Imaging Biomarker Explorer Research Articles

Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study.

To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures. Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness. The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image's radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features. We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.

Analysing the possibility of utilizing CBCT radiomics as an independent modality: a phantom study.

Aim:To verify if computed tomography (CT) radiomics were reproducible by cone beam CT (CBCT) radiomics by using Catphan® 504. Materials and Methods:Catphan® 504 was imaged using the default IGRT OBI CBCT imaging protocols and CT scanner. Seven known density image regions of the phantom were segmented and image feature was extracted by Imaging Biomarker Explorer (IBEX) software. The 49 selected features from four feature categories were analyzed by considering each region of interest (ROI) segment as individual image set. Correlation was studies using interclass correlation coefficient (ICC) and Pearson’s correlation coefficient. Results:The ICC of the three feature categories, namely intensity, GLCM, and GLRLM was significant (p-value<0.05) in comparison with CT, while the ICC of the fourth feature category, NID, was no significant. The average absolute Pearson’s correlation coefficient from the features of the images was as follows: CT: r=0.679±0.257, CBCThead: r=0.707±0.231, CBCTthorax: r=0.643±0.260, and CBCTpelvis: r=0.594±0.276. Conclusion: It seems that the various densities of Catphan® 504 ROI image segments of the CT radiomics are reproducible with CBCT radiomics and CBCT radiomics can be used as an independent modality.

MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer.

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes

Purpose: to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house radiomics platform. Materials and Methods: we developed a series of digital bar phantoms for characterizing intensity and texture features and a series of heteromorphic sphere phantoms for characterizing shape features. The bar phantoms consisted of n equal-width bars (n = 2, 4, 8, or 64). The voxel values of the bars were evenly distributed between 1 and 64. Starting from a perfect sphere, the heteromorphic sphere phantoms were constructed by stochastically attaching smaller spheres to the phantom surface over 5500 iterations. We compared 61 features typically extracted from three radiomics toolboxes: (1) CERR (2) IBEX (3) in-house toolbox. The degree of inconsistency was quantified by concordance correlation coefficient (CCC) and Pearson correlation coefficient (PCC). Sources of discrepancies were characterized based on differences in mathematical definition, pre-processing, and calculation methods. Results: For the intensity and texture features, only 53%, 45%, 55% features demonstrated perfect reproducibility (CCC = 1) between in-house/CERR, in-house/IBEX, and CERR/IBEX comparisons, while 71%, 61%, 61% features reached CCC > 0.8 and 25%, 39%, 39% features were with CCC < 0.5, respectively. Meanwhile, most features demonstrated PCC > 0.95. For shape features, the toolboxes produced similar (CCC > 0.98) volume yet inconsistent surface area, leading to inconsistencies in other shape features. However, all toolboxes resulted in PCC > 0.8 for all shape features except for compactness 1, where inconsistent mathematical definitions were observed. Discrepancies were characterized in pre-processing and calculation implementations from both type of phantoms. Conclusions: Inconsistencies among radiomics extraction toolboxes can be accurately identified using the developed digital phantoms. The inconsistencies demonstrate the significance of implementing quality assurance (QA) of radiomics extraction for reproducible and generalizable radiomic studies. Digital phantoms are therefore very useful tools for QA.

Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.

BackgroundStereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone.MethodsA total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves.ResultsFive CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression.ConclusionsBoth pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone.Key points Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.

Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.

BackgroundTotal mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC).ObjectiveTo predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI‐based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed.MethodsOne hundred and sixty‐five MRI‐based radiomics features in axial T2‐weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort.ResultsOne hundred and thirty‐one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad‐score between pCR and non‐pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86‐0.99) and 0.87 (95% CI, 0.74‐1.00) in the primary and validation cohorts, respectively. The Rad‐score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR.ConclusionOur predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.

Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.

