Knowledge-Based Statistical Inference Method for Plan Quality Quantification.


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.





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Publication Info

Zhang, Jiang, Q Jackie Wu, Yaorong Ge, Chunhao Wang, Yang Sheng, Jatinder Palta, Joseph K Salama, Fang-Fang Yin, et al. (2019). Knowledge-Based Statistical Inference Method for Plan Quality Quantification. Technology in cancer research & treatment, 18. p. 1533033819857758. 10.1177/1533033819857758 Retrieved from

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Qingrong Wu

Professor of Radiation Oncology

Chunhao Wang

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


Yang Sheng

Assistant Professor of Radiation Oncology

My research interest focuses on machine learning and AI application in radiation oncology treatment planning, including prostate cancer, head-and-neck cancer and pancreatic cancer etc.


Joseph Kamel Salama

Professor of Radiation Oncology

I have the privilege to be the Chief of the Durham VA Radiation Oncology Service, where I care for veterans who have served our country. I am a dedicated educator, serving as the Residency Program Director for the Duke Radiation Oncology Residency Program.  I am also a cancer researcher developing novel treatment techniques for patients with head and neck cancer, lung cancer, prostate cancer, and those limited metastatic disease, and integration of these treatments with drug therapies. 


Fang-Fang Yin

Gustavo S. Montana Distinguished Professor of 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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