In Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
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 1.5T 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 was used to build prediction models using Bayesian classifier. The model performance was judged using the AUC of 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.
Nasief Haidy G, Parchur Abdul K, Omari Eenas, Zhang Ying, Chen Xinfeng, Paulson Eric, Hall William A, Erickson Beth, Allen Li X
MR-linac, MRI-guided adaptive radiation therapy, Pancreatic cancer, online adaptive replanning, wavelets