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Title: MO-B-207B-01: Harmonization & Robustness in Radiomics

Abstract

Feature extraction for radiomics studies typically comprises the following stages: Imaging, segmentation, image processing, and feature extraction. Each of these stages has associated uncertainties that can affect the quality of a radiomics model created using the resulting image features. For example, the imaging device manufacturer and model have been shown to impact the values of image features, as have pixel size and imaging protocol parameters. Image processing, such as low-pass filtering to reduce noise, also changes calculated image features and should be designed to optimize the information content of the resulting features. The details of certain feature algorithms, such as co-occurrence matrix bin sizes, are also important, and should be optimized for specific radiomics tasks. The volume of the region of interest should be considered as image features can be related to volume and can give unanticipated results when the volumes are too small. In this session we will describe approaches to quantify the variabilities in radiomics studies, including the most recent results quantifying these variabilities for CT, MRI and PET imaging. We will discuss methods to optimize image processing and feature extraction in order to maximize the information content of the image features. Finally, we will describe work tomore » harmonize imaging protocols and feature calculations to help minimize uncertainties in radiomics studies. Learning Objectives: At the end of this session, participants will be able to: Identify the sources of uncertainty in radiomics studies (CT, PET, and MRI imaging) Describe methods for quantifying the magnitude of uncertainties Describe approaches for mitigating the effects of the uncertainties on radiomics models Funding from NIH, CPRIT, Varian, Elekta; L. Court, NCI, CPRIT, Varian, Elekta.« less

Authors:
 [1]
  1. UT MD Anderson Cancer Center (United States)
Publication Date:
OSTI Identifier:
22649517
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; EXTRACTION; IMAGE PROCESSING; NMR IMAGING; POSITRON COMPUTED TOMOGRAPHY

