title: A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.
abstract: OBJECTIVE
  To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation.
  
  
  METHODS
  Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information.
  
  
  RESULTS
  DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy approximately 80% in prediction and effectiveness in improving ART planning quality.
  
  
  CONCLUSIONS
  An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.
authors: Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27708, USA. xiaofeng_zhu@med.unc.edu
authors: Xiaofeng
authors: X
authors: Zhu
authors: Yaorong
authors: Y
authors: Ge
authors: Taoran
authors: T
authors: Li
authors: Danthai
authors: D
authors: Thongphiew
authors: Fang-Fang
authors: FF
authors: Yin
authors: Q Jackie
authors: QJ
authors: Wu
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008297:Male; D009929:Organ Size; D025341:Principal Component Analysis; D011471:Prostatic Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D011786:Quality Control; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000592:standards*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012189:Retrospective Studies