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

ObjectivesTo explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers.MethodsPreoperative 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.ResultsMean 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.ConclusionCT 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.

[1]  P. Laurén,et al.  THE TWO HISTOLOGICAL MAIN TYPES OF GASTRIC CARCINOMA: DIFFUSE AND SO-CALLED INTESTINAL-TYPE CARCINOMA. AN ATTEMPT AT A HISTO-CLINICAL CLASSIFICATION. , 1965, Acta pathologica et microbiologica Scandinavica.

[2]  Y. Maehara,et al.  Neural invasion in gastric carcinoma. , 1995, Journal of clinical pathology.

[3]  Diagnostic reference levels in medical imaging: review and additional advice. , 2001, Annals of the ICRP.

[4]  P. Kim,et al.  Gastric cancer staging at multi-detector row CT gastrography: comparison of transverse and volumetric CT scanning. , 2005, Radiology.

[5]  Zhi-gang Yang,et al.  Gastric adenocarcinoma: can perfusion CT help to noninvasively evaluate tumor angiogenesis? , 2011, Abdominal Imaging.

[6]  H. Matsubara,et al.  Role of Perfusion CT in Assessing Tumor Blood Flow and Malignancy Level of Gastric Cancer , 2010, Digestive Surgery.

[7]  Jing Wu,et al.  Clinicopathological significance of E-cadherin, VEGF, and MMPs in gastric cancer , 2010, Tumor Biology.

[8]  E. Fishman,et al.  Hypervascular gastric masses: CT findings and clinical correlates. , 2010, AJR. American journal of roentgenology.

[9]  Xin-dao Yin,et al.  A preliminary study on correlations of triple-phase multi-slice CT scan with histological differentiation and intratumoral microvascular/lymphatic invasion in gastric cancer. , 2011, Chinese medical journal.

[10]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[11]  Rui-hua Xu,et al.  Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China , 2013, Journal of Translational Medicine.

[12]  Yong Li,et al.  Tumor chemosensitivity is correlated with expression of multidrug resistance associated factors in variously differentiated gastric carcinoma tissues. , 2012, Hepato-gastroenterology.

[13]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[14]  Jeong Hoon Lee,et al.  Pathologic Discordance of Differentiation Between Endoscopic Biopsy and Postoperative Specimen in Mucosal Gastric Adenocarcinomas , 2013, Annals of Surgical Oncology.

[15]  H. Honda,et al.  Extent of arterial tumor enhancement measured with preoperative MDCT gastrography is a prognostic factor in advanced gastric cancer after curative resection. , 2013, AJR. American journal of roentgenology.

[16]  V. Goh,et al.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. , 2013, Radiology.

[17]  Dina Muin,et al.  Texture-based classification of different gastric tumors at contrast-enhanced CT. , 2013, European journal of radiology.

[18]  X. Liang,et al.  Enhanced Intensity on Preoperative Double Contrast‐Enhanced Sonography as a Useful Indicator of Lymph Node Metastasis in Patients With Gastric Cancer , 2014, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[19]  Y. Doki,et al.  Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer , 2015, European Radiology.

[20]  Yingwei Xue,et al.  Clinicopathologic characteristics and prognostic value of various histological types in advanced gastric cancer. , 2014, International journal of clinical and experimental pathology.

[21]  Myung Ah Lee,et al.  Correlation of dynamic contrast‐enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: Preliminary results , 2015, Journal of magnetic resonance imaging : JMRI.

[22]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[23]  H. Honda,et al.  Differentiation of early gastric cancer with ulceration and resectable advanced gastric cancer using multiphasic dynamic multidetector CT , 2016, European Radiology.

[24]  Nicola Schieda,et al.  Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? , 2015, Radiology.

[25]  J. Yun,et al.  Vascular invasion as an independent predictor of poor prognosis in nonmetastatic gastric cancer after curative resection. , 2015, International journal of clinical and experimental pathology.

[26]  S. Demeter,et al.  Internet-based ICRP resource for healthcare providers on the risks and benefits of medical imaging that uses ionising radiation , 2016, Annals of the ICRP.

[27]  Balaji Ganeshan,et al.  CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer , 2016, Acta radiologica.

[28]  X. Zhihui,et al.  CT perfusion imaging of the stomach: a quantitative analysis according to different degrees of adenocarcinoma cell differentiation. , 2016, Clinical imaging.

[29]  Yanqi Huang,et al.  Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer? , 2017, European Radiology.

[30]  Sang Joon Park,et al.  Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. , 2016, European journal of radiology.

[31]  Francesco Giganti,et al.  Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker , 2017, European Radiology.

[32]  Song Liu,et al.  Assessment of histological differentiation in gastric cancers using whole‐volume histogram analysis of apparent diffusion coefficient maps , 2017, Journal of magnetic resonance imaging : JMRI.

[33]  Kui-Sheng Chen,et al.  Spectral computed tomography in advanced gastric cancer: Can iodine concentration non-invasively assess angiogenesis? , 2017, World journal of gastroenterology.

[34]  W J Macdonald,et al.  GASTRIC CARCINOMA. , 1912, Canadian Medical Association journal.