Dataset Information

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

ABSTRACT: PURPOSE:Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. METHOD:The radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB® application programming interface. RESULTS:The CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. CONCLUSION:The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses.

SUBMITTER: Apte AP

PROVIDER: S-EPMC6597320 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

Apte Aditya P AP Iyer Aditi A Crispin-Ortuzar Mireia M Pandya Rutu R van Dijk Lisanne V LV Spezi Emiliano E Thor Maria M Um Hyemin H Veeraraghavan Harini H Oh Jung Hun JH Shukla-Dave Amita A Deasy Joseph O JO

Medical physics 20180613

<h4>Purpose</h4>Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research.<h4>Method</h4>The radiomics tools in CERR were designed specifically to quantitate medical images in combination with C ...[more]

PMID: 29896896

Similar Datasets

Project description:PURPOSE:Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying the tumor phenotype and predicting treatment response. The three major challenges of radiomics research and clinical adoption are: (a) lack of standardized methodology for radiomics analyses, (b) lack of a universal lexicon to denote features that are semantically equivalent, and (c) lists of feature values alone do not sufficiently capture the details of feature extraction that might nonetheless strongly affect feature values (e.g. image normalization or interpolation parameters). These barriers hamper multicenter validation studies applying subtly different imaging protocols, preprocessing steps and radiomics software. We propose an open-source ontology-guided radiomics analysis workflow (O-RAW) to address the above challenges in the following manner: (a) distributing a free and open-source software package for radiomics analysis, (b) deploying a standard lexicon to uniquely describe features in common usage and (c) provide methods to publish radiomic features as a semantically interoperable data graph object complying to FAIR (findable accessible interoperable reusable) data principles. METHODS:O-RAW was developed in Python, and has three major modules using open-source component libraries (PyRadiomics Extension and PyRadiomics). First, PyRadiomics Extension takes standard DICOM-RT (Radiotherapy) input objects (i.e. a DICOM series and an RTSTRUCT file) and parses them as arrays of voxel intensities and a binary mask corresponding to a volume of interest (VOI). Next, these arrays are passed into PyRadiomics, which performs the feature extraction procedure and returns a Python dictionary object. Lastly, PyRadiomics Extension parses this dictionary as a W3C-compliant Semantic Web "triple store" (i.e., list of subject-predicate-object statements) with relevant semantic meta-labels drawn from the radiation oncology ontology and radiomics ontology. The output can be published on an SPARQL endpoint, and can be remotely examined via SPARQL queries or to a comma separated file for further analysis. RESULTS:We showed that O-RAW executed efficiently on four datasets with different modalities, RIDER (CT), MMD (CT), CROSS (PET) and THUNDER (MR). The test was performed on an HP laptop running Windows 7 operating system and 8GB RAM on which we noted execution time including DICOM images and associated RTSTRUCT matching, binary mask conversion of a single VOI, batch-processing of feature extraction (105 basic features in PyRadiomics), and the conversion to an resource description framework (RDF) object. The results were (RIDER) 407.3, (MMD) 123.5, (CROSS) 513.2 and (THUNDER) 128.9 s for a single VOI. In addition, we demonstrated a use case, taking images from a public repository and publishing the radiomics results as FAIR data in this study on www.radiomics.org. Finally, we provided a practical instance to show how a user could query radiomic features and track the calculation details based on the RDF graph object created by O-RAW via a simple SPARQL query. CONCLUSIONS:We implemented O-RAW for FAIR radiomics analysis, and successfully published radiomic features from DICOM-RT objects as semantic web triples. Its practicability and flexibility can greatly increase the development of radiomics research and ease transfer to clinical practice.

Project description:PurposeDosimetric properties of the new microSilicon diode detector (60023) have been studied with focus on application in small-field dosimetry. The influences of the dimensions of the sensitive volume and the density of the epoxy layer surrounding the silicon chip of microSilicon have been quantified and compared to its predecessor (Diode E 60017) and the microDiamond (60019, all PTW-Freiburg, Germany).MethodsDose linearity has been studied in the range from 0.01 to 8.55 Gy and dose-per-pulse dependence from 0.13 to 0.86 mGy/pulse. The effective point of measurement (EPOM) was determined by comparing measured percentage depth dose curves with a reference curve (Roos chamber). Output ratios were measured for nominal field sizes from 0.5 × 0.5 cm2 to 4 × 4 cm2 . The corresponding small-field output correction factors, k, were derived with a plastic scintillation detector as reference. The lateral dose-response function, K(x), was determined using a slit beam geometry.ResultsMicroSilicon shows linear dose response (R2 = 1.000) in both low and high dose range up to 8.55 Gy with deviations of only up to 1% within the dose-per-pulse values investigated. The EPOM was found to lie (0.7 ± 0.2) mm below the front detector's surface. The derived k for microSilicon (0.960 at seff = 0.55 cm) is similar to that of microDiamond (0.956), while Diode E requires larger corrections (0.929). This improved behavior of microSilicon in small-fields is reflected in the slightly wider K(x) compared to Diode E. Furthermore, the amplitude of the negative values in K(x) at the borders of the sensitive volume has been reduced.ConclusionsCompared to its predecessor, microSilicon shows improved dosimetric behavior with higher sensitivity and smaller dose-per-pulse dependence. Profile measurements demonstrated that microSilicon causes less perturbation in off-axis measurements. It is especially suitable for the applications in small-field output factors and profile measurements.

Dataset Information

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

Publications

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets