Auto‐detection of necessity for MRI‐guided online adaptive replanning using a machine learning classifier. Issue 1 (22nd October 2022)

• Record Type: Journal Article
• Authors: Parchur, Abdul K.; Lim, Sara; Nasief, Haidy G.; Omari, Eenas A.; Zhang, Ying; Paulson, Eric S.; Hall, William A.; Erickson, Beth; Li, X. Allen
• Is Part Of: Medical physics. Volume 50:Issue 1(2023)
• Page Start: 440
• Page End: 448
• Publication Date: 2022-10-22
• Subjects: adaptive radiation therapy -- MR‐guided radiation therapy -- online adaptive replanning -- pancreatic cancer -- structural similarity index measure; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16047
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: 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.