دورية أكاديمية
Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features
| العنوان: | Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features |
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| المؤلفون: | Rahmim, Arman, Toosi, Amirhosein, Salmanpour, Mohammad R., Dubljevic, Natalia, Janzen, Ian, Shiri Lord, Isaac, Yuan, Ren, Ho, Cheryl, Zaidi, Habib, MacAulay, Calum, Uribe, Carlos, Yousefirizi, Fereshteh |
| المصدر: | ISSN: 2223-4306 ; Quantitative imaging in medicine and surgery, vol. 13, no. 12 (2023) p. 7680-7694. |
| سنة النشر: | 2023 |
| المجموعة: | Université de Genève: Archive ouverte UNIGE |
| مصطلحات موضوعية: | info:eu-repo/classification/ddc/616.0757, Imaging biomarkers, Image fusion, Machine learning (ML), Outcome/disease prediction, Radiomics |
| الوصف: | 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 tomography-computed 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/38106259; https://archive-ouverte.unige.ch/unige:174987Test; unige:174987 |
| الإتاحة: | https://doi.org/10.21037/qims-23-163Test https://archive-ouverte.unige.ch/unige:174987Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.AE0E4AFA |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |