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22.06.2017 | Gastrointestinal

Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

verfasst von: Shunli Liu, Song Liu, Changfeng Ji, Huanhuan Zheng, Xia Pan, Yujuan Zhang, Wenxian Guan, Ling Chen, Yue Guan, Weifeng Li, Jian He, Yun Ge, Zhengyang Zhou

Erschienen in: European Radiology | Ausgabe 12/2017

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Abstract

Objectives

To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers.

Methods

Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed.

Results

Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, −0.231 ~ −0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = −0.265, −0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion.

Conclusion

CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers.

Key Points

• CT texture analysis is noninvasive and effective for gastric cancer.

• Portal venous CT images correlated significantly with differentiation degree and Lauren classification.

• Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion.

Nur mit Berechtigung zugänglich

Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61:69–90CrossRefPubMed

Zu H, Wang H, Li C, Xue Y (2014) Clinicopathologic characteristics and prognostic value of various histological types in advanced gastric cancer. Int J Clin Exp Pathol 7:5692–5700PubMedPubMedCentral

Qiu MZ, Cai MY, Zhang DS et al (2013) Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China. J Transl Med 11:58CrossRefPubMedPubMedCentral

Li P, Ling YH, Zhu CM et al (2015) Vascular invasion as an independent predictor of poor prognosis in nonmetastatic gastric cancer after curative resection. Int J Clin Exp Pathol 8:3910–3918PubMedPubMedCentral

Mori M, Adachi Y, Kamakura T, Ikeda Y, Maehara Y, Sugimachi K (1995) Neural invasion in gastric carcinoma. J Clin Pathol 48:137–142CrossRefPubMedPubMedCentral

Lee IS, Park YS, Lee JH et al (2013) Pathologic discordance of differentiation between endoscopic biopsy and postoperative specimen in mucosal gastric adenocarcinomas. Ann Surg Oncol 20:4231–4237CrossRefPubMed

Saito T, Kurokawa Y, Takiguchi S et al (2015) Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer. Eur Radiol 25:368–374CrossRefPubMed

Komori M, Asayama Y, Fujita N et al (2013) Extent of arterial tumor enhancement measured with preoperative MDCT gastrography is a prognostic factor in advanced gastric cancer after curative resection. AJR Am J Roentgenol 201:W253–W261CrossRefPubMed

Tsurumaru D, Miyasaka M, Nishimuta Y et al (2016) Differentiation of early gastric cancer with ulceration and resectable advanced gastric cancer using multiphasic dynamic multidetector CT. Eur Radiol 26:1330–1337CrossRefPubMed

10.

Ma Z, Liang C, Huang Y et al (2016) Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer? Eur Radiol. 10.1007/s00330-016-4695-6

11.

Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: Histopathologic correlates for texture parameters at CT. Radiology 266:326–336CrossRefPubMed

12.

Ba-Ssalamah A, Muin D, Schernthaner R et al (2013) Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol 82:e537–e543CrossRefPubMed

13.

Hodgdon T, McInnes MDF, Schieda N, Flood TA, Lamb L, Thornhill RE (2015) Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology 276:787–796CrossRefPubMed

14.

Andersen MB, Harders SW, Ganeshan B, Thygesen J, Madsen HHT, Rasmussen F (2016) CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer. Acta Radiologica 57:669–676CrossRefPubMed

15.

Giganti F, Antunes S, Salerno A et al (2016) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. 10.1007/s00330-016-4540-y

16.

Lubner MG, Stabo N, Lubner SJ et al (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40:2331–2337CrossRefPubMed

17.

Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184CrossRefPubMed

18.

American Association of Physicists in Medicine (2008) The measurement, reporting, and management of radiation dose in CT. AAPM, Task Group 23. Available via http://www.aapm.org. Accessed 2008

19.

American College of Radiology (2016) ACR-AAPM Practice Parameter for Diagnostic Reference Levels and Achievable Doses in Medical X-Ray Imaging. ACR. Available via https://www.acr.org. Accessed 22 June 2016

20.

