To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them.
Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied.
Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG.
Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors.
Fischer R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer. 2013;108:479–85. CrossRef
Moscoso A, Aguiar P, Pardo-Montero J, Ruibal A. Textural analysis to assess heterogeneity in breast cancer. Biomark J. 2016;2:1–12.
García-Vicente AM, Soriano-Castrejón A, León-Martín A, Chacón-López-Muñiz I, Muñoz-Madero V, Muñoz-Sánchez MM, et al. Molecular subtypes of breast cancer: metabolic correlation with 18F-FDG PET/CT. Eur J Nucl Med Molec Imag. 2013;40:1304–11. CrossRef
Burger AI, Vargas HA, Apte A, Beattie BJ, Humm JL, Gonen M, et al. PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients. Nucl Med Bio. 2014;41:410–8. CrossRef
Haralick RM, Shanmugam K, Dinstein I. Textural features of image classification. IEEE Trans Syst Man Cyber. 1973;3:610–21. CrossRef
Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4:172–9. CrossRef
Mavi A, Urhan M, Yu JQ, Zhuang H, Houseni M, Cermik TF, et al. Dual time point 18F-FDG PET imaging detects breast cancer with high sensitivity and correlates well with histologic subtypes. J Nucl Med. 2006;47:1440–6. PubMed
Xu D, Kurani AS, Furst JD, Raicu DS. Run-length encoding for volumetric texture. In: The 4th IASTED international conference on visualization, imaging, and image processing. 2004. pp. 452–8.
Molina D, Pérez-Beteta J, Luque B, Arregui E, Calvo M, Borrás JM et al. Tumor heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. Br J Radiol. 2016; 89:20160242.
Yoon H, Kim Y, Kim BS. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol. 2015;12:3648–58. CrossRef
García-Vicente AM, Soriano-Castrejón A, Relea-Calatayud F, Palomar-Muñoz A, León-Martín AA, Chacón-López-Muñiz I, et al. 18-F fluorodeoxyglucose retention index and biological prognostic parameters in breast cancer. Clin Nucl Med. 2012;37:470–6. CrossRef
O’Connor J, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Cancer Res. 2014;21:249–57.
Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velásquez C, Arana E, et al. Influence of gray-level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Med Biol. 2016;78:49–57. CrossRef
Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging. 2015;42:1682–91. CrossRefPubMedPubMedCentral
- Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer
Ana María Garcia-Vicente
María Jesús Tello-Galán
- Springer Japan