Program Information
Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures
S Yip*, T Coroller , N Niu , H Mamon , H Aerts , R Berbeco , Brigham and Women's Hospital, Boston, MA
Presentations
TU-AB-BRA-12 (Tuesday, July 14, 2015) 7:30 AM - 9:30 AM Room: Ballroom A
Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre- onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response.
Methods:Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively.
Results:Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV~80%) except for fast-demons (OV~60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69-0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68-0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71-0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%).
Conclusion:Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms.
Funding Support, Disclosures, and Conflict of Interest: The project was supported in part by a Kaye Scholar Award.
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