The reproducibility and non-redundancy of radiomic features are challenges in accelerating the clinical translation of radiomics. In this study, we focused on the robustness and non-redundancy of radiomic features extracted from computed tomography (CT) scans in hepatocellular carcinoma (HCC) patients with respect to different tumor segmentation methods. Arterial enhanced CT images were retrospectively randomly obtained from 106 patients. As a training data set, 26 HCC patients were used to calculate the features' reproducibility and redundancy. Another data set (55 HCC patients and 25 healthy volunteers) was used for classification. The GrowCut and GraphCut semiautomatic segmentation methods were implemented in 3D Slicer software by two independent observers, and manual delineation was performed by five abdominal radiation oncologists to acquire the gross tumor volume (GTV). Seventy-one radiomic features were extracted from GTVs using Imaging Biomarker Explorer (IBEX) software, including 17 tumor intensity statistical features, 16 shape features and 38 textural features. For each radiomic feature, intraclass correlation coefficient (ICC) and hierarchical clustering were used to quantify its reproducibility and redundancy. Features with ICC values greater than 0.75 were considered reproducible. To generate the number of non-redundancy feature subgroups, the R2 statistic method was used. Then, a classification model was built using a support vector machine (SVM) algorithm with 10-fold cross validation, and area under ROC curve (AUC) was used to evaluate the utility of non-redundant feature extraction by hierarchical clustering. The percentages of excellent reproducible features in the manual delineation group, GraphCut and GrowCut segmentation group were 69% [49], 73% [52] and 79% [56], respectively. Sixty-five percent [46] of the features showed strong robustness for all segmentation methods. The optimal number of cluster subgroup were 9, 13 and 11 for manual delineation, GraphCut and GrowCut segmentation, respectively. The optimal cluster subgroup number was 6 for all groups when the collectively high reproducibility features were selected for clustering. The receiver operating characteristic (ROC) analysis of radiomics classification model with and without feature reduction for healthy liver and HCC had an AUC value of 0.857 and 0.721 respectively. Our study demonstrates that variations exist in the reproducibility of quantitative imaging features extracted from tumor regions segmented using different methods. The reproducibility and non-redundancy of the radiomic features rely greatly on the tumor segmentation in HCC CT images. We recommend that the most reliable and uniform radiomic features should be selected in the clinical use of radiomics. Classification experiments with feature reduction showed that radiomic features were effective in identifying healthy liver and HCC.

Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics.

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's graphical user interface (GUI) and to demonstrate how IBEX calculated features have been used in clinical studies. IBEX allows for the import of DICOM images with DICOM radiation therapy structure files or Pinnacle files. Once the images are imported, IBEX has tools within the Data Selection GUI to manipulate the viewing of the images, measure voxel values and distances, and create and edit contours. IBEX comes with 27 preprocessing and 132 feature choices to design feature sets. Each preprocessing and feature category has parameters that can be altered. The output from IBEX is a spreadsheet that contains: 1) each feature from the feature set calculated for each contour in a data set, 2) image information about each contour in a data set, and 3) a summary of the preprocessing and features used with their selected parameters. Features calculated from IBEX have been used in studies to test the variability of features under different imaging conditions and in survival models to improve current clinical models.

Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer

Background and purposeOligometastatic non-small cell lung cancer (NSCLC) is a heterogeneous condition with few known risk stratification factors. A quantitative imaging feature (QIF) on positron emission tomography (PET), gray-level co-occurrence matrix energy, has been linked with outcome of nonmetastatic NSCLC. We hypothesized that GLCM energy would enhance the ability of models comprising standard clinical prognostic factors (CPFs) to stratify oligometastatic patients based on overall survival (OS). Materials and methodsWe assessed 79 patients with oligometastatic NSCLC (≤3 metastases) diagnosed in 2007–2015. The primary and largest metastases at diagnosis were delineated on pretreatment scans with GLCM energy extracted using imaging biomarker explorer (IBEX) software. Iterative stepwise elimination feature selection based on the Akaike information criterion identified the optimal model comprising CPFs for predicting OS in a multivariate Cox proportional hazards model. GLCM energy was tested for improving prediction accuracy. ResultsEnergy was a significant predictor of OS (P = 0.028) in addition to the selected CPFs. The c-indexes for the CPF-only and CPF + Energy models were 0.720 and 0.739. ConclusionsIncorporating Energy strengthened a CPF model for predicting OS. These findings support further exploration of QIFs, including markers of the primary tumor vs. those of the metastatic sites.

Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma

Background: The reproducibility of radiomic features is a critical challenge facing radiomic models of tumor prediction or prognosis. The aim of this study is to evaluate the reproducibility of radiomic features with the GrowCut and GraphCut semi-automatic tumor segmentation methods in hepatocellular carcinoma (HCC) CT images. Methods: Computed tomography (CT) data sets (arterial enhanced phase) of 15 patients with HCC were randomly selected in this study. To acquire the gross tumor volume (GTV), semi-automatic segmentation with the GrowCut and GraphCut methods was implemented in 3D Slicer software by two independent observers. Meanwhile, manual delineation of the GTV was implemented by five abdomen radiation oncologists in this study. We divided the sample into three groups: the GrowCut group, the GraphCut group and manual group. Radiomic features (including tumor intensity histogram-based features, textural features and shape-based features) were extracted using the Imaging Biomarker Explorer (IBEX) software. The intraclass correlation coefficient (ICC) was applied to assess the reproducibility of all radiomic features. Results: The radiomic features in the GrowCut group (ICC =0.87±0.19) showed higher reproducibility compared with the radiomic features in the GraphCut group (ICC =0.82±0.24, P Conclusions: Our study reveals that variations exist in the reproducibility of quantitative imaging features extracted from tumor regions segmented using different methods. The 3D Slicer can serve as a better alternative to the manual delineation, and care must be taken when selecting segmentation tools to draw the tumor region.

IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. The IBEX software package was developed using the MATLAB and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and c/c++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX's functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX's source code can be downloaded. The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

SU‐D‐9A‐05: IBEX: An Open Software Infrastructure Platform to Accelerate Collaborative Work On Quantitative Image Features and Predictive Models

Purpose:Quantitative imaging analysis has great potential to improve the accuracy and efficiency of cancer research, but the lack of software infrastructure prohibits progress from developing and using its methods. We have developed a software platform to flexibly support common quantitative imaging analysis tasks such as multimodality image data import and review, feature algorithm development, predictive model design, and consistent data sharing among multi‐institutions.Methods:The IBEX(Imaging Biomarker Explorer) software package was developed under Matlab and C/C++. The software architecture deploys the modern Model‐View‐Controller (MVC) and unit testing programming concepts to isolate each task and to validate if their relevant data and algorithms are fit for use. The View component represents multimodality images with delineated structures (if available), which are imported using DICOM, CERR, or other native formats. Non‐image data, such as patient performance status can also be entered. The Controller component represents image pre‐processing and feature extraction algorithms. The Model component represents the predictive model formula and coefficients. Thanks to the unit testing implementation, the user has the option of reviewing the corresponding result to check the quality of data and algorithms at each stage. While IBEX itself is self‐contained and has implemented typical algorithms and models, it is an open system and additional algorithms and models can be easily added by defining them in library files. Also, a complete external model can be imported, including necessary data about pre‐processing algorithms, feature algorithms, model formula, and model coefficients. This greatly helps maintain data consistency when institutions attempt to validate external models.Results:Testers found the IBEX software to be intuitive and easy to use. All import, pre‐processing algorithms, and feature algorithms were fully validated.Conclusion:We have successfully implemented an open software infrastructure platform to accelerate work on quantitative imaging analysis and collaborative research activities.This work was funded by the NCI(1R03CA178495‐01)