#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Application of computerized 3D-CT texture analysis of pancreas for the assessment of patients with diabetes


Autoři: Siwon Jang aff001;  Jung Hoon Kim aff002;  Seo-Youn Choi aff004;  Sang Joon Park aff002;  Joon Koo Han aff002
Působiště autorů: Department of Radiology, SMG—SNU Boramae Medical Center, Seoul, Korea aff001;  Department of Radiology, Seoul National University Hospital, Seoul, Korea aff002;  Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea aff003;  Department of Radiology, Soonchunhyang University College of Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Korea aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227492

Souhrn

Objective

To evaluate the role of computerized 3D CT texture analysis of the pancreas as quantitative parameters for assessing diabetes.

Methods

Among 2,493 patients with diabetes, 39 with type 2 diabetes (T2D) and 12 with type 1 diabetes (T1D) who underwent CT using two selected CT scanners, were enrolled. We compared these patients with age-, body mass index- (BMI), and CT scanner-matched normal subjects. Computerized texture analysis for entire pancreas was performed by extracting 17 variable features. A multivariate logistic regression analysis was performed to identify the predictive factors for diabetes. A receiver operator characteristic (ROC) curve was constructed to determine the optimal cut off values for statistically significant variables.

Results

In diabetes, mean attenuation, standard deviation, variance, entropy, homogeneity, surface area, sphericity, discrete compactness, gray-level co-occurrence matrix (GLCM) contrast, and GLCM entropy showed significant differences (P < .05). Multivariate analysis revealed that a higher variance (adjusted OR, 1.002; P = .005), sphericity (adjusted OR, 1.649×104; P = .048), GLCM entropy (adjusted OR, 1.057×105; P = .032), and lower GLCM contrast (adjusted OR, 0.997; P < .001) were significant variables. The mean AUCs for each feature were 0.654, 0.689, 0.620, and 0.613, respectively (P < .05). In subgroup analysis, only larger surface area (adjusted OR, 1.000; P = .025) was a significant predictor for T2D.

Conclusions

Computerized 3D CT texture analysis of the pancreas could be helpful for predicting diabetes. A higher variance, sphericity, GLCM entropy, and a lower GLCM contrast were the significant predictors for diabetes.

Klíčová slova:

Computed axial tomography – diabetes mellitus – Entropy – HbA1c – Insulin – Pancreas – Regression analysis – Type 2 diabetes


Zdroje

1. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). The Lancet. 1998;352(9131):854–65. doi: https://doi.org/10.1016/S0140-6736(98)07037-8.

2. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. 1998;352(9131):837–53. doi: https://doi.org/10.1016/S0140-6736(98)07019-6.

3. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HAW. 10-Year Follow-up of Intensive Glucose Control in Type 2 Diabetes. New England Journal of Medicine. 2008;359(15):1577–89. doi: 10.1056/NEJMoa0806470 18784090

4. Mardon R, Marker D, Nooney J, Campione J, Jenkins F, Johnson M, et al. Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence. Preventing Chronic Disease. 2017;14:E106. doi: 10.5888/pcd14.160572 29101768

5. Winston CB, Mitchell DG, Cutwater EK, Ehrlich SM. Pancreatic signal intensity on Tl- weighted fat saturation MR images: Clinical correlation. Journal of Magnetic Resonance Imaging. 1995;5(3):267–71. doi: 10.1002/jmri.1880050307 7633102

6. Sakata N, Egawa S, Rikiyama T, Yoshimatsu G, Masuda K, Ohtsuka H, et al. Computed Tomography Reflected Endocrine Function of the Pancreas. Journal of Gastrointestinal Surgery. 2011;15(3):525–32. doi: 10.1007/s11605-010-1406-5 21181561

7. Goda K, Sasaki E, Nagata K, Fukai M, Ohsawa N, Hahafusa T. Pancreatic volume in type 1 und type 2 diabetes mellitus. Acta diabetologica. 2001;38(3):145–9. doi: 10.1007/s005920170012 11827436

8. Yokota K, Fukushima M, Takahashi Y, Igaki N, Seino S. Insulin secretion and computed tomography values of the pancreas in the early stage of the development of diabetes. Journal of Diabetes Investigation. 2012;3(4):371–6. doi: 10.1111/j.2040-1124.2012.00212.x PubMed PMID: PMC4019257. 24843592

9. Saisho Y, Butler AE, Meier JJ, Monchamp T, Allen-Auerbach M, Rizza RA, et al. Pancreas volumes in humans from birth to age one hundred taking into account sex, obesity, and presence of type-2 diabetes. Clinical Anatomy (New York, Ny). 2007;20(8):933–42. doi: 10.1002/ca.20543 PubMed PMID: PMC2680737. 17879305

10. Chae H-D, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Computerized Texture Analysis of Persistent Part-Solid Ground-Glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas. Radiology. 2014;273(1):285–93. doi: 10.1148/radiol.14132187 25102296

11. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. European radiology. 2012;22(4):796–802. doi: 10.1007/s00330-011-2319-8 22086561

12. Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim J- H, Sohn C-H. Glioma: Application of Whole-Tumor Texture Analysis of Diffusion-Weighted Imaging for the Evaluation of Tumor Heterogeneity. PLOS ONE. 2014;9(9):e108335. doi: 10.1371/journal.pone.0108335 25268588

13. Hodgdon T, McInnes MDF, Schieda N, Flood TA, Lamb L, Thornhill RE. Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology. 2015;276(3):787–96. doi: 10.1148/radiol.2015142215 25906183.

14. Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma–a quantitative analysis. BMC Medical Imaging. 2017;17. doi: 10.1186/s12880-017-0209-5 28629416; PubMed Central PMCID: PMC5477257.

15. Beckers RCJ, Lambregts DMJ, Schnerr RS, Maas M, Rao S- X, Kessels AGH, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases—A multicentre study. European journal of radiology. 2017;92:64–71. doi: 10.1016/j.ejrad.2017.04.019 28624022

16. Lim S, Bae JH, Chun EJ, Kim H, Kim SY, Kim KM, et al. Differences in pancreatic volume, fat content, and fat density measured by multidetector-row computed tomography according to the duration of diabetes. Acta diabetologica. 2014;51(5):739–48. Epub 2014/03/29. doi: 10.1007/s00592-014-0581-3 24671510.

17. Begovatz P, Koliaki C, Weber K, Strassburger K, Nowotny B, Nowotny P, et al. Pancreatic adipose tissue infiltration, parenchymal steatosis and beta cell function in humans. Diabetologia. 2015;58(7):1646–55. doi: 10.1007/s00125-015-3544-5 25740696

18. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. RadioGraphics. 2017;37(5):1483–503. doi: 10.1148/rg.2017170056 28898189.

19. Ionescu-Tirgoviste C, Gagniuc PA, Gubceac E, Mardare L, Popescu I, Dima S, et al. A 3D map of the islet routes throughout the healthy human pancreas. Scientific Reports. 2015;5:14634. doi: 10.1038/srep14634 https://www.nature.com/articles/srep14634#supplementary-information. 26417671

20. Gilbeau JP, Poncelet V, Libon E, Derue G, Heller FR. The density, contour, and thickness of the pancreas in diabetics: CT findings in 57 patients. AJR American journal of roentgenology. 1992;159(3):527–31. Epub 1992/09/01. doi: 10.2214/ajr.159.3.1503017 1503017.

21. Macauley M, Percival K, Thelwall PE, Hollingsworth KG, Taylor R. Altered Volume, Morphology and Composition of the Pancreas in Type 2 Diabetes. PLOS ONE. 2015;10(5):e0126825. doi: 10.1371/journal.pone.0126825 25950180

22. Davnall F, Yip CSP, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights into Imaging. 2012;3(6):573–89. doi: 10.1007/s13244-012-0196-6 23093486

23. Bayanati H, E. Thornhill R, Souza CA, Sethi-Virmani V, Gupta A, Maziak D, et al. Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? European radiology. 2015;25(2):480–7. doi: 10.1007/s00330-014-3420-6 25216770

24. Bashir U, Siddique MM, McLean E, Goh V, Cook GJ. Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges. American Journal of Roentgenology. 2016;207(3):534–43. doi: 10.2214/AJR.15.15864 27305342

25. Wu C- M, Chen Y-C. Statistical feature matrix for texture analysis. CVGIP: Graphical Models and Image Processing. 1992;54(5):407–19. doi: https://doi.org/10.1016/1049-9652(92)90025-S.

26. Srinivasan G, Shobha G, editors. Statistical texture analysis. Proceedings of world academy of science, engineering and technology; 2008.

27. Xin A, Mizukami H, Inaba W, Yoshida T, Takeuchi Y-k, Yagihashi S. Pancreas Atrophy and Islet Amyloid Deposition in Patients With Elderly-Onset Type 2 Diabetes. The Journal of Clinical Endocrinology & Metabolism. 2017;102(9):3162–71. doi: 10.1210/jc.2016-3735 28505316

28. Matsuda A, Makino N, Tozawa T, Shirahata N, Honda T, Ikeda Y, et al. Pancreatic fat accumulation, fibrosis, and acinar cell injury in the Zucker diabetic fatty rat fed a chronic high-fat diet. Pancreas. 2014;43(5):735–43. Epub 2014/04/11. doi: 10.1097/MPA.0000000000000129 24717823; PubMed Central PMCID: PMC4076101.

29. Chen C, Cohrs CM, Stertmann J, Bozsak R, Speier S. Human beta cell mass and function in diabetes: Recent advances in knowledge and technologies to understand disease pathogenesis. Molecular Metabolism. 2017;6(9):943–57. doi: 10.1016/j.molmet.2017.06.019 PubMed PMID: PMC5605733. 28951820

30. Saisho Y. Pancreas Volume and Fat Deposition in Diabetes and Normal Physiology: Consideration of the Interplay Between Endocrine and Exocrine Pancreas. The Review of Diabetic Studies: RDS. 2016;13(2–3):132–47. doi: 10.1900/RDS.2016.13.132 PubMed PMID: PMC5553763. 28012279

31. Burute N, Nisenbaum R, Jenkins DJ, Mirrahimi A, Anthwal S, Colak E, et al. Pancreas volume measurement in patients with Type 2 diabetes using magnetic resonance imaging-based planimetry. Pancreatology. 2014;14(4):268–74. doi: 10.1016/j.pan.2014.04.031 25062875


Článek vyšel v časopise

PLOS One


2020 Číslo 1
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 3/2024 (znalostní test z časopisu)
nový kurz

Kardiologické projevy hypereozinofilií
Autoři: prof. MUDr. Petr Němec, Ph.D.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Aktuální možnosti diagnostiky a léčby litiáz
Autoři: MUDr. Tomáš Ürge, PhD.

Závislosti moderní doby – digitální závislosti a hypnotika
Autoři: MUDr. Vladimír Kmoch

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#