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An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning.

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Date
2018-01
Authors
Zhang, Jiahan
Wu, Q Jackie
Xie, Tianyi
Sheng, Yang
Yin, Fang-Fang
Ge, Yaorong
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Abstract
Knowledge-based planning (KBP) utilizes experienced planners' knowledge embedded in prior plans to estimate optimal achievable dose volume histogram (DVH) of new cases. In the regression-based KBP framework, previously planned patients' anatomical features and DVHs are extracted, and prior knowledge is summarized as the regression coefficients that transform features to organ-at-risk DVH predictions. In our study, we find that in different settings, different regression methods work better. To improve the robustness of KBP models, we propose an ensemble method that combines the strengths of various linear regression models, including stepwise, lasso, elastic net, and ridge regression. In the ensemble approach, we first obtain individual model prediction metadata using in-training-set leave-one-out cross validation. A constrained optimization is subsequently performed to decide individual model weights. The metadata is also used to filter out impactful training set outliers. We evaluate our method on a fresh set of retrospectively retrieved anonymized prostate intensity-modulated radiation therapy (IMRT) cases and head and neck IMRT cases. The proposed approach is more robust against small training set size, wrongly labeled cases, and dosimetric inferior plans, compared with other individual models. In summary, we believe the improved robustness makes the proposed method more suitable for clinical settings than individual models.
Type
Journal article
Subject
dose volume histogram prediction
ensemble model
machine learning
regression model
statistical modeling
treatment planning
Permalink
https://hdl.handle.net/10161/19374
Published Version (Please cite this version)
10.3389/fonc.2018.00057
Publication Info
Zhang, Jiahan; Wu, Q Jackie; Xie, Tianyi; Sheng, Yang; Yin, Fang-Fang; & Ge, Yaorong (2018). An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning. Frontiers in oncology, 8(MAR). pp. 57. 10.3389/fonc.2018.00057. Retrieved from https://hdl.handle.net/10161/19374.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Wu

Qingrong Wu

Professor of Radiation Oncology
Yin

Fang-Fang Yin

Professor in Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics
Alphabetical list of authors with Scholars@Duke profiles.
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