Quantifying the robustness of [ 18 F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients

Belli, Maria Luisa; Mori, Martina; Broggi, S.; Cattaneo, Giovanni Mauro; Bettinardi, Valentino; Dell’Oca, I.; Fallanca, Federico; Passoni, P.; Vanoli, Emilia Giovanna; Calandrino, R.; Muzio, N. Di; Picchio, Maria; Fiorino, Claudio

doi:10.1016/j.ejmp.2018.05.013

Cited by 56 publications

(41 citation statements)

References 31 publications

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“…The NEMA NU 2-2012 IQ phantom was used in this study consisting of a background volume of 9400 ml and six fillable spheres with inner diameters of 10,13,17,22,28, and 37 mm. The phantom was filled with different 18 F-FDG concentrations.…”

Section: A1 Nema Image Quality Phantommentioning

confidence: 99%

“…Another challenge facing radiomic features is the establishment of their measurement error (i.e., reproducibility, repeatability, and reliability). Several studies have shown that the majority of the 18 F‐FDG PET radiomic features are sensitive to numerous sources such as image acquisition, reconstruction protocols, or delineation method . Our study investigates the relationship of multiple confounding factors, including different activity uptake levels, thereby simulating tracers showing differences in uptake.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have shown that the majority of the 18 F-FDG PET radiomic features are sensitive to numerous sources such as image acquisition, reconstruction protocols, or delineation method. 9,[11][12][13][14][15][16][17][18][19] Our study investigates the relationship of multiple confounding factors, including different activity uptake levels, thereby simulating tracers showing differences in uptake. For this purpose, two phantoms (the NEMA image quality (IQ) phantom and a phantom containing in-house designed 3D printed inserts simulating realistic heterogeneity uptake) were scanned.…”

Section: Introductionmentioning

confidence: 99%

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Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

et al. 2018

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Background 18F‐fluoro‐2‐deoxy‐D‐Glucose positron emission tomography (18F‐FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18F‐FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method. Methods The NEMA image quality phantom was scanned with various sphere‐to‐background ratios (SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET‐based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins (FBN) and a fixed bin width (FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates. Results In general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL‐compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point‐spread‐function (PSF) resulted in the highest repeatability when compared with OSEM or time‐of‐flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT‐based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL‐compliant reconstruction and larger high uptake spheres). Conclusion Feature space reduction and repeatability of 18F‐FDG PET radiomic features depended on all studied fa...

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Section: A1 Nema Image Quality Phantommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

et al. 2018

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“…Most prior studies have focused solely on the effects of inter-observer variability in manual tumor delineation to identify radiomics features with high robustness [17,18]. In fact, quanti cation of tumor delineation and tolerance assessment of the differences are likely more important in developing standardized research.…”

Section: Introductionmentioning

confidence: 99%

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

Zhang

Zhong

Zhang

et al. 2019

Preprint

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Background Manual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer. Methods This study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC). Results The larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p > 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725, p = 0.001; dilation: 0.800 vs. 0.725, p = 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations. Conclusions Differences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard.

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“…Unlike in lung cancer patients, 18 F-FDG PET has already been established to play a role in the application of target volume contouring [21]. In addition, the artifacts caused by tumor location, anatomical boundaries, clinical situations, and patient cooperation during examination have a great impact on the quality of the contouring [22,23]. 18 F-FDG, a marker of glycolysis in cells, has a short half-life, high imaging spatial resolution, and excellent nuclear and chemical features [24], which make it the most widely used radioactive tracer for PET.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Hybrid PET/MRI for Gross Tumor Volume (GTV) Delineation in Liver Cancer Radiotherapy

Zhang

et al. 2020

Preprint

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Background: Hybrid 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/magnetic resonance imaging (PET/MRI) has been increasingly incorporated into the practice of radiation oncologists since it contains both anatomical and biological data and may bring about personalized radiation plans for each patient. The objective of this study was to evaluate the feasibility of gross tumor volume (GTV) delineation from hybrid PET/MRI compared with that from current-practice MRI during radiotherapy planning in patients with liver cancer. Methods: Twelve patients (eighteen lesions) with liver cancer were enrolled in this study. We chose one of the most popular delineating methods—the visual method—in this study, and three physicians delineated the target volume of each lesion from MRI, PET, and hybrid PET/MRI images. The difference and correlation of GTV values obtained by MRI, PET and hybrid PET/MRI were subjected to statistical analysis. In addition, the Dice similarity coefficient (DSC) was calculated to assess the spatial overlap. GTV-MRI was set as a reference. Results: Most GTV-PET/MRI (83%) and 50% of GTV-PET were larger than the reference GTV-MRI. Statistical analysis revealed that GTV-PET/MRI (p=0.021) diverged statistically significantly from the reference GTV-MRI. In contrast, GTV-PET (p=0.266) was not significantly different from GTV-MRI. GTV-PET (r=0.991, p<0.001) and GTV-PET/MRI (r=0.997, p<0.001) were significantly related to GTV-MRI. The average DSC value between GTV-MRI and GTV-PET was 0.45 (range 0–0.90) and that between GTV-MRI and GTV-PET/MRI was 0.76 (range 0.43–0.90). Conclusions: With the database used, PET/MRI-based target volume delineation for liver cancer is feasible. The larger GTV-PET/MRI may allow adequate irradiation of the diseased tissue and improved treatment effect.

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Quantifying the robustness of [ 18 F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients

Cited by 56 publications

References 31 publications

Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

Evaluation of Hybrid PET/MRI for Gross Tumor Volume (GTV) Delineation in Liver Cancer Radiotherapy

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Quantifying the robustness of [ 18 F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients

Cited by 56 publications

References 31 publications

Repeatability of 18F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

Repeatability of 18F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

Evaluation of Hybrid&nbsp;PET/MRI&nbsp;for Gross Tumor Volume (GTV) Delineation in Liver Cancer Radiotherapy

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Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

Repeatability of ¹⁸F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

Evaluation of Hybrid PET/MRI for Gross Tumor Volume (GTV) Delineation in Liver Cancer Radiotherapy