Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer

Soussan, Michaël; Orlhac, Fanny; Boubaya, Marouane; Zelek, Laurent; Ziol, Marianne; Eder, Véronique; Buvat, Irène

doi:10.1371/journal.pone.0094017

Cited by 138 publications

(120 citation statements)

References 28 publications

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“…Second, the correlation between F-18 FDG uptake and molecular subtype was evaluated by using only SUVmax. Further investigations with other parameters such as SUVmean, SUVpeak, or tumor heterogeneity measured on F-18 FDG PET/CT are warranted [36,37].…”

Section: Discussionmentioning

confidence: 99%

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Lee

Bae

Park³

et al. 2016

Nucl Med Mol Imaging

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Purpose This study aimed to investigate the relationship between the SUVmax of primary breast cancer lesions and the molecular subtypes based on the recommendations of the St. Gallen consensus meeting 2013. Methods Clinical records of patients who underwent F-18 FDG PET/CT for initial staging of invasive ductal carcinoma (IDC) of the breast were reviewed. A total of 183 patients were included. SUVmax was correlated with the molecular subtypes defined by the St. Gallen Consensus Meeting 2013, i.e., luminal A-like (LA), luminal B-like HER2 negative (LBHER2-), luminal Blike HER2 positive (LBHER2+), HER2 positive (HER2+), and triple negative (TN), and with the clinicohistopathologic characteristics. Results The molecular subtype was LA in 38 patients, LBHER2-in 72, LBHER2+ in 21, HER2+ in 30, and TN in 22. The mean SUVmax in the LA, LBHER2-, LBHER2+, HER2+, and TN groups were 4.5 ± 2.3, 7.2 ± 4.9, 7.2 ± 4.3, 10.2 ± 5.5, and 8.8 ± 7.1, respectively. Although SUVmax differed significantly among these subtypes (p < 0.001), the values showed a wide overlap. Optimal cut-off SUVmax to differentiate LA from LBHER2-, LBHER2+, HER2+ and TN were 5.9, 5.8, 7.5, and 10.2 respectively, with area under curve (AUC) of 0.648, 0.709, 0.833, and 0.697 respectively. The cut-off value of 5.9 yielded the highest accuracy for differentiation between the LA and non-LA subtypes, with sensitivity, specificity, and AUC of 79.4 %, 57.9 %, and 0.704 respectively. Conclusion The SUVmax showed a significant correlation with the molecular subtype. Although SUVmax measurements could be used along with immunohistochemical analysis for differentiating between molecular subtypes, its application to individual patients may be limited due to the wide overlaps in SUVmax.

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Section: Discussionmentioning

confidence: 99%

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Lee

Bae

Park³

et al. 2016

Nucl Med Mol Imaging

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“…The patient cohort and imaging protocol were described previously (5,11). The Institutional Review Board approved this retrospective study, and the requirement to obtain informed consent was waived (Ile-de-France X).…”

Section: Patients and Pet Acquisitionsmentioning

confidence: 99%

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

et al. 2016

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The use of texture indices to characterize tumor heterogeneity from PET images is being increasingly investigated in retrospective studies, yet the interpretation of PET-derived texture index values has not been thoroughly reported. Furthermore, the calculation of texture indices lacks a standardized methodology, making it difficult to compare published results. To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Methods: Ten sphere models mimicking different activity distribution patterns and the 18 F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured. The values of texture indices and how they changed as a function of the activity distribution were assessed and compared with the visual assessment of tumor heterogeneity. Results: Using the sphere models and real tumors, we identified 2 sets of texture indices reflecting different types of uptake heterogeneity. Set 1 included homogeneity, entropy, short-run emphasis, and long-run emphasis, all of which were sensitive to the presence of uptake heterogeneity but did not distinguish between hyper-and hyposignal within an otherwise uniform activity distribution. Set 2 comprised high-gray-level-zone emphasis and low-gray-level-zone emphasis, which were mostly sensitive to the average uptake rather than to the uptake local heterogeneity. Four of 6 texture indices significantly differed between homogeneous and heterogeneous lesions as defined by 2 nuclear medicine physicians (P , 0.05). All texture index values were sensitive to voxel size (variations up to 85.8% for the most homogeneous sphere models) and edge effects (variations up to 29.1%). Conclusion: Unlike a previous report, our study found that variations in texture indices were intuitive in the sphere models and real tumors: the most homogeneous uptake distribution exhibited the highest homogeneity and lowest entropy. Two families of texture index reflecting different types of uptake patterns were identified. Variability in texture index values as a function of voxel size and inclusion of tumor edges was demonstrated, calling for a standardized calculation methodology. This study provides guidance for nuclear medicine physicians in interpreting texture indices in future studies and clinical practice. Ani ncreasing number of studies focus on the characterization of tumor heterogeneity based on texture analysis from CT, MRI, ultrasound images, and, since 2009, PET images (1). The calculation of tumor heterogeneity indices from medical images offers the benefits of being noninvasive, accounting for the whole tumor, and being repeatable during treatment. These indices, if proven, could be used to reliably identify the existence of subpopulations of cells with distinct genomic alterations and could guide the choice of treatment, especially for targeted therapeutics (2). In PET, several retrospective studies suggest that texture indices reflect ...

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“…Subsequently, there are increasing numbers of publications related to the application of TFs in PET tumor imaging, mostly with 18 F-FDG (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). These previous studies mainly focused on the predictive and prognostic value (5-10,13-25) of TF and also the potential use in radiotherapy planning (20,21).…”

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confidence: 99%

Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET

et al. 2015

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Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-offlight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUV peak ), maximum SUV, and mean SUV (SUV mean ). Methods: This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body 18 F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated. Results: SUV peak , SUV mean , 18 TFs, and 1 FOS were the most robust (COV # 5%) whereas skewness, cluster shade, and zone percentage were the least robust (COV . 20%) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUV peak and SUV mean exhibited variation with a COV less than 5%; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5%-10%. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5%, whereas SUV peak and SUV mean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUV peak and SUV mean , having a COV less than 10%. Conclusion: Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.

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Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer

Cited by 138 publications

References 28 publications

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET

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Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer

Cited by 138 publications

References 28 publications

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET

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Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET