Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy. (November 2022)

• Record Type: Journal Article
• Authors: Nasief, Haidy G.; Parchur, Abdul K.; Omari, Eenas; Zhang, Ying; Chen, Xinfeng; Paulson, Eric; Hall, William A.; Erickson, Beth; Li, X. Allen
• Is Part Of: Radiotherapy and oncology. Volume 176(2022)
• Page Start: 165
• Page End: 171
• Publication Date: 2022-11
• Subjects: MRI-guided adaptive radiation therapy -- Online adaptive replanning -- MR-linac -- Wavelets -- Pancreatic cancer; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.10.001
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: A method to determine the necessity of OLAR during MRgART. A framework to extract wavelet multiscale features from daily MRI during MRgART. A machine learning classifier to determine when OLAR is beneficial during MRgART. Abstract: Purpose: Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART. Methods: A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve. Results: Spearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2–3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98. Conclusions: A machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.