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

Frontiers in Oncology

卷:8

Fang-Fang Yin¹  Q. Jackie Wu¹  Jiahan Zhang¹  Tianyi Xie¹  Yang Sheng¹  Yaorong Ge² 

[1] Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States;

[2] Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States;

关键词: treatment planning;    dose volume histogram prediction;    regression model;    machine learning;    ensemble model;    statistical modeling;   

DOI  :  10.3389/fonc.2018.00057

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown