Knowledge‐based radiation treatment planning: A data‐driven method survey

Momin, Shadab; Fu, Yabo; Lei, Yang; Roper, Justin; Bradley, Jeffrey D.; Tian, Lei; Yang, Xiaofeng

doi:10.1002/acm2.13337

Cited by 51 publications

(44 citation statements)

References 210 publications

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“…The quality of the obtained plan is in line with other authors findings for KB models [23] , [35] , [36] .…”

Section: Discussionsupporting

confidence: 90%

“…To do so, RP required an inverse-planned modality plan as input in the training phase. For this reason, mock rotational VMAT (Volumetric Modulated Arc Therapy) plans were generated using the geometry arrangement of the ViTAT (Virtual Tangential-fields Arc Therapy) technique introduced in previous works [30] , [35] . In short, four arcs were used, ranging from 300° to 135°, with collimator angles equal to ±5° and ±10°.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy

Esposito¹,

Castriconi²,

Mangili³

et al. 2022

Physics and Imaging in Radiation Oncology

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“…The quality of the obtained plan is in line with other authors findings for KB models [23] , [35] , [36] .…”

Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy

Esposito¹,

Castriconi²,

Mangili³

et al. 2022

Physics and Imaging in Radiation Oncology

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“…Therefore, they can use all information available in the images. Over the past few years, deep learning techniques have rapidly grown and demonstrated remarkable performance with successful implementations in many fields, including radiation therapy 12,28–30 …”

Section: Introductionmentioning

confidence: 99%

“…Over the past few years, deep learning techniques have rapidly grown and demonstrated remarkable performance with successful implementations in many fields, including radiation therapy. 12,[28][29][30] Compared with IMRT planning of other anatomical sites, head-and-neck treatment planning is one of the most challenging sites requiring a high level of knowledge, human clinical expertise, and effort to produce high-quality plans. These challenging aspects are due to the large size of the PTV, multiple prescription dose levels that are simultaneously integrated boosted, and the presence of several critical OARs nearby the PTV.…”

Section: Introductionmentioning

confidence: 99%

Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

Osman

2022

J Applied Clin Med Phys

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Purpose Deep learning–based knowledge‐based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high‐quality plans. This paper presents a novel KBP model using an attention‐gating mechanism and a three‐dimensional (3D) U‐Net for intensity‐modulated radiation therapy (IMRT) 3D dose distribution prediction in head‐and‐neck cancer. Methods A total of 340 head‐and‐neck cancer plans, representing the OpenKBP—2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention‐gated 3D U‐Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean‐squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U‐Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning‐based dose prediction models. Results The proposed attention‐gated 3D U‐Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground‐truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U‐Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U‐Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U‐Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U‐Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U‐Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U‐Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U‐Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U‐Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U‐Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the Dmax${D_{max}}$ (brainstem, spinal cord, and mandible) and Dmean${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground‐truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground‐truth plans. The proposed model needs ...

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“…We expand on the impact of these contributions throughout this paper. Comparing the quality of competing KBP models from the research community is difficult because the vast majority of research is conducted with large private datasets, as noted in several reviews [6]- [9]. To help address this issue, the Open Knowledge-Based Planning (OpenKBP) Grand Challenge was organized to facilitate the largest international effort to date for developing and comparing dose prediction models on a single open dataset [10].…”

Section: Introductionmentioning

confidence: 99%

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Babier,

Mahmood,

Zhang

et al. 2022

Preprint

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We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dosevolume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P <0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt.

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Knowledge‐based radiation treatment planning: A data‐driven method survey

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 51 publications

References 210 publications

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy

Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

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