Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?. Issue 12 (6th November 2015)

• Record Type: Journal Article
• Authors: Fave, Xenia; Mackin, Dennis; Yang, Jinzhong; Zhang, Joy; Fried, David; Balter, Peter; Followill, David; Gomez, Daniel; Kyle Jones, A.; Stingo, Francesco; Fontenot, Jonas; Court, Laurence
• Is Part Of: Medical physics. Volume 42:Issue 12(2015)
• Page Start: 6784
• Page End: 6797
• Publication Date: 2015-11-06
• Subjects: cancer -- computerised tomography -- feature extraction -- image motion analysis -- image texture -- linear accelerators -- lung -- medical image processing -- phantoms; Computed tomography -- Image analysis; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of motion -- Analysis of texture -- Linear accelerators; texture -- quantitative imaging features -- reproducibility -- cone‐beam CT; Cone beam computed tomography -- Cancer -- Rubber -- Image analysis -- Linear accelerators -- Image scanners -- Medical image quality -- Lungs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4934826
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone‐beam CT (CBCT) images taken using medical Linac onboard‐imaging systems in order to track them through treatment. Methods: Test‐retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rank correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test‐retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features' interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic‐motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test‐retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4–13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer‐protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6–8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray‐level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray‐level run emphasis, gray‐level nonuniformity, sum‐average, information measure correlation, scaled mean, and entropy) from the center‐slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.