Abdominal Radiology

, Volume 42, Issue 12, pp 2882–2889 | Cite as

Texture analysis of FDG PET/CT for differentiating between FDG-avid benign and metastatic adrenal tumors: efficacy of combining SUV and texture parameters

  • Masatoyo NakajoEmail author
  • Megumi Jinguji
  • Masayuki Nakajo
  • Tetsuya Shinaji
  • Yoshiaki Nakabeppu
  • Yoshihiko Fukukura
  • Takashi Yoshiura



To retrospectively investigate the SUV-related and texture parameters individually and in combination for differentiating between F-18-fluorodeoxyglucose (FDG)-avid benign and metastatic adrenal tumors with PET/CT.


Thirteen benign adrenal tumors (BATs) and 22 metastatic adrenal tumors (MATs) with a metabolic tumor volume (MTV) > 10.0 cm3 and SUV ≥ 2.5 were included. SUVmax, MTV, total lesion glycolysis, and four textural parameters [entropy, homogeneity, intensity variability (IV), and size-zone variability] were obtained. These parameters were compared between BATs and MATs using Mann–Whitney U test, and the diagnostic performance was evaluated by the area under the curve (AUC) values derived from the receiver operating characteristic analysis. The diagnostic value of combining SUV and texture parameters was examined using a scoring system.


MATs showed significantly higher SUVmax (p = 0.004), entropy (p = 0.013), IV (p = 0.006), and lower homogeneity (p = 0.019) than BATs. The accuracies for diagnosing MATs were 82.9, 82.9, 85.7, and 71.4% for SUVmax, entropy, IV, and homogeneity, respectively. No significant differences in AUC were found among these parameters (p > 0.05 each). When each parameter was scored as 0 (negative for malignancy) and 1 (positive for malignancy) according to each threshold criterion and the four parameter summed scores 0, 1, and 2 were defined as benignity and 3 and 4 as malignancy, the sensitivity and specificity and accuracy to predict MATs were 100% (22/22), 84.6% (11/13), and 94.3% (33/35), respectively, with 0.97 of the AUC.


The combined use of SUVmax and texture parameters has a potential to significantly increase the diagnostic performance to differentiate between large FDG-avid BATs and MATs.


Adrenal tumor FDG PET/CT SUVmax Textural analysis 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was waived by the institutional review board for this retrospective study.

Supplementary material

261_2017_1207_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 15 kb)


