Knowledge-Based Statistical Inference Method for Plan Quality Quantification.

Zhang, Jiang

Wu, Q Jackie

Ge, Yaorong

Wang, Chunhao

Sheng, Yang

Palta, Jatinder

Salama, Joseph K

Yin, Fang-Fang

Zhang, Jiahan

2019-10-01T14:09:49Z

2019-10-01T14:09:49Z

2019-01

2019-10-01T14:09:47Z

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.

1533-0346

1533-0338

https://hdl.handle.net/10161/19369

eng

SAGE Publications

Technology in cancer research & treatment

10.1177/1533033819857758

data analytics

knowledge-based planning

plan quality assurance

Knowledge-Based Statistical Inference Method for Plan Quality Quantification.

Journal article

Sheng, Yang|0000-0003-3380-1966

Salama, Joseph K|0000-0001-5159-4687

Yin, Fang-Fang|0000-0002-2025-4740|0000-0003-1064-2149

1533033819857758

School of Medicine

Duke

Duke Cancer Institute

Institutes and Centers

Radiation Oncology

Clinical Science Departments

Duke Kunshan University Faculty

Duke Kunshan University

Published

18