Committee ICRP (2001) Diagnostic reference levels in medical imaging: review and additional advice. Ann ICRP 31:33–52

21.

Kim HJ, Kim AY, Oh ST et al (2005) Gastric cancer staging at multi-detector row CT gastrography: comparison of transverse and volumetric CT scanning. Radiology 236:879–885CrossRefPubMed

22.

Lauwers GYFS, Carneiro F et al (2010) Gastric carcinoma. In: Bosman FT, Carneiro F, Hruban RH, Theise ND (eds) WHO classification of tumors of the digestive system:Lyon. IARC Press, France, pp 225–227

23.

Satoh A, Shuto K, Okazumi S et al (2010) Role of perfusion CT in assessing tumor blood flow and malignancy level of gastric cancer. Dig Surg 27:253–260CrossRefPubMed

24.

Yao J, Yang ZG, Chen HJ, Chen TW, Huang J (2011) Gastric adenocarcinoma: can perfusion CT help to noninvasively evaluate tumor angiogenesis? Abdom Imaging 36:15–21CrossRefPubMed

25.

Chen XH, Ren K, Liang P, Chai YR, Chen KS, Gao JB (2017) Spectral computed tomography in advanced gastric cancer: Can iodine concentration non-invasively assess angiogenesis? World J Gastroenterol 23:1666–1675CrossRefPubMedPubMedCentral

26.

Zongqiong S, Xiaohong L, Wei C et al (2016) CT perfusion imaging of the stomach: a quantitative analysis according to different degrees of adenocarcinoma cell differentiation. Clin Imaging 40:558–562CrossRefPubMed

27.

Li SY, Huang PT, Xu HS et al (2014) Enhanced intensity on preoperative double contrast-enhanced sonography as a useful indicator of lymph node metastasis in patients with gastric cancer. J Ultrasound Med 33:1773–1781CrossRefPubMed

28.

Li Y, Tan BB, Zhao Q et al (2013) Tumor chemosensitivity is correlated with expression of multidrug resistance associated factors in variously differentiated gastric carcinoma tissues. Hepatogastroenterology 60:213–216PubMed

29.

Zhou Y, Li G, Wu J et al (2010) Clinicopathological significance of E-cadherin, VEGF, and MMPs in gastric cancer. Tumour Biol 31:549–558CrossRefPubMed

30.

Lauren P (1965) The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand 64:31–49CrossRefPubMed

31.

Yin XD, Huang WB, Lu CY, Zhang L, Wang LW, Xie GH (2011) A preliminary study on correlations of triple-phase multi-slice CT scan with histological differentiation and intratumoral microvascular/lymphatic invasion in gastric cancer. Chin Med J (Engl) 124:347–351

32.

Yeo DM, Oh SN, Jung CK et al (2015) Correlation of dynamic contrast-enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: Preliminary results. J Magn Reson Imaging 41:474–480CrossRefPubMed

33.

Zhang Y, Chen J, Liu S et al (2016) Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps. J Magn Reson Imaging. 10.1002/jmri.25360

34.

Ahn SJ, Kim JH, Park SJ, Han JK (2016) Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol 85:1867–1874CrossRefPubMed

35.

Johnson PT, Horton KM, Fishman EK (2010) Hypervascular gastric masses: CT findings and clinical correlates. AJR Am J Roentgenol 195:W415–W420CrossRefPubMed

36.

Demeter S, Applegate KE, Perez M (2016) Internet-based ICRP resource for healthcare providers on the risks and benefits of medical imaging that uses ionising radiation. Ann ICRP 45:148–155CrossRefPubMed

Titel: Application of CT texture analysis in predicting histopathological characteristics of gastric cancers
verfasst von: Shunli Liu
Song Liu
Changfeng Ji
Huanhuan Zheng
Xia Pan
Yujuan Zhang
Wenxian Guan
Ling Chen
Yue Guan
Weifeng Li
Jian He
Yun Ge
Zhengyang Zhou
Publikationsdatum: 22.06.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 12/2017
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-4881-1

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Application of CT texture analysis in predicting histopathological characteristics of gastric cancers