Improving Outcomes Prediction Using a Combination of MRI Wavelet Delta-Radiomic Textures and Clinical Biomarkers for MR-Guided Adaptive Radiation Therapy of Pancreatic Cancer International Journal of Radiation Oncology, Biology, Physics Volume 114 Issue 3 Pages S79-S80
Date
11/01/2022Abstract
Purpose/Objective(s)
We have previously reported that wavelet delta radiomic features (WDRF) extracted from daily MRIs can predict treatment response of MRI-guided adaptive radiation therapy (MRgART) for pancreatic cancer. This study aims to investigate whether combining available clinical biomarkers with the WDRF would improve outcome prediction for pancreatic cancer treated with pre-operative MRgART.
Materials/Methods
This method was developed and demonstrated using 90 daily motion averaged MRI sets derived from 4D MRIs acquired during MRgART with concurrent chemotherapy for 18 pancreatic cancer patients treated in 5 SBRT fractions on a 1.5T MR-Linac, along with their CA19-9, CEA, metastatic status, and follow up data. For each daily MRI set, wavelet decompositions were applied a programming environment to provide a robust way for feature extraction and to overcome challenges with inter- and intra-patient MRI intensity variations. 438 WDRF were calculated from different decomposition levels. Good or bad response groups were defined based on pathological response or distant metastasis free progression, and 50% reduction in CA19-9. The time between the end of treatment and end points of death or presence of distant metastasis within 2 years were used to build progression free survival (PFS) model. Spearman correlations were applied to rule out redundant features. T-test and regression models were used to identify independent WDRFs with significant changes that correlate to response and to examine the effect of combining CA19-9, CEA, and WDRFs on response prediction. Concordance statistics was used to measure model effectiveness. A Cox analysis was performed to identify predictors of PFS and test the association of the combinations on PFS correlations. The effect of a decrease in CA19-9 or CEA levels during treatment versus failure of CA19-9 or CEA levels to normalize on PFS was examined.
Results
Spearman correlation showed that 67 WDRFs from different wavelet decomposition were not redundant and can be used for the analysis. 30 WDRF demonstrated opposite trends correlating to response. Of these, 8 WDRFs passed the t-test showing significant changes correlating to response by the 3rd or 4th fraction. CA199 and CEA were each correlated to 2 WDRFs. Increasing CA19-9, CEA levels during treatment were correlated to bad response. Incorporating CEA, CA19-9 with the significant WDRFs increased the concordance statistics from 0.65 to 0.96. The Cox multivariate analysis showed that a treatment related decrease in CA19-9 levels (p=0.0017), and 3 WDRFs (p=0.004-0.013) were independent predictors of PFS. The hazard ratio was reduced from 0.57, p=0.03 for using CA19-9 only to 0.163, p=0.004 for the combination of WDRFs and CA19-9.
Conclusion
Combining wavelet delta-radiomics features extracted from daily treatment MRIs with common clinical biomarkers improves outcome prediction for chemoradiation therapy of pancreatic cancer and may ultimately enable intensification of additional treatment to improve outcomes.
Author List
HG Nasief, X Chen, BA Erickson, ES Paulson, A Li, WA HallAuthor
Haidy G. Nasief PhD Instructor in the Radiation Oncology department at Medical College of WisconsinView Online
