Automatically Determining Necessity of Online Adaptive Replanning Based on MRI Wavelet Multiscale Texture Features for MRI-Guided Adaptive Radiation Therapy

Abstract

MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) as enabled with MR-LINAC, can substantially improve radiation therapy delivery. However, OLAR is labor-intensive and time-consuming. OLAR is unnecessary for all treatment fractions and being able to objectively and automatically determine when it is needed is desirable. In this study, we investigate whether wavelet multiscale texture features extracted from daily MRI can be used to automatically and objectively identify when OLAR is needed using a machine learning approach.The method was developed and demonstrated using 70 daily motion average MRI sets acquired on a 1.5T MR-LINAC using a balanced turbo field echo sequence during MRgART for 14 pancreatic cancer patients each treated with 5 fractions. For each daily MRI set, OLAR and repositioning (i.e., adapt to position) plans were created and judged per clinical dose-volume constrains by experienced MRL physicists. Multiscale texture features were extracted from the region enclosed by 50 and 100% reference isodose surfaces for different wavelet decomposition levels. Spearman correlations were utilized to rule-out redundant features. Inter-class correlation (ICC), coefficient of variance (COV), and t-test (P < 0.05) were used to determine significant features. Mahalonobis distance classifier with a leave one out cross-validation method was used to develop prediction models based on the identified features and their combinations. The performance of the model to predict whether OLAR is necessary for a given daily MRI set was measured using the AUC of the ROC curve.Spearman correlation identified 123 features from different wavelet decomposition levels that were not redundant (r < 0.9). Of these features, 82 showed high ICC for repositioning, 67 had a COV greater than 9% for OLAR and lower than 5% for repositioning. In addition, 38 of the 67 features passed the t-test with P value < 0.05. Best performing model was a 3-feature combination that can predict OLAR necessity with an AUC of 0.95. It was found that misclassification was attributed to some cases with very close dosimetric gain between repositioning and OLAR and hence a lower discrimination ability for this subgroup.The necessity of OLAR can be automatically determined using a machine learning model based on the wavelet multiscale texture features from the daily MRI immediately after its acquisition, avoiding unnecessary effort during MRgART.