Citation Formats

Court, L. MO-B-207B-01: Harmonization & Robustness in Radiomics. United States: N. p., 2016. Web. doi:10.1118/1.4957190.
Court, L. MO-B-207B-01: Harmonization & Robustness in Radiomics. United States. doi:10.1118/1.4957190.
Court, L. Wed . "MO-B-207B-01: Harmonization & Robustness in Radiomics". United States. doi:10.1118/1.4957190.
@article{osti_22649517,
title = {MO-B-207B-01: Harmonization & Robustness in Radiomics},
author = {Court, L.},
abstractNote = {Feature extraction for radiomics studies typically comprises the following stages: Imaging, segmentation, image processing, and feature extraction. Each of these stages has associated uncertainties that can affect the quality of a radiomics model created using the resulting image features. For example, the imaging device manufacturer and model have been shown to impact the values of image features, as have pixel size and imaging protocol parameters. Image processing, such as low-pass filtering to reduce noise, also changes calculated image features and should be designed to optimize the information content of the resulting features. The details of certain feature algorithms, such as co-occurrence matrix bin sizes, are also important, and should be optimized for specific radiomics tasks. The volume of the region of interest should be considered as image features can be related to volume and can give unanticipated results when the volumes are too small. In this session we will describe approaches to quantify the variabilities in radiomics studies, including the most recent results quantifying these variabilities for CT, MRI and PET imaging. We will discuss methods to optimize image processing and feature extraction in order to maximize the information content of the image features. Finally, we will describe work to harmonize imaging protocols and feature calculations to help minimize uncertainties in radiomics studies. Learning Objectives: At the end of this session, participants will be able to: Identify the sources of uncertainty in radiomics studies (CT, PET, and MRI imaging) Describe methods for quantifying the magnitude of uncertainties Describe approaches for mitigating the effects of the uncertainties on radiomics models Funding from NIH, CPRIT, Varian, Elekta; L. Court, NCI, CPRIT, Varian, Elekta.},
doi = {10.1118/1.4957190},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Feature extraction for radiomics studies typically comprises the following stages: Imaging, segmentation, image processing, and feature extraction. Each of these stages has associated uncertainties that can affect the quality of a radiomics model created using the resulting image features. For example, the imaging device manufacturer and model have been shown to impact the values of image features, as have pixel size and imaging protocol parameters. Image processing, such as low-pass filtering to reduce noise, also changes calculated image features and should be designed to optimize the information content of the resulting features. The details of certain feature algorithms, such asmore » co-occurrence matrix bin sizes, are also important, and should be optimized for specific radiomics tasks. The volume of the region of interest should be considered as image features can be related to volume and can give unanticipated results when the volumes are too small. In this session we will describe approaches to quantify the variabilities in radiomics studies, including the most recent results quantifying these variabilities for CT, MRI and PET imaging. We will discuss methods to optimize image processing and feature extraction in order to maximize the information content of the image features. Finally, we will describe work to harmonize imaging protocols and feature calculations to help minimize uncertainties in radiomics studies. Learning Objectives: At the end of this session, participants will be able to: Identify the sources of uncertainty in radiomics studies (CT, PET, and MRI imaging) Describe methods for quantifying the magnitude of uncertainties Describe approaches for mitigating the effects of the uncertainties on radiomics models Funding from NIH, CPRIT, Varian, Elekta; L. Court, NCI, CPRIT, Varian, Elekta.« less
  • Purpose: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer. Methods: 40 rectal cancer patients were enrolled in this study, each of whom underwent two CT scans within average 8.7 days (5 days to 17 days), before any treatment was delivered. The rectal gross tumor volume (GTV) was distinguished and segmented by an experienced oncologist in both CTs. Totally, more than 2000 radiomics features were defined in this study, which were divided into four groups (I: GLCM, II: GLRLM III: Wavelet GLCM and IV: Waveletmore » GLRLM). For each group, five types of features were extracted (Max slice: features from the largest slice of target images, Max value: features from all slices of target images and choose the maximum value, Min value: minimum value of features for all slices, Average value: average value of features for all slices, Matrix sum: all slices of target images translate into GLCM and GLRLM matrices and superpose all matrices, then extract features from the superposed matrix). Meanwhile a LOG (Laplace of Gauss) filter with different parameters was applied to these images. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) were calculated to assess the reproducibility. Results: 403 radiomics features were extracted from each type of patients’ medical images. Features of average type are the most reproducible. Different filters have little effect for radiomics features. For the average type features, 253 out of 403 features (62.8%) showed high reproducibility (ICC≥0.8), 133 out of 403 features (33.0%) showed medium reproducibility (0.8≥ICC≥0.5) and 17 out of 403 features (4.2%) showed low reproducibility (ICC≥0.5). Conclusion: The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.« less
