Looking ahead Opportunities and challenges in radiomics and radiogenomics

Li, Ruijiang; Wu, Yan; Gensheimer, Michael F.; Khuzani, Masoud Badiei; Xing, Lei

doi:10.1201/9781351208277-22

Cited by 8 publications

(11 citation statements)

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“…BNs 32, 64, 128, and 256 were chosen, according to values as described in the most recent literature. 3,10,13,15 The discretized gray-levels I FBN were generated using the following equation:…”

Section: Fixed Bin Numbermentioning

confidence: 99%

“…Quantitative radiomic analysis of PET images aims to capture such texture patterns to be used as potential biomarkers of therapeutic response. 1,3,4 An essential computational pre-processing step that is essential to PET radiomics is image discretization (i.e., the process of resampling voxel intensity, consequently narrowing the dynamic range of the image). [5][6][7] By resampling the intensity values into fewer discrete states, discretization is particularly important in PET radiomics because it reduces the sparsity of texture matrices that ultimately define metabolic heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, FBS is an absolute resampling technique that uses predefined bin widths/sizes, but varying bin numbers, to resample an ROI's intensity values. 3 Fig. 1 General radiomics workflow, highlighting discretization.…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, PET imaging may be able to characterize biological heterogeneity of HNSCC tumors, leading to better representation of the disease. Quantitative radiomic analysis of PET images aims to capture such texture patterns to be used as potential biomarkers of therapeutic response 1 , 3 , 4 …”

Section: Introductionmentioning

confidence: 99%

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Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma

Riley,

Stevens,

et al. 2024

J. Med. Imag.

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We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer.Approach: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N ¼ 69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18 fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSOregularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo crossvalidation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model.Results: FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p ¼ 0.0158 and p ¼ 0.0180, respectively; log-rank test).

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Section: Fixed Bin Numbermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma

Riley,

Stevens,

et al. 2024

J. Med. Imag.

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“…Radiomics aims to extract quantitative image features from medical images to identify valuable biomarkers of underlying cancer biology. 1 – 4 These features in combination with machine learning algorithms can be used for diagnosis and to predict clinical outcomes and/or treatment response. 5 – 7 In addition, association of these imaging features with cancer genomics or other patient information may further describe the fundamental biology.…”

Section: Introductionmentioning

confidence: 99%

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines

et al. 2020

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Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials. © The Authors.Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features

Rahmim,

Toosi,

Salmanpour

et al. 2022

2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Background: Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. Methods: We provide illustrative instances of TR implementation in positron emission tomographycomputed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms.Results: Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy.TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS.

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Looking ahead Opportunities and challenges in radiomics and radiogenomics

Cited by 8 publications

References 1 publication

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines

Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features

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