Top

Published in:

Open Access 01-12-2020 | Biomarkers | Research article

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Authors: Dan Zhang, Xiaojiao Li, Liang Lv, Jiayi Yu, Chao Yang, Hua Xiong, Ruikun Liao, Bi Zhou, Xianlong Huang, Xiaoshuang Liu, Zhuoyue Tang

Published in: BMC Medical Imaging | Issue 1/2020

Abstract

Background

Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.

Methods

The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).

Results

Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001).

Conclusions

Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.

Izzo L, Casullo A, Caputo M, et al. Space occupying lesions of parotid gland. Comparative diagnostic imaging and pathological analysis of echo color /power Doppler and of magnetic resonance imaging. Acta Otorhinolaryngol Ital. 2006;26(3):147–53.PubMedPubMedCentral

Hilton JM, Phillips JS, Hellquist HB, et al. Multifocal multi-site Warthin tumour. Eur Arch Otorhinolaryngol. 2008;265(12):1573–5. https://doi.org/10.1007/s00405-008-0622-z.CrossRefPubMed

Chulam TC, Noronha Francisco AL, Goncalves Filho J, et al. Warthin’s tumour of the parotid gland: our experience. Acta Otorhinolaryngol Ital. 2013;33(6):393–7.PubMedPubMedCentral

Yu C, Song Z, Xiao Z, et al. Mucoepidermoid carcinoma arising in Warthin’s tumor of the parotid gland: clinicopathological characteristics and immunophenotypes. Sci Rep. 2016;6:30149. https://doi.org/10.1038/srep30149.CrossRefPubMedPubMedCentral

Iro H, Zenk J. Role of extracapsular dissection in surgical management of benign parotid tumors. JAMA Otolaryngol Head Neck Surg. 2014;140(8):768–9. https://doi.org/10.1001/jamaoto.2014.1218.CrossRefPubMed

Park SJ, Han S, Lee HJ, et al. Preservation of salivary function following extracapsular dissection for tumors of the parotid gland. J Oral Maxillofac Surg. 2018;76(9):2004–10. https://doi.org/10.1016/j.joms.2018.03.033.CrossRefPubMed

van Dijk LV, Brouwer CL, van der Schaaf A, et al. CT image biomarkers to improve patient-specifc prediction of radiation-induced xerostomia and sticky saliva. Radiother Oncol. 2016;122(2):185–91. https://doi.org/10.1016/j.radonc.2016.07.007.CrossRefPubMed

Nardone V, Tini P, Nioche C, et al. Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: a subset analysis. Radiother Oncol. 2017;122(2):321. https://doi.org/10.1016/j.radonc.2016.09.004.CrossRefPubMed

Cui Y, Song J, Pollom E, et al. Quantitative analysis of (18) F-fluorodeoxyglucose positron emission tomography identifies novel prognostic imaging biomarkers in locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2016;96(1):102–9. https://doi.org/10.1016/j.ijrobp.%202016.04.034.CrossRefPubMed

10.

Chen S, Zhu Y, Liu Z, et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: a retrospective pilot study. Eur J Radiol. 2017;90:198–204. https://doi.org/10.1016/j.ejrad.2017.02.035.CrossRefPubMed

11.

Canellas R, Burk KS, Parakh A, et al. Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. AJR Am J Roentgenol. 2018;210(2):341–6. https://doi.org/10.2214/AJR.17.18417.CrossRefPubMed

12.

Liu J, Mao Y, Li Z, et al. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging. 2016;44(2):445–55. https://doi.org/10.1002/jmri.25156.CrossRefPubMed

13.

Ciuman RR, Oels W, Jaussi R, et al. Outcome, general, and symptom-specific quality of life after various types of parotid resection. Laryngoscope. 2012;122(6):1254–61. https://doi.org/10.1002/lary.23318.CrossRefPubMed

14.

Tweedie DJ, Jacob A. Surgery of the parotid gland: evolution of techniques, nomenclature and a revised classification system. Clin Otolaryngol. 2009;34(4):303–8. https://doi.org/10.1111/j.1749-4486.2009.01953.x.CrossRefPubMed

15.

Kato MG, Erkul E, Nguyen SA, et al. Extracapsular dissection vs superficial parotidectomy of benign parotid lesions surgical outcomes and cost-effectiveness analysis. JAMA Otolaryngol Head Neck Surg. 2017;143(11):1092–7. https://doi.org/10.1001/jamaoto.2017.1618.CrossRefPubMedPubMedCentral

16.

Hamilton BE, Salzman KL, Wiggins RH, et al. Earring lesions of the parotid tail. AJNR Am J Neuroradiol. 2003;24(9):1757–64.PubMed

17.

Witt RL, Rejto L. Pleomorphic adenoma: extracapsular dissection versus partial superficial parotidectomy with facial nerve dissection. Del Med J. 2009;81(3):119–25.PubMed

18.

Patel DK, Morton RP. Demographics of benign parotid tumours: Warthin’s tumour versus other benign salivary tumours. Acta Otolaryngol. 2016;136(1):83–6. https://doi.org/10.3109/00016489.2015.1081276.CrossRefPubMed

19.