  1. 1.
    Boland GW, Blake MA, Hahn PF, Mayo-Smith WW (2008) Incidental adrenal lesions: principles, techniques, and algorithms for imaging characterization. Radiology 249:756–775CrossRefPubMedGoogle Scholar
  2. 2.
    Caoili EM, Korobkin M, Francis IR, Cohan RH, Dunnick NR (2000) Delayed enhanced CT of lipid-poor adrenal adenomas. AJR Am J Roentgenol 175:1411–1415CrossRefPubMedGoogle Scholar
  3. 3.
    Fujiyoshi F, Nakajo M, Fukukura Y, Tsuchimochi S (2003) Characterization of adrenal tumors by chemical shift low-angle shot MR imaging: comparison of four methods of quantitative evaluation. AJR Am J Roentgenol 180:1649–1657CrossRefPubMedGoogle Scholar
  4. 4.
    Chong S, Ks Lee, Kim HY, et al. (2006) Integrated PET-CT for the characterization of adrenal gland lesions in cancer patients: diagnostic efficacy and interpretation pitfalls. Radiographics 26:1811–1824CrossRefPubMedGoogle Scholar
  5. 5.
    von Schulthess GK, Steinert HC, Hany TF (2006) Integrated PET/CT: current applications and future directions. Radiology 238:405–422CrossRefGoogle Scholar
  6. 6.
    Boland GW, Blake MA, Holalkere NS, Hahn PF (2009) PET/CT for the characterization of adrenal masses in patients with cancer: qualitative versus quantitative accuracy in 150 consecutive patients. AJR Am J Roentgenol 192:956–962CrossRefPubMedGoogle Scholar
  7. 7.
    Blake MA, Slattery JM, Kalra MK, et al. (2006) Adrenal lesions: characterization with fused PET/CT image in patients with proved or suspected malignancy-initial experience. Radiology 238:970–977CrossRefPubMedGoogle Scholar
  8. 8.
    Boland GW, Dwamena BA, Jagtiani Sangwaiya M, et al. (2011) Characterization of adrenal masses by using FDG PET: a systematic review and meta-analysis of diagnostic test performance. Radiology 259:117–126CrossRefPubMedGoogle Scholar
  9. 9.
    Low G, Dhliwayo H, Lomas DJ (2012) Adrenal neoplasms. Clin Radiol 67:988–1000CrossRefPubMedGoogle Scholar
  10. 10.
    El Naqa I, Grigsby P, Apte A, et al. (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 42:1162–1171CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pugachev A, Ruan S, Carlin S, et al. (2005) Dependence of FDG uptake on tumor microenvironment. Int J Radiat Oncol Biol Phys 62:545–553CrossRefPubMedGoogle Scholar
  12. 12.
    van Baardwijk A, Bosmans G, van Suylen RJ, et al. (2008) Correlation of intra-tumour heterogeneity on 18F-FDG PET with pathologic features in non-small cell lung cancer: a feasibility study. Radiother Oncol 87:55–58CrossRefPubMedGoogle Scholar
  13. 13.
    van Velden FH, Cheebsumon P, Yaqub M, et al. (2011) Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 38:1636–1647CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Asselin MC, O’Connor JP, Boellaard R, Thacker NA, Jackson A (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48:447–455CrossRefPubMedGoogle Scholar
  15. 15.
    Cook GJ, Yip C, Siddique M, et al. (2013) Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med 54:19–26CrossRefPubMedGoogle Scholar
  16. 16.
    Werner RA, Kroiss M, Nakajo M, et al. (2016) Assessment of tumor heterogeneity in treatment-naïve adrenocortical cancer patients using 18F-FDG positron emission tomography. Endocrine 53:791–800CrossRefPubMedGoogle Scholar
  17. 17.
    Tn S, Kligerman S, Chen W, et al. (2013) Spatial-temporal [18F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys 85:1375–1382CrossRefGoogle Scholar
  18. 18.
    Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D (2013) Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging 40:1662–1671CrossRefPubMedGoogle Scholar
  19. 19.
    Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49:1012–1016CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Tixier F, Hatt M, Le Rest CC, et al. (2012) Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 53:693–700CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gebejes A, Huertas R (2013) Texture characterization based on grey-level co-occurrence matrix. Proc ICTIC 2:375–378Google Scholar
  22. 22.
    Cheng NM, Fang YH, Lee LY, et al. (2015) Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging 42:419–428CrossRefPubMedGoogle Scholar
  23. 23.
    Tixier F, Le Rest CC, Hatt M, et al. (2011) Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Orlhac F, Soussan M, Maisonobe JA, et al. (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55:414–422CrossRefPubMedGoogle Scholar
  25. 25.
    Hatt M, Majdoub M, Vallières M, et al. (2015) 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med 56:38–44CrossRefPubMedGoogle Scholar
  26. 26.
    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35CrossRefPubMedGoogle Scholar
  27. 27.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMedGoogle Scholar
  28. 28.
    Metser U, Miller E, Lerman H, et al. (2006) 18F-FDG PET/CT in the evaluation of adrenal masses. J Nucl Med 47:32–37PubMedGoogle Scholar
  29. 29.
    Groussin L, Bonardel G, Silvéra S, et al. (2009) 18F-Fluorodexyglucose positron emission tomography for the diagnosis of adrenocortical tumors: a prospective study in 77 operated patients. J Clin Endocrinol Metab 94:1713–1722CrossRefPubMedGoogle Scholar
  30. 30.
    Shulkin BL, Thompson NW, Shapiro B, Francis IR, Sisson JC (1999) Pheochromocytomas: imaging with 2-[Fluorine-18] fluoro-2-deoxy-d-glucose PET. Radiology 212:35–41CrossRefPubMedGoogle Scholar
  31. 31.
    Taïeb D, Sebag F, Barlier A, et al. (2009) 18F-FDG avidity of pheochromocytomas and paragangliomas: a new molecular imaging signature? J Nucl Med 50:711–717CrossRefPubMedGoogle Scholar
  32. 32.
    Kim YI, Cheon GJ, Paeng JC, et al. (2014) Total lesion glycolysis as the best 18F-FDG PET/CT parameter in differentiating intermediate-high risk adrenal incidentaloma. Nucl Med Commun 35:606–612CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Masatoyo Nakajo
    • 1
    Email author
  • Megumi Jinguji
    • 1
  • Masayuki Nakajo
    • 2
  • Tetsuya Shinaji
    • 3
  • Yoshiaki Nakabeppu
    • 1
  • Yoshihiko Fukukura
    • 1
  • Takashi Yoshiura
    • 1
  1. 1.Department of Radiology, Graduate School of Medical and Dental SciencesKagoshima UniversityKagoshimaJapan
  2. 2.Department of RadiologyNanpuh HospitalKagoshimaJapan
  3. 3.Department of Nuclear MedicineUniversity of WürzburgWürzburgGermany

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