A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images

Forgács, Attila; Jonsson, Hermann Pall; Dahlbom, Magnus; Daver, Freddie; DiFranco, Matthew D.; Opposits, Gábor; Krizsán, Áron Krisztián; Garai, Ildikó; Czernin, Johannes; Varga, József; Trón, Lajos; Balkay, László

doi:10.1371/journal.pone.0164113

Cited by 38 publications

(41 citation statements)

References 38 publications

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“…Second-order features were calculated from GLCMs measuring the grey-level distribution between pairs of voxels and high-order features were derived from three-dimensional matrices taking into consideration neighbouring voxels in adjacent planes. All these features have been previously described in detail [ 13 , 14 ] and the chosen parameters have previously shown utility and/or robustness when used in clinical 18 F-FDG PET data of cancers [ 17 – 22 ].…”

Section: Methodsmentioning

confidence: 99%

Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET

Cook

Lovat

Siddique

et al. 2017

Eur J Nucl Med Mol Imaging

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PurposeMeasurement of heterogeneity in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images is reported to improve tumour phenotyping and response assessment in a number of cancers. We aimed to determine whether measurements of 18F-FDG heterogeneity could improve differentiation of benign symptomatic neurofibromas from malignant peripheral nerve sheath tumours (MPNSTs).Methods 18F-FDG PET data from a cohort of 54 patients (24 female, 30 male, mean age 35.1 years) with neurofibromatosis-1 (NF1), and clinically suspected malignant transformation of neurofibromas into MPNSTs, were included. Scans were performed to a standard clinical protocol at 1.5 and 4 h post-injection. Six first-order [including three standardised uptake value (SUV) parameters], four second-order (derived from grey-level co-occurrence matrices) and four high-order (derived from neighbourhood grey-tone difference matrices) statistical features were calculated from tumour volumes of interest. Each patient had histological verification or at least 5 years clinical follow-up as the reference standard with regards to the characterisation of tumours as benign (n = 30) or malignant (n = 24).ResultsThere was a significant difference between benign and malignant tumours for all six first-order parameters (at 1.5 and 4 h; p < 0.0001), for second-order entropy (only at 4 h) and for all high-order features (at 1.5 h and 4 h, except contrast at 4 h; p < 0.0001–0.047). Similarly, the area under the receiver operating characteristic curves was high (0.669–0.997, p < 0.05) for the same features as well as 1.5-h second-order entropy. No first-, second- or high-order feature performed better than maximum SUV (SUVmax) at differentiating benign from malignant tumours.Conclusions 18F-FDG uptake in MPNSTs is higher than benign symptomatic neurofibromas, as defined by SUV parameters, and more heterogeneous, as defined by first- and high-order heterogeneity parameters. However, heterogeneity analysis does not improve on SUVmax discriminative performance.

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Section: Methodsmentioning

confidence: 99%

Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET

Cook

Lovat

Siddique

et al. 2017

Eur J Nucl Med Mol Imaging

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“…In department A, 18 F-FDG PET/CT images were acquired using a Gemini TF scanner (Philips) at 78 6 9 min (mean 6 SD; range, 59-108 min) after injection of 18 F-FDG (3 MBq/kg) at a rate of 1.45 min per bed position. PET images were reconstructed using a list-mode iterative algorithm (blob ordered-subsets time-of-flight, 2 iterations, 33 subsets).…”

Section: Pet/ct Imaging Protocolmentioning

confidence: 99%

“…In department B, 18 F-FDG PET/CT images were acquired using a Discovery 690 scanner (GE Healthcare) at 74 6 8 min (range, 55-99 min) after injection of 18 F-FDG (3-3.5 MBq/kg) at a rate of 2.5 min per bed position. PET images were reconstructed using an ordered-subset expectation maximization iterative algorithm (2 iterations, 24 subsets) and gaussian postfiltering (6 mm in full width at half maximum).…”

Section: Pet/ct Imaging Protocolmentioning

confidence: 99%

“…Indeed, it has been shown that radiomic features are sensitive to acquisition and reconstruction parameters (12,13), thus hindering the pooling of data acquired using different scanners or protocols. More precisely, radiomic features are sensitive to the reconstruction algorithm, number of iterations or subsets, scan duration per bed position, postreconstruction filter, and voxel size (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). This variability of radiomic features implies that a radiomic model established using data from a given PET scanner might not be directly applicable to data from another PET scanner, as recently demonstrated in cervical cancer by Reuzé et al (23).…”

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confidence: 99%

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A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET

Orlhac¹,

Boughdad²,

Philippe³

et al. 2018

J Nucl Med

288

250

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Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols. PretreatmentF-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization. In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S ( < 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments ( > 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department. The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.

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“…Therefore, textural feature extraction and assessment through radiomics have been proposed as a promising means for a more encompassing characterization of lesions and lesion heterogeneity. 11,12 Nevertheless, the reproducibility and comparability of radiomics features is still a matter of debate, [13][14][15][16][17] and standardization efforts are yet to be expanded. Therefore, optimum standardized imaging objects along with imaging and analysis procedures are needed for facilitating the definition of benchmark parameters for pathology-specific imaging applications.…”

Section: Introductionmentioning

confidence: 99%

Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs

2020

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Background: In oncology, lesion characterization is essential for tumor grading, treatment planning, and follow-up of cancer patients. Hybrid imaging systems, such as Single Photon Emission Computed Tomography (SPECT)/CT, Positron Emission Tomography (PET)/CT, or PET/magnetic resonance imaging (MRI), play an essential role for the noninvasive quantification of tumor characteristics. However, most of the existing approaches are challenged by intra-and intertumor heterogeneity. Novel quantitative imaging parameters that can be derived from textural feature analysis (as part of radiomics) are promising complements for improved characterization of tumor heterogeneity, thus, supporting clinically relevant implementations of personalized medicine concepts. Nevertheless, establishing new quantitative parameters for tumor characterization requires the use of standardized imaging objects to test the reliability of results prior to their implementation in patient studies. Methods: In this review, we summarize existing reports on heterogeneous phantoms with a focus on simulating tumor heterogeneity. We discuss the techniques, materials, advantages, and limitations of the existing phantoms for PET, CT, and MR imaging modalities. Conclusions: Finally, we outline the future directions and requirements for the design of cross modality imaging phantoms.

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A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images

Cited by 38 publications

References 38 publications

Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET

Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET

A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET

Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs

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