Wang CW, Chu YH, Chiu DY, et al. The Warthin tumor score: a simple and reliable method to distinguish Warthin tumors from pleomorphic adenomas and carcinomas. AJR Am J Roentgenol. 2018;210(6):1330–7. https://doi.org/10.2214/AJR.17.18492.CrossRefPubMed

20.

Xu ZF, Yong F, Yu T, et al. Different histological subtypes of parotid gland tumors: CT findings and diagnostic strategy. World J Radiol. 2013;5(8):313–20. https://doi.org/10.4329/wjr.v5.i8.313.CrossRefPubMedPubMedCentral

21.

Jo HJ, Ahn HJ, Jung S, et al. Diagnostic difficulties in fine needle aspiration of benign salivary glandular lesions. Korean J Pathol. 2012;46(6):569–75. https://doi.org/10.4132/koreanjpathol.2012.46.6.569.CrossRefPubMedPubMedCentral

22.

Kechagias N, Ntomouchtsis A, Valeri R, et al. Fine-needle aspiration cytology of salivary gland tumours: a 10-year retrospective analysis. Oral Maxillofac Surg. 2012;16(1):35–40. https://doi.org/10.1007/s10006-011-0291-8.CrossRefPubMed

23.

Lipinski KA, Barber LJ, Davies MN, et al. Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer. 2016;2(1):49–63. https://doi.org/10.1016/j.trecan.2015.11.003.CrossRefPubMedPubMedCentral

24.

Woo SH, Choi DS, Kim JP, et al. Two-phase computed tomography study of Warthin tumor of parotid gland: differentiation from other parotid gland tumors and its pathologic explanation. J Comput Assist Tomogr. 2013;37(4):518–24. https://doi.org/10.1097/rct.0b013e31828aede8.CrossRefPubMed

25.

Dong Y, Lei GW, Wang SW, et al. Diagnostic value of CT perfusion imaging for parotid neoplasms. Dentomaxillofac Radiol. 2014;43(1):20130237. https://doi.org/10.1259/dmfr.20130237.CrossRefPubMed

26.

Al Ajmi E, Forghani B, Reinhold C, et al. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol. 2018;28(6):2604–11. https://doi.org/10.1007/s00330-017-5214-0.CrossRefPubMed

27.

Koo HJ, Sung YS, Shim WH, et al. Quantitative computed tomography features for predicting tumor recurrence in patients with surgically resected adenocarcinoma of the lung. PLoS One. 2017;12(1):e0167955. https://doi.org/10.1371/journal.pone.0167955.CrossRefPubMedPubMedCentral

28.

Zhai TT, Langendijk JA, van Dijk LV, et al. The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation. Oral Oncol. 2019;95:178–86. https://doi.org/10.1016/j.oraloncology.%202019.06.020.CrossRefPubMed

29.

Eilaghi A, Baig S, Zhang Y, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma – a quantitative analysis. BMC Med Imaging. 2017;17(1). https://doi.org/10.1186/s12880-017-0209-5.

30.

Simpson AL, Doussot A, Creasy JM, et al. Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol. 2017;24(9):2482–90. https://doi.org/10.1245/s10434-017-5896-1.CrossRefPubMedPubMedCentral

31.

Li Z, Mao Y, Huang W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017;17(1):42. https://doi.org/10.1186/s12880-017-0212-x.CrossRefPubMedPubMedCentral

32.

Pham TD, Watanabe Y, Higuchi M, et al. Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography. Sci Rep. 2017;7:43209. https://doi.org/10.1038/srep43209.CrossRefPubMedPubMedCentral

33.

Nardone V, Tini P, Nioche C, et al. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 2018;123(6):415–23. https://doi.org/10.1007/s11547-017-0850-7.CrossRefPubMed

34.

Wu H, Chen X, Yang X, et al. Early prediction of acute xerostomia during radiation therapy for head and neck cancer based on texture analysis of daily CT. Int J Radiat Oncol Biol Phys. 2018;102(4):1308–18. https://doi.org/10.1016/j.ijrobp.2018.04.059.CrossRefPubMed

Title: Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters
Authors: Dan Zhang
Xiaojiao Li
Liang Lv
Jiayi Yu
Chao Yang
Hua Xiong
Ruikun Liao
Bi Zhou
Xianlong Huang
Xiaoshuang Liu
Zhuoyue Tang
Publication date: 01-12-2020
Publisher: BioMed Central
Keyword: Biomarkers
Published in: BMC Medical Imaging / Issue 1/2020
Electronic ISSN: 1471-2342
DOI: https://doi.org/10.1186/s12880-020-00442-x

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Abstract

Background

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2020

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

A quantitative MRI index for assessing the severity of hippocampal sclerosis in temporal lobe epilepsy

Dual-energy spectral CT quantitative parameters for the differentiation of Glioma recurrence from treatment-related changes: a preliminary study

Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F-FDG PET/CT-derived parameters

Prospective evaluation of contrast-enhanced ultrasound of breast BI-RADS 3–5 lesions