Knowledge-Based Statistical Inference Method for Plan Quality Quantification.
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AIM:The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. METHODS AND MATERIALS:We propose a knowledge-based plan quality inference method that references to similar plans in the historical database for patient-specific plan quality evaluation. First, a novel plan similarity metric with high-dimension geometrical difference quantification is utilized to retrieve similar plans. Subsequently, dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics-dosimetric result probability and dose deviation index-are proposed to quantify plan quality among prior similar plans. To evaluate the performance of the proposed method, we exported 927 clinically approved head and neck treatment plans. Eight organs at risk, including brain stem, cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid, were analyzed. Twelve suboptimal plans identified by dosimetric result probability were replanned to validate the capability of the proposed methods in identifying inferior plans. RESULTS:After replanning, left and right parotid median doses are reduced by 31.7% and 18.2%, respectively; 83% of these cases would not be identified as suboptimal without the proposed similarity plan selection. Analysis of population plan quality reveals that average parotid sparing has been improving significantly over time (21.7% dosimetric result probability reduction from year 2006-2007 to year 2016-2017). Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the 2 planning targets, revealing the collective trend in planning conventions. CONCLUSIONS:The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.
Published Version (Please cite this version)10.1177/1533033819857758
Publication InfoZhang, Jiang; Wu, Q Jackie; Ge, Yaorong; Wang, Chunhao; Sheng, Yang; Palta, Jatinder; ... Zhang, Jiahan (2019). Knowledge-Based Statistical Inference Method for Plan Quality Quantification. Technology in cancer research & treatment, 18. pp. 1533033819857758. 10.1177/1533033819857758. Retrieved from https://hdl.handle.net/10161/19369.
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Professor of Radiation Oncology
Development of novel radiation treatment techniques for patients with limited metastatic disease, and integration of these treatments with systemic therapies. Treatment of head and neck cancers with chemotherapy, radiation and novel targeted drugs. Improving outcomes for medically inoperable patients with lung cancer with new radiation methods. Developing predictors for toxicity of patients treated with chemotherapy and radiation therapy for non-small cell and small cell lung cancer.</do
Assistant Professor of Radiation Oncology
Deep learning methods for image-based radiotherapy outcome prediction and assessment Machine learning in outcome modelling Automation in radiotherapy planning and delivery
Professor of Radiation Oncology
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.