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

Apte, Aditya; Iyer, Aditi; Crispín-Ortuzar, Mireia; Pandya, R. E.; Dijk, Lisanne V. van; Spezi, Emiliano; Thor, Maria; Um, Hyemin; Veeraraghavan, Harini; Oh, Jung Hun; Shukla–Dave, Amita; Deasy, Joseph O.

doi:10.1002/mp.13046

Cited by 126 publications

(110 citation statements)

References 25 publications

(46 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…The primary objective of this study was to conform IBEX to the ongoing IBSI: from our point of view the standardization of IBEX definitions and calculations is essential for its reliability, as other software programs have already started to join the standardization process. [4][5][6][7][8] The result, S-IBEX, is a radiomic software that includes all the feature definitions and preprocessing steps proposed by IBSI. S-IBEX shares the strengths and ease of use of IBEX software.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics is an emerging field that allows the noninvasive characterization of tumor phenotype, with the aim to extract predictive and prognostic information. Currently, many free software packages are available for radiomic feature calculation in the form of stand‐alone programs, modules and libraries such as MaZda, CGITA, IBEX, LIFEx, MITK Phenotyping, RaCaT, CERR radiomic extension, and Pyradiomics . Moreover, several studies report in‐house developed radiomic programs …”

Section: Introductionmentioning

confidence: 99%

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Technical Note: An IBEX adaption toward image biomarker standardization

et al. 2020

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Purpose Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open‐source radiomic software, and we developed and validated standardized‐IBEX (S‐IBEX), an adaptation of IBEX to IBSI. Methods Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re‐implemented wherever differences emerged: in particular, contour‐to‐binary‐mask conversion, image sub‐portion extraction, re‐segmentation, gray‐level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S‐IBEX. On a patient phantom, S‐IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. Results Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non‐negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S‐IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. Conclusions The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S‐IBEX, a free IBSI‐compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Technical Note: An IBEX adaption toward image biomarker standardization

et al. 2020

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“…Lung nodules were segmented on scans belonging to the lung reconstruction and were copied over to scans belonging to the standard reconstruction. For each reconstruction method, a set of 132 radiomic features were extracted using the CERR radiomics toolbox [10]. Extracted features fell into three categories: (1) first order statistics, (2) shape-based features, and (3) higher order textures including gray level cooccurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), neighborhood gray tone difference matrix (NGTDM), and neighboring gray level dependence matrix (NGLDM).…”

Section: Ct Scans In Lung Cancer Patientsmentioning

confidence: 99%

“…Images were reconstructed using the filtered back projection algorithm. Two sets of 132 radiomic features were extracted using the CERR toolbox for the standard and lung reconstruction kernels [10].…”

Section: Phantom Datamentioning

confidence: 99%

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

Apte

Katsoulakis

et al. 2019

Preprint

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These authors contributed equally to this work. ABSTRACTPurpose: To construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation.Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network. Methods:We propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution. Results:Common radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data. Conclusions:We showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

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Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

Jin,

Qiao,

Zhao

et al. 2023

Cancer Medicine

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BackgroundTo investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC).MethodsThe region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co‐expression network analysis was used to construct co‐expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features.ResultsFour hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs.ConclusionsOSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.

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

Cited by 126 publications

References 25 publications

Technical Note: An IBEX adaption toward image biomarker standardization

Technical Note: An IBEX adaption toward image biomarker standardization

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

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