable, we found that, in general, the Ethos 12‐field IMRT plans met most of the dosimetric goals for treatment. Also, Ethos IOE consistently generated dosimetrically hotter VMAT plans versus IMRT plans. On average, Ethos TPS took 13 min to generate 2‐full arc VMAT plans, compared to 5 min for 12‐field IMRT plans. Varian Ethos TPS can generate multiple treatment plans in an efficient time frame and the quality of the plans could be deemed clinically acceptable when compared to manually generated treatment plans.

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Section: Discussionmentioning

confidence: 99%

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Pokharel

Pacheco²,

Tanner³

2022

J Applied Clin Med Phys

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“…Multiple CNN‐based methods have been identified for their good forecasting ability; however, they cannot learn global semantic data because of the convolution kernel's receptive field limitations [106,107] . Initially, Transformer was proposed for resolving sequence modeling problems and was widely used in the field of natural language processing [18] .…”

Section: The Roles Of Transformers In the Prediction Of Voxel‐level D...mentioning

confidence: 99%

“…Multiple CNN-based methods have been identified for their good forecasting ability; however, they cannot learn global semantic data because of the convolution kernel's receptive field limitations. [106,107] Initially, Transformer was proposed for resolving sequence modeling problems and was widely used in the field of natural language processing. [18] It employs a self-attention mechanism to determine sequence correlations, thereby overcoming the limitations of RNN model, which includes a lack of long-distance dependency identification.…”

Section: The Roles Of Transformers In the Prediction Of Voxel-level D...mentioning

confidence: 99%

Potential roles of transformers in brain tumor diagnosis and treatment

Lan

Zou

Qin

et al. 2023

Brain-X

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Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self‐attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology‐based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel‐level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real‐time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer‐based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.

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“…The former depends on a high‐quality database, and involves complex artificial features such as overlap volume histogram (OVH) and distance to target histogram (DTH), which are limited to the quality of the clinical delivery plan 11 . To overcome these problems, some convolution neural networks (CNNs) were proposed for DVH prediction to completely avoid manually extracting features, greatly increasing calculation costs 12,13 . Besides, the predicted DVH curves lack the spatial information of dose distributions.…”

Section: Introductionmentioning

confidence: 99%

“…11 To overcome these problems, some convolution neural networks (CNNs) were proposed for DVH prediction to completely avoid manually extracting features, greatly increasing calculation costs. 12,13 Besides, the predicted DVH curves lack the spatial information of dose distributions. In contrast, the latter completely eliminates the possibility of human intervention and achieves voxel-level dose prediction which can reflect spatial distribution characteristics.…”

Section: Introductionmentioning

confidence: 99%

Deep learning architecture with transformer and semantic field alignment for voxel‐level dose prediction on brain tumors

et al. 2022

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The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise voxel-level dose prediction on brain tumors. Methods: A dataset of 120 patients with brain tumors is built for the retrospective study. The dose distributions are predicted by a designed end-to-end model called TS-Net, in which the transformer encoder module is utilized to obtain abundant global features by learning long-range correlations of the input sequence. In addition, semantic field alignment (SFA) block is proposed in decoding path to ensure effective propagation of strong semantic information from deep to shallow. Five images from different channels are fed into the architecture, including a computed tomography (CT) image, a planning target volumes (PTV) image, an organs-at-risk (OARs) image, a beam configuration image, and a distance image, and the predicted dose distributions are taken as outputs. We use different evaluation metrics to evaluate the performance of the model and discuss the role of the auxiliary beam configuration information provided by non-modulated dose distributions. Results:The TS-Net prediction accuracies in terms of mean absolute error (MAE) are 2.98% for PTV, 7.19% for brainstem, 1.88% for left len, 2.48% for right len, 9.61% for left optic nerve, 9.10% for right optic nerve, 8.99% for optic chiasma, and 8.28% for pituitary. There is no statistically significant difference between the predicted results and clinical dose distributions for clinical indexes including homogeneity index (HI), D50, and D95 for PTV; V40, mean dose, and max dose for OARs; except for conformation index (CI) and D2 for PTV. The model has dice similarity coefficient (DSC) values of above 0.91 for most isodose volumes, clearly outperforming HD U-Net, and being slightly better than U-Net and DCNN. Conclusion:The proposed TS-Net with beam configuration input can achieve accurate voxel-level dose prediction for brain tumors, and is a usable tool for improving the efficiency and quality of radiotherapy. K E Y W O R D S brain tumors, dose prediction, transformer, semantic field alignment Jinna Yang and Fan Zhang contributed equally to this study.

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DVHnet: A deep learning‐based prediction of patient‐specific dose volume histograms for radiotherapy planning

Cited by 15 publications

References 36 publications

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Potential roles of transformers in brain tumor diagnosis and treatment

Deep learning architecture with transformer and semantic field alignment for voxel‐level dose prediction on brain tumors

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