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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Radiomic Features Associated with Extent of Resection in Glioma Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:341-347. [PMID: 34862558 DOI: 10.1007/978-3-030-85292-4_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.
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