Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth

Yang, Fei; Dogan, Nesrin; Stoyanova, Radka; Ford, John C.

doi:10.1016/j.ejmp.2018.05.017

Cited by 87 publications

(77 citation statements)

References 39 publications

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“…The radiomic features extracted from DW‐MRI depend on acquisition parameters and conditions as k‐space trajectory, gradient strengths and b‐values. The repeatability of MR‐based radiomic features has been investigated using a ground truth digital phantom of brain glioma patients and an MRI simulator capable of generating images according to different acquisition (field strength, pulse sequence, arrangement of field coils) and reconstruction methods. It was found that some features are subject to small changes, compared with clinical effect size.…”

Section: Current Challenges and Recommendationsmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

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Section: Current Challenges and Recommendationsmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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“…Approach legend RAW S333 S1 S100BW15 BINCOUNT64 BLADDERNORM and image pre-processing (normalization and quantization) in ADC maps of cervix cancer patients. Despite the relatively small sample size, our work is the first study to propose a strong methodology to assess the robustness of radiomic features in ADC maps of cervical cancer patients, with the aim of extending the effort of harmonization carried by the IBSI for radiomics in PET and CT. At the best of our knowledge, prior studies focused mainly on repeatability of radiomic features for prostate [25], brain [26] and/or dedicate phantoms [27] for T1-and T2-weighted MR images, except for the prostate study by Schwier et al which also examined ADC maps. Our results are in line with the Schwier study, which stated that: (a) digital image pre-processing prior to features extraction sensitively changed features values; and (b) normalization prior to features extraction improved the features repeatabilities.…”

Section: Overall Summary and Comparison To Prior Studiesmentioning

confidence: 99%

Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients

Traverso

Kazmierski

Welch

et al. 2020

Radiotherapy and Oncology

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a b s t r a c tPurpose: The aims of this study are to evaluate the stability of radiomic features from Apparent Diffusion Coefficient (ADC) maps of cervical cancer with respect to: (1) reproducibility in inter-observer delineation, and (2) image pre-processing (normalization/quantization) prior to feature extraction. Materials and methods: Two observers manually delineated the tumor on ADC maps derived from pretreatment diffusion-weighted Magnetic Resonance imaging of 81 patients with FIGO stage IB-IVA cervical cancer. First-order, shape, and texture features were extracted from the original and filtered images considering 5 different normalizations (four taken from the available literature, and one based on urine ADC) and two different quantization techniques (fixed-bin widths from 0.05 to 25, and fixed-bin count). Stability of radiomic features was assessed using intraclass correlation coefficient (ICC): poor (ICC < 0.75); good (0.75 ICC 0.89), and excellent (ICC ! 0.90). Dependencies of the features with tumor volume were assessed using Spearman's correlation coefficient (q). Results: The approach using urine-normalized values together with a smaller bin width (0.05) was the most reproducible (428/552, 78% features with ICC ! 0.75); the fixed-bin count approach was the least (215/552, 39% with ICC ! 0.75). Without normalization, using a fixed bin width of 25, 348/552 (63%) of features had an ICC ! 0.75. Overall, 26% (range 25-30%) of the features were volume-dependent (q ! 0.6). None of the volume-independent shape features were found to be reproducible. Conclusion: Applying normalization prior to features extraction increases the reproducibility of ADCbased radiomics features. When normalization is applied, a fixed-bin width approach with smaller widths is suggested. Ó 2019 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 143 (2020) 88-94 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Cervical cancer is the fourth most frequent cancer in women, with an estimated 570,000 new cases in 2018, representing 6.6% of all female cancers worldwide. Cervical cancer still represents a significant burden for middle-and low-income countries [1]. Standard treatment for locally advanced (stage IB2-IVA) cervical cancer is concurrent chemoradiation.Computed Tomography (CT) and Magnetic Resonance (MR) are the standard imaging modalities for cervical cancer staging and evaluation of treatment response. Through appropriate choice of pulse sequences MR imaging provides greater soft tissue contrast than CT and enables assessment of physiological parameters and biochemical function. Diffusion-weighted imaging (DWI) in MR enables measurement of water diffusivity via generation of Apparent Diffusion Coefficient (ADC) maps, and ADC is an established biomarker of tumor cell density and related changes posttherapy [2]. DWI is increasing acquired in addition to T2weighted MRI to detect cervical tumor [3], and pre-treatment tumor ADC has been shown to be p...

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“…Another phantom-based study by Buch et al 12 assessed the effect of magnet strength, flip angles, number of excitations, and different scanner platforms and concluded that some texture features are more robust (for example, except for histogram-related median, entropy, and GLCM contrast, all other histogram, GLCM, GLRLM, gray-level gradient matrix, and Law features did not show a significant difference from flip angles) and some are more susceptible to acquisition parameters (all Law features were significantly different for different magnetic strengths). Yang et al 70 found that different reconstruction algorithms, noise levels, and parallel imaging acceleration factors can influence texture parameters. Texture features are also affected by a number of coil elements, coil arrangement, and k-space sampling.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

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Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging. ABBREVIATIONS:AUC ϭ area under the curve; CE ϭ contrast-enhanced; GLCM ϭ gray-level co-occurrence matrix; GLRLM ϭ gray-level run-length matrix; HGG ϭ high-grade glioma; IDH ϭ isocitrate dehydrogenase; IDM ϭ inverse difference moment; LGG ϭ low-grade glioma; MRTA ϭ MR imaging texture analysis; PCNSLϭ primary central nervous system lymphoma; PCA ϭ principal component analysis; SVM ϭ support vector machine; TA ϭ texture analysis Indicates open access to non-subscribers at www.ajnr.org Indicates article with supplemental on-line tables.

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Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth

Cited by 87 publications

References 39 publications

Machine and deep learning methods for radiomics

Machine and deep learning methods for radiomics

Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients

Texture Analysis in Cerebral Gliomas: A Review of the Literature

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