Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography

Abstract

Background

Intrapancreatic accessory spleens (IPASs) are usually misdiagnosed as pancreatic neuroendocrine tumors (PNETs). Texture analysis is valuable in tumor detection, diagnosis, and staging.

Purpose

To identify the potential of texture features in differentiating IPASs from small hypervascular PNETs.

Material and Methods

Twenty-one patients with PNETs and 13 individuals with IPASs who underwent pretreatment dynamic contrast-enhanced computed tomography (CT) were retrospectively analyzed. The routine imaging features—such as location, size, margin, cystic or solid appearance, enhancement degree and pattern, and lymph node enlargement—were recorded. Texture features, such as entropy, skewness, kurtosis, and uniformity, on contrast-enhanced images were analyzed. Receiver operating characteristic (ROC) analysis was performed to differentiate IPASs from PNETs.

Results

No significant differences were observed in margin, enhancement degree (arterial and portal phase), lymph node enlargement, or size between PNETs and IPASs (all P > 0.05). However, IPASs usually showed heterogeneous enhancement at the arterial phase and the same degree of enhancement as the spleen at the portal phase, both of which were greater than those of PNETs (69% vs. 35%, P = 0.06; 100% vs. 29%, P = 0.04). Entropy and uniformity were significantly different between IPASs and PNETs at moderate (1.5) and high sigma values (2.5) (both P < 0.01). ROC analysis showed that uniformity at moderate and high sigma had the highest area under the curve (0.82 and 0.89) with better sensitivity (85.0–95.0%) and acceptable specificity (75.0–83.3%) for differentiating IPASs from PNETs.

Conclusions

Texture parameters have potential in differentiating IPASs from PNETs.

Keywords

Intrapancreatic accessory spleens pancreatic neuroendocrine tumors texture analysis computed tomography

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References

Gayer

Zissin

Apter

et al.

CT findings in congenital anomalies of the spleen. Br J Radiol 2001; 74: 767–772.

Wadham

Adams

Johnson

. Incidence and location of accessory spleens. N Engl J Med 1981; 304: 1111–1111.

Kawamoto

Johnson

Hall

et al.

Intrapancreatic accessory spleen: CT appearance and differential diagnosis. Abdom Imaging 2012; 37: 812–827.

Halpert

Alden

. Accessory spleens in or at the tail of the pancreas. A survey of 2,700 additional necropsies. Arch Pathol 1964; 77: 652–654.

Kim

Lee

Han

et al.

MDCT and superparamagnetic iron oxide (SPIO)-enhanced MR findings of intrapancreatic accessory spleen in seven patients. Eur Radiol 2006; 16: 1887–1897.

Kim

Lee

Han

et al.

Intrapancreatic accessory spleen: findings on MR Imaging, CT, US and scintigraphy, and the pathologic analysis. Korean J Radiol 2008; 9: 162–174.

Spencer

Spizarny

Williams

. Imaging features of intrapancreatic accessory spleen. Br J Radiol 2010; 83: 668–673.

Jang

Kim

Lee

et al.

Differentiation of an intrapancreatic accessory spleen from a small solid pancreatic tumor: value of diffusion-weighted MR imaging. Radiology 2013; 266: 159–167.

Coquia

Kawamoto

Zaheer

et al.

Intrapancreatic accessory spleen: possibilities of computed tomography in differentiation from nonfunctioning pancreatic neuroendocrine tumor. J Comput Assist Tomogr 2014; 38: 874–878.

10.

Brasca

Zanello

De Gaspari

et al.

Intrapancreatic accessory spleen mimicking a neuroendocrine tumor: magnetic resonance findings and possible diagnostic role of different nuclear medicinetests. Eur Radiol 2004; 14: 1322–1323.

11.

Kang

Kim

Byun

et al.

Diffusion-weighted MRI: usefulness for differentiating intrapancreatic accessory spleen and small hypervascular neuroendocrine tumor of the pancreas. Acta Radiol 2014; 55: 1157–1165.

12.

Pandey

Ghasabeh

et al.

Accuracy of apparent diffusion coefficient in differentiating pancreatic neuroendocrine tumour from intrapancreatic accessory spleen. Eur Radiol 2018; 28: 1560–1567.

13.

Giganti

Antunes

Salerno

et al.

Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 2017; 27: 1831–1839.

14.

Sacconi

Anzidei

Leonardi

et al.

Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol 2017; 72: 443–450.

15.

Ramkumar

Ranjbar

Ning

et al.

MRI-based texture analysis to differentiate sinonasal squamous cell carcinoma from inverted papilloma. Am J Neuroradiol 2017; 38: 1019–1025.

16.

Kim

Lim

et al.

Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology 2017; 282: 665–675.

17.

Zhang

Tian

et al.

Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 2017; 23: 4259–4269.

18.

Ganeshan

Miles

Young

et al.

Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol 2007; 62: 761–768.

19.

Miles

Ganeshan

Hayball

. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 2013; 13: 400–406.

20.

Goh

Ganeshan

Nathan

et al.

Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 2011; 261: 165–171.

21.

Rao

Lambregts

Schnerr

et al.

Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

United European Gastroenterol J 2014; 2: 530–538.

22.

Miles

Ganeshan

Griffiths

et al.

Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009; 250: 444–452.

23.

Kim

Lee

Han

et al.

MDCT and superparamagnetic iron oxide (SPIO)-enhanced MR findings of intrapancreatic accessory spleen in seven patients. Eur Radiol 2006; 16: 1887–1897.

24.

Herédia

Altun

Bilaj

et al.

Gadolinium- and superparamagnetic-iron-oxide-enhanced MR findings of intrapancreatic accessory spleen in five patients. Magn Reson Imaging 2008; 26: 1273–1278.

25.

Canellas

Burk

Parakh

et al.

Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. Am J Roentgenol 2017; 15: 1–6.

26.

Choi

Kim

et al.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 2018; 59: 383–392.

27.

Summers

. Texture analysis in radiology: Does the emperor have no clothes? Abdom Radiol (NY) 2017; 42: 342–345.

28.

Lubner

Smith

Sandrasegaran

et al.

CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographic 2017; 37: 1483–1503.

29.

Zhou

Zhang

Kang

et al.

Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging 2017; 45: 1476–1485.