Time stability of delta‐radiomics features and the impact on patient analysis in longitudinal CT images. Issue 4 (18th February 2019)

• Record Type: Journal Article
• Authors: Plautz, Tia E.; Zheng, Cheng; Noid, George; Li, X. Allen
• Is Part Of: Medical physics. Volume 46:Issue 4(2019)
• Page Start: 1663
• Page End: 1676
• Publication Date: 2019-02-18
• Subjects: computed tomography -- delta‐radiomics -- repeatability -- texture analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13395
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: This study first aims to show that the values of texture features extracted from phantoms are stable over clinical timescales. Second, that changes in patients' feature values over the course of radiation therapy (RT) are treatment induced and statistically significant. Methods: The CT datasets of a 3D printed anatomically informed texture phantom containing liver and low‐contrast modules, and the homogeneous module of the Catphan 500‐Series phantom, were acquired once per week over the course of a 6‐week period, to simulate the timescale of conventional RT duration. A Definition AS Open CT scanner on rails (Siemens) and our institution's standard abdominal protocol were used. In each phantom module, 8 regions of interest (20 cm 3 ) were selected and 50 texture features were extracted from each module over the longitudinal dataset. The time stability of each feature was evaluated. The expected variation over the treatment timescale was quantified for each texture (module). Subsequently, the pancreas heads of 10 patients who underwent RT for adenocarcinoma of the pancreas head with a pathologic response of at least "moderate" (grade 2), were contoured on the daily CTs acquired using the same scanner. The pancreas heads were contoured on one image per week. Mean CT number, skewness, kurtosis, and coarseness were extracted from these data. The phantom modules were shown to be accurate representations of these features in the pancreas data. The change in the feature value between fractions 2 and 26 was compared with the phantom data in order to identify significant changes in feature value. Results: Of the 50 features examined in all 3 phantom modules, 47 were found to have zero time‐trend when a fit assuming homogeneous variance was used. When a fit allowing for heterogeneous variance was used, 49 features were found to have zero time‐trend. Features were stable and repeatable within a feature‐specific confidence interval over the 6‐week period of acquisition in all three phantom modules. Changes in feature value between fractions 2 and 26 were highly patient specific. Mean CT number was found to decrease significantly in 7 of 10 patients and increase significantly in one patient. Skewness increased significantly in one patient and decreased significantly in one patient. Kurtosis decreased significantly in four patients and increased significantly in one patient. Coarseness increased significantly in seven patients and decreased significantly in one patient. Only one patient experienced no significant changes in feature value. Conclusion: The CT texture feature measurements of phantoms are stable and repeatable within a feature‐specific confidence interval in all three phantom modules. This suggests that the changes observed in features extracted from longitudinal patient CT data may be treatment induced, and demonstrates their potentiality for early assessment of treatment response.