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Journal ArticleDOI

CERR: A computational environment for radiotherapy research

01 May 2003-Medical Physics (Med Phys)-Vol. 30, Iss: 5, pp 979-985
TL;DR: CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.
Abstract: A software environment is described, called the computational environment for radiotherapy research (CERR, pronounced "sir"). CERR partially addresses four broad needs in treatment planning research: (a) it provides a convenient and powerful software environment to develop and prototype treatment planning concepts, (b) it serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.), (c) it provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and (d) it provides a convenient and powerful tool for sharing and reproducing treatment planning research results. The functional components currently being distributed, including source code, include: (1) an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell-array data object, facilitating manipulation; (2) viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash, (3) a suite of contouring tools to edit and/or create anatomical structures, (4) dose-volume and dose-surface histogram calculation and display tools, and (5) various predefined commands. CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self-describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose-volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization. In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.
Citations
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Journal ArticleDOI
TL;DR: Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
Abstract: Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients) We identified that Wilcoxon test based feature selection method WLCX (stability = 084 ± 005, AUC = 065 ± 002) and a classification method random forest RF (RSD = 352%, AUC = 066 ± 003) had highest prognostic performance with high stability against data perturbation Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (3421% of total variance) Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice

749 citations

Journal ArticleDOI
TL;DR: Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value, and in multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates.
Abstract: This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

639 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluated computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.

524 citations

Journal ArticleDOI
15 Jul 2014-PLOS ONE
TL;DR: 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors and can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.
Abstract: Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

483 citations

Journal ArticleDOI
TL;DR: Investigation of intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment suggests proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

445 citations


Cites methods from "CERR: A computational environment f..."

  • ...The pre-treatment scans were transferred using the digital imaging and communications in medicine (DICOM) protocol into the research treatment planning system CERR, which stands for computational environment for radiotherapy research [28], where the intensity values were converted into SUV....

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References
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Book ChapterDOI
01 Jan 1995
TL;DR: Wavelab is a library of wavelet-packet analysis, cosine- Packet analysis and matching pursuit, available free of charge over the Internet.
Abstract: Wavelab is a library of wavelet-packet analysis, cosine-packet analysis and matching pursuit. The library is available free of charge over the Internet. Versions are provided for Macintosh, UNIX and Windows machines.

570 citations

Journal ArticleDOI
TL;DR: A DICOM (Digital Imaging and Communication in Medicine) based toolbox, developed for the evaluation and the verification of radiotherapy treatment plans, offers the possibility of importing treatment plans generated with different calculation algorithms and/or different optimization engines and evaluating dose distributions on an independent platform.
Abstract: The verification of radiotherapy plans is an essential step in the treatment planning process This is especially important for highly conformal and IMRT plans which produce non-intuitive fluence maps and complex 3D dose distributions In this work we present a DICOM (Digital Imaging and Communication in Medicine) based toolbox, developed for the evaluation and the verification of radiotherapy treatment plans The toolbox offers the possibility of importing treatment plans generated with different calculation algorithms and/or different optimization engines and evaluating dose distributions on an independent platform Furthermore the radiotherapy set-up can be exported to the BEAM Monte Carlo code system for dose verification This can be done by simulating the irradiation of the patient CT dataset or the irradiation of a software-generated water phantom We show the application of some of the functions implemented in this toolbox for the evaluation and verification of an IMRT treatment of the head and neck region

87 citations

Journal ArticleDOI
TL;DR: Within the constraints of the X Window System environment, this assemblage of software tools provides a portable, flexible, and convenient method for the quantitative evaluation of several radiotherapy treatment plans.
Abstract: Purpose: This article announces the availability of a convenient and useful software environment for the evaluation of three-dimensional (3D) radiotherapy treatment plans. Materials and Methods: Using standards such as American National Standards for Information Systems C and the X Window System allowed us to bring the computation and display of dose-volume histograms, dose statistics, tumor control probabilities, normal tissue complication probabilities, and a figure of merit together under one user interface. These plan evaluation tools are not stand alone, but must interact with a 3D radiation therapy planning system to obtain the required dose matrices and patient anatomical contours. Installation of the software involves a programmer who writes a software bridge between the radiation therapy planning system and the tools, thereby providing access to local data files. This design strategy confines portability issues to one area of the software. Results: Access to the other tools is through the Graphical Plan Evaluation Tool (GPET). GPET coordinates the use of each of the tools and provides graphical facilities for display of their results. Importantly, GPET assures that the displayed results of each tool have been computed with the same input specifications for all treatment plans being compared. For added convenience, the user can rearrange the resultant data to be reviewed in various ways on the video screen. The software design also allows incorporation of customized algorithms and input data for computing tumor control probability and normal tissue complication probabilities, since those currently available are controversial. Conclusion: The Graphical Plan Evaluation Tool unifies the simultaneous computation for several analytical tools and graphical display of their results. Within the constraints of the X Window System environment, this assemblage of software tools provides a portable, flexible, and convenient method for the quantitative evaluation of several radiotherapy treatment plans.

19 citations

Journal ArticleDOI
TL;DR: The authors have created a tool, Pyfort, for connecting Fortran routines to Python, which produces one or more Python extension modules which you then compile and load into Python, either statically or dynamically, as desired.
Abstract: Python is a great scripting language. It is portable, free, and has a powerful numerical facility, object oriented features, and a library of modules that enable a huge variety of applications: cryptography, image processing, special effects for movies, Web programming, Web site search engines, and so on. The authors have created a tool, Pyfort, for connecting Fortran routines to Python. To use Pyfort, you create an input file that describes the Fortran functions and subroutines you wish to access from Python. This file uses a syntax that is close to a subset of the Fortran 95 interface syntax. Once the input file is prepared, you execute the Pyfort tool. The tool produces one or more Python extension modules, which you then compile and load into Python, either statically or dynamically, as desired.

18 citations

Journal Article
TL;DR: Pyfort as mentioned in this paper is a tool for connecting Fortran routines to Python, using a syntax that is close to a subset of the Fortran 95 interface syntax, which can produce one or more Python extension modules which can then be loaded into Python, either statically or dynamically, as desired.
Abstract: Python is a great scripting language. It is portable, free, and has a powerful numerical facility, object oriented features, and a library of modules that enable a huge variety of applications: cryptography, image processing, special effects for movies, Web programming, Web site search engines, and so on. The authors have created a tool, Pyfort, for connecting Fortran routines to Python. To use Pyfort, you create an input file that describes the Fortran functions and subroutines you wish to access from Python. This file uses a syntax that is close to a subset of the Fortran 95 interface syntax. Once the input file is prepared, you execute the Pyfort tool. The tool produces one or more Python extension modules, which you then compile and load into Python, either statically or dynamically, as desired.

18 citations