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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. 2016. "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 = 2016,
month = 6
}
  • 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 determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co-occurrence, run-length, gray-tone difference, and shape features, were calculated from pretreatment and weekly intra-treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit-depth resample and/or a smoothing filter were tested for their impact on volume-correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized featuresmore » were calculated from the intratreatment images and tested in linear mixed-effects models to determine which features changed significantly with dose-fraction. The slopes in these significant features were defined as delta-radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta-radiomics features for overall-survival, local-recurrence, and distant-metastases. Leave-one-out cross validation was used for model-fitting and patient predictions. Concordance indices(c-index) and p-values for the log-rank test with patients stratified at the median were calculated. Results: Approximately one-half of the 62 optimized features required no preprocessing, one-fourth required smoothing, and one-fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall-survival, the c-index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta-radiomics features. For distant-metastases, the c-index improved from 0.53 to 0.58, while for local-recurrence it did not improve. Patient stratification significantly improved (p-value<0.05) for overallsurvival and distant-metastases when delta-radiomics features were included. The delta-radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave-one-out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High-risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).« 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: The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade. Methods: We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging featuresmore » were added for comparison. AUC was computed for continuous and χ{sup 2} for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing. Results: Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test). Conclusion: We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.« less
  • Purpose: Early prediction of distant metastasis may provide crucial information for adaptive therapy, subsequently improving patient survival. Radiomic features that extracted from PET and CT images have been used for assessing tumor phenotype and predicting clinical outcomes. This study investigates the values of radiomic features in predicting distant metastasis (DM) in non-small cell lung cancer (NSCLC). Methods: A total of 108 patients with stage II–III lung adenocarcinoma were included in this retrospective study. Twenty radiomic features were selected (10 from CT and 10 from PET). Conventional features (metabolic tumor volume, SUV, volume and diameter) were included for comparison. Concordance indexmore » (CI) was used to evaluate features prognostic value. Noether test was used to compute p-value to consider CI significance from random (CI = 0.5) and were adjusted for multiple testing using false rate discovery (FDR). Results: A total of 70 patients had DM (64.8%) with a median time to event of 8.8 months. The median delivered dose was 60 Gy (range 33–68 Gy). None of the conventional features from PET (CI ranged from 0.51 to 0.56) or CT (CI ranged from 0.57 to 0.58) were significant from random. Five radiomics features were significantly prognostic from random for DM (p-values < 0.05). Four were extracted from CT (CI = 0.61 to 0.63, p-value <0.01) and one from PET which was also the most prognostic (CI = 0.64, p-value <0.001). Conclusion: This study demonstrated significant association between radiomic features and DM for patients with locally advanced lung adenocarcinoma. Moreover, conventional (clinically utilized) metrics were not significantly associated with DM. Radiomics can potentially help classify patients at higher risk of DM, allowing clinicians to individualize treatment, such as intensification of chemotherapy) to reduce the risk of DM and improve survival. R.M. has consulting interests with Amgen.« less