Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer

Purpose Radiomic features of cone‐beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, an...

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Bibliographic Details
Published in:Medical physics (Lancaster) 2021-05, Vol.48 (5), p.2386-2399
Main Authors: Delgadillo, Rodrigo, Spieler, Benjamin O., Ford, John C., Kwon, Deukwoo, Yang, Fei, Studenski, Matthew, Padgett, Kyle R., Abramowitz, Matthew C., Dal Pra, Alan, Stoyanova, Radka, Pollack, Alan, Dogan, Nesrin
Format: Article
Language:English
Subjects:
cone‐beam CT
prostate cancer
radiomics
radiotherapy
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Summary:Purpose Radiomic features of cone‐beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT‐based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. Methods The quality of 42 CBCT‐based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. Results Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray‐level size zone matrix (GLSZM), neighborhood gray‐tone difference matrix (NGTDM), and gray‐level‐run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top‐repeatable iCBCT method (iCBCT‐Sharp‐VeryHigh) is more repeatable than the top‐repeatable sCBCT method (sCBCT‐Smooth) in 64% of the radiomic features. Conclusion Methods for
ISSN:0094-2405
2473-4209
DOI:10.1002/mp.14787