A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics

Gallivanone, Francesca; D’Ambrosio, Daniela; Carne, Irene; D'Arcangelo, Micol; Montagna, P.; Giroletti, E.; Poggi, Paolo; Vellani, C.; Moro, Luca; Castiglioni, Isabella

doi:10.1016/j.ejmp.2022.04.007

Cited by 6 publications

(6 citation statements)

References 43 publications

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“…However, most of the existing phantoms are limited in their abilities to repeat and reproduce the heterogeneous nature of the tumor particularly in multicenter studies.Recently,Gallivanone et al,reported the reliability of radiomics metrics using a novel multimodality image phantom suitable for PET, CT and multiparametric MRI imaging. 27,28 However, due to the limitations of the process of creating the phantom, 3D digital images of the synthetic lesion were not obtained.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Gallivanone et al., reported the reliability of radiomics metrics using a novel multimodality image phantom suitable for PET, CT and multiparametric MRI imaging. 27 , 28 However, due to the limitations of the process of creating the phantom, 3D digital images of the synthetic lesion were not obtained. While the use of a multimodality phantom is valuable for cross‐imaging radiomics validation, results from our study, could help identify 3D printed texture patterns that could be used in such phantoms.…”

Section: Discussionmentioning

confidence: 99%

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Technical and clinical considerations of a physical liver phantom for CT radiomics analysis

Varghese,

Cen,

Jensen

et al. 2024

J Applied Clin Med Phys

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ObjectiveThis study identifies key characteristics to help build a physical liver computed tomography (CT) phantom for radiomics harmonization; particularly, the higher‐order texture metrics.Materials and methodsCT scans of a radiomics phantom comprising of 18 novel 3D printed inserts with varying size, shape, and material combinations were acquired on a 64‐slice CT scanner (Brilliance 64, Philips Healthcare). The images were acquired at 120 kV, 250 mAs, CTDIvol of 16.36 mGy, 2 mm slice thickness, and iterative noise‐reduction reconstruction (iDose, Philips Healthcare, Andover, MA). Radiomics analysis was performed using the Cancer Imaging Phenomics Toolkit (CaPTk), following automated segmentation of 3D regions of interest (ROI) of the 18 inserts. The findings were compared to three additional ROI obtained of an anthropomorphic liver phantom, a patient liver CT scan, and a water phantom, at comparable imaging settings. Percentage difference in radiomic metrics values between phantom and tissue was used to assess the biological equivalency and <10% was used to claim equivalent.ResultsThe HU for all 18 ROI from the phantom ranged from −30 to 120 which is within clinically observed HU range of the liver, showing that our phantom material (T3‐6B) is representative of biological CT tissue densities (liver) with >50% radiomic features having <10% difference from liver tissue. Based on the assessment of the Neighborhood Gray Tone Difference Matrix (NGTDM) metrics it is evident that the water phantom ROI show extreme values compared to the ROIs from the phantom. This result may further reinforce the difference between a structureless quantity such as water HU values and tissue HU values found in liver.ConclusionThe 3‐D printed patterns of the constructed radiomics phantom cover a wide span of liver tissue textures seen in CT images. Using our results, texture metrics can be selectively harmonized to establish clinically relevant and reliable radiomics panels.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Technical and clinical considerations of a physical liver phantom for CT radiomics analysis

Varghese,

Cen,

Jensen

et al. 2024

J Applied Clin Med Phys

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“…For this point, we reinforce in particular hybrid systems such as MR-LINAC and PET-MR, radiomics, artificial intelligence and multi-modal imaging. These topics represent important fields of research and development in oncology clinical trials, as evidenced by numerous recently published articles, including some position papers from international scientific societies [180][181][182][183][184][185][186][187][188][189][190][191][192][193][194][195][196][197][198][199]. The second is that it was not possible to follow a rigorous selection criterion to include the articles, given the high number of manuscripts published in recent years on QIBs.…”

Section: Discussionmentioning

confidence: 99%

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Chauvie,

Mazzoni,

O’Doherty

2023

Tomography

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Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on “the role of the MPEs in clinical trials”-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

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“…As the intensity value of the MR image depends on the imaging protocol and scanner used, the intensities in the original MR images were normalized to a value between 0 and 255. Tissue inhomogeneity may lead to significant misclassification in the segmentation procedure [42,43], therefore, A multiplicative intrinsic component optimization (MICO) [42], was also applied to the images to correct the inhomogeneity through a specific tissue and prevent the overlay between the ranges of the intensities of different tissues. In the next step, to limit the image to brain borders, the extra background parts of the original images with the dimension of 240 × 240 were cropped and then reduced to 224 × 224 using the nearest-neighbor interpolation technique, because the input of our developed deep neural network, Deep-Net, needs an image input with the size of 224 × 224 × 3, where 3 is RGB channels.…”

Section: Data Preparation and Image Preprocessingmentioning

confidence: 99%

Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights

2022

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A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics

Cited by 6 publications

References 43 publications

Technical and clinical considerations of a physical liver phantom for CT radiomics analysis

Technical and clinical considerations of a physical liver phantom for CT radiomics analysis

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights

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