  • Purpose: To develop and validate a prediction model using radiomics features extracted from MR images to distinguish radiation necrosis from tumor progression for brain metastases treated with Gamma knife radiosurgery. Methods: The images used to develop the model were T1 post-contrast MR scans from 71 patients who had had pathologic confirmation of necrosis or progression; 1 lesion was identified per patient (17 necrosis and 54 progression). Radiomics features were extracted from 2 images at 2 time points per patient, both obtained prior to resection. Each lesion was manually contoured on each image, and 282 radiomics features were calculated for eachmore » lesion. The correlation for each radiomics feature between two time points was calculated within each group to identify a subset of features with distinct values between two groups. The delta of this subset of radiomics features, characterizing changes from the earlier time to the later one, was included as a covariate to build a prediction model using support vector machines with a cubic polynomial kernel function. The model was evaluated with a 10-fold cross-validation. Results: Forty radiomics features were selected based on consistent correlation values of approximately 0 for the necrosis group and >0.2 for the progression group. In performing the 10-fold cross-validation, we narrowed this number down to 11 delta radiomics features for the model. This 11-delta-feature model showed an overall prediction accuracy of 83.1%, with a true positive rate of 58.8% in predicting necrosis and 90.7% for predicting tumor progression. The area under the curve for the prediction model was 0.79. Conclusion: These delta radiomics features extracted from MR scans showed potential for distinguishing radiation necrosis from tumor progression. This tool may be a useful, noninvasive means of determining the status of an enlarging lesion after radiosurgery, aiding decision-making regarding surgical resection versus conservative medical management.« less
  • Purpose: To investigate the impact of reconstruction Field of View on Radiomics features in computed tomography (CT) using a texture phantom. Methods: A rectangular Credence Cartridge Radiomics (CCR) phantom, composed of 10 different cartridges, was scanned on four different CT scanners from two manufacturers. A pre-defined scanning protocol was adopted for consistency. The slice thickness and reconstruction interval of 1.5 mm was used on all scanners. The reconstruction FOV was varied to result a voxel size ranging from 0.38 to 0.98 mm. A spherical region of interest (ROI) was contoured on the shredded rubber cartridge from CCR phantom CT scans.more » Ninety three Radiomics features were extracted from ROI using an in-house program. These include 10 shape, 22 intensity, 26 GLCM, 11 GLZSM, 11 RLM, 5 NGTDM and 8 fractal dimensional features. To evaluate the Interscanner variability across three scanners, a coefficient of variation (COV) was calculated for each feature group. Each group was further classified according to the COV by calculating the percentage of features in each of the following categories: COV≤ 5%, between 5 and 10% and ≥ 10%. Results: Shape features were the most robust, as expected, because of the spherical contouring of ROI. Intensity features were the second most robust with 54.5 to 64% of features with COV < 5%. GLCM features ranged from 31 to 35% for the same category. RLM features were sensitive to specific scanner and 5% variability was 9 to 54%. Almost all GLZM and NGTDM features showed COV ≥10% among the scanners. The dependence of fractal dimensions features on FOV was not consistent across different scanners. Conclusion: We concluded that reconstruction FOV greatly influence Radiomics features. The GLZSM and NGTDM are highly sensitive to FOV. funded in part by Grant NIH/NCI R01CA190105-01.« less
  • Purpose: Use a NEMA-IEC PET phantom to assess the robustness of FDG-PET-based radiomics features to changes in reconstruction parameters across different scanners. Methods: We scanned a NEMA-IEC PET phantom on 3 different scanners (GE Discovery VCT, GE Discovery 710, and Siemens mCT) using a FDG source-to-background ratio of 10:1. Images were retrospectively reconstructed using different iterations (2–3), subsets (21–24), Gaussian filter widths (2, 4, 6mm), and matrix sizes (128,192,256). The 710 and mCT used time-of-flight and point-spread-functions in reconstruction. The axial-image through the center of the 6 active spheres was used for analysis. A region-of-interest containing all spheres was ablemore » to simulate a heterogeneous lesion due to partial volume effects. Maximum voxel deviations from all retrospectively reconstructed images (18 per scanner) was compared to our standard clinical protocol. PET Images from 195 non-small cell lung cancer patients were used to compare feature variation. The ratio of a feature’s standard deviation from the patient cohort versus the phantom images was calculated to assess for feature robustness. Results: Across all images, the percentage of voxels differing by <1SUV and <2SUV ranged from 61–92% and 88–99%, respectively. Voxel-voxel similarity decreased when using higher resolution image matrices (192/256 versus 128) and was comparable across scanners. Taking the ratio of patient and phantom feature standard deviation was able to identify features that were not robust to changes in reconstruction parameters (e.g. co-occurrence correlation). Metrics found to be reasonably robust (standard deviation ratios > 3) were observed for routinely used SUV metrics (e.g. SUVmean and SUVmax) as well as some radiomics features (e.g. co-occurrence contrast, co-occurrence energy, standard deviation, and uniformity). Similar standard deviation ratios were observed across scanners. Conclusions: Our method enabled a comparison of feature variability across scanners and was able to identify features that were not robust to changes in reconstruction parameters.« less