MED-PHAICVFeb 16, 2022

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

arXiv:2202.08303v130 citationsHas Code
Originality Synthesis-oriented
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This work provides a reproducible benchmark for radiotherapy planning, addressing a domain-specific need for clinicians and researchers, though it is incremental in building on existing KBP models.

The authors tackled the problem of evaluating knowledge-based planning (KBP) pipelines in radiotherapy by establishing an open framework with 100 patient cases and 76 pipelines, finding that KBP-generated plans significantly outperformed input predictions on 18 of 23 criteria and satisfied a higher percentage of criteria than reference plans.

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 dose-volume 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 KBP-generated 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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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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