Association between Histological Types and Enhancement of Dynamic CT for Primary Lung Cancer.

The aim of this study was to explore enhancement patterns of different types of primary lung cancers on 2-phase dynamic computed tomography (CT). This study included 217 primary lung cancer patients (141 adenocarcinomas [ADs], 48 squamous cell carcinomas [SCCs], 20 small cell lung carcinomas [SCLCs], and 8 others) who were examined using a 2-phase dynamic scan. Regions of interest were identified and mean enhancement values were calculated. After excluding the 20 SCLCs because these lesions had different clinical stages from the other cancer types, the mean attenuation values and subtractions between phases were compared between types of non-small cell lung carcinomas (NSCLCs) using the Kruskal-Wallis test. Late phase attenuation and attenuation of the late minus unenhanced phase (LMU) of SCCs were significantly higher than those of ADs (p<0.05). To differentiate SCC and AD in the late phase, a threshold of 80.21 Hounsfield units (HU) gave 52.9% accuracy. In LMU, a threshold of 52.16 HU gave 59.3% accuracy. Dynamic lung CT has the potential to aid in differentiating among NSCLC types.

[1]  L. Schwartz,et al.  Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. , 2019, Academic radiology.

[2]  Paulo Mazzoncini de Azevedo Marques,et al.  Radiomics-based features for pattern recognition of lung cancer histopathology and metastases , 2018, Comput. Methods Programs Biomed..

[3]  Xinzhong Zhu,et al.  Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer , 2018, European Radiology.

[4]  S. Khanduri,et al.  Rationale of Using Dynamic Imaging for Characterization of Suspicious Lung Masses into Benign or Malignant on Contrast Enhanced Multi Detector Computed Tomography , 2017, Journal of clinical imaging science.

[5]  Katsuyuki Nakanishi,et al.  Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size? , 2014, Acta radiologica.

[6]  L. Tanoue,et al.