Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier. Med Phys 2023 Jan;50(1):440-448
Date
10/14/2022Pubmed ID
36227732Pubmed Central ID
PMC9868055DOI
10.1002/mp.16047Scopus ID
2-s2.0-85140247594 (requires institutional sign-in at Scopus site) 1 CitationAbstract
PURPOSE: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set.
METHODS: A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose-volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry-run) MRI in the region enclosed by 50-100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t-test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve.
RESULTS: A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No-OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No-OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern.
CONCLUSION: A machine learning classifier based on simple first-order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.
Author List
Parchur AK, Lim S, Nasief HG, Omari EA, Zhang Y, Paulson ES, Hall WA, Erickson B, Li XAAuthors
Beth A. Erickson MD Professor in the Radiation Oncology department at Medical College of WisconsinWilliam Adrian Hall MD Professor in the Radiation Oncology department at Medical College of Wisconsin
Haidy G. Nasief PhD Instructor in the Radiation Oncology department at Medical College of Wisconsin
Eenas Omari PhD Assistant Professor in the Radiation Oncology department at Medical College of Wisconsin
Abdul Kareem Parchur Medical Physicist Assistant in the Radiation Oncology department at Medical College of Wisconsin
Eric Paulson PhD Chief, Professor in the Radiation Oncology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
HumansMachine Learning
Magnetic Resonance Imaging
Pancreatic Neoplasms
Radiotherapy Planning, Computer-Assisted
Retrospective Studies
