An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning.
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 articleSubject
dose volume histogram predictionensemble model
machine learning
regression model
statistical modeling
treatment planning
Permalink
https://hdl.handle.net/10161/19374Published Version (Please cite this version)
10.3389/fonc.2018.00057Publication 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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Show full item recordScholars@Duke
Qingrong Wu
Professor of Radiation Oncology
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
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