Indications of Online Adaptive Replanning Based On Organ Deformation

Sara N. Lim, Ergun E. Ahunbay, Haidy Nasief, Cheng Zheng, Colleen Lawton, X. Allen Li

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Although vital to account for interfractional variations during radiation therapy, online adaptive replanning (OLAR) is time-consuming and labor-intensive compared with the repositioning method. Repositioning is enough for minimal interfractional deformations. Therefore, determining indications for OLAR is desirable. We introduce a method to rapidly determine the need for OLAR by analyzing the Jacobian determinant histogram (JDH) obtained from deformable image registration between reference (planning) and daily images. Methods and Materials: The proposed method was developed and tested based on daily computed tomography (CT) scans acquired during image guided radiation therapy for prostate cancer using an in-room CT scanner. Deformable image registration between daily and reference CT scans was performed. JDHs were extracted from the prostate and a uniform surrounding 10-mm expansion. A classification tree was trained to determine JDH metrics to predict the need for OLAR for a daily CT set. Sixty daily CT scans from 12 randomly selected prostate cases were used as the training data set, with dosimetric plans for both OLAR and repositioning used to determine their class. The resulting classification tree was tested using an independent data set of 45 daily CT scans from 9 other patients with 5 CT scans each. Results: Of a total of 27 JDH metrics tested, 5 were identified predicted whether OLAR was substantially superior to repositioning for a given fraction. A decision tree was constructed using the obtained metrics from the training set. This tree correctly identified all cases in the test set where benefits of OLAR were obvious. Conclusions: A decision tree based on JDH metrics to quickly determine the necessity of online replanning based on the image of the day without segmentation was determined using a machine learning process. The process can be automated and completed within a minute, allowing users to quickly decide which fractions require OLAR.

Original languageEnglish (US)
Pages (from-to)e95-e102
JournalPractical Radiation Oncology
Volume10
Issue number2
DOIs
StatePublished - Mar 1 2020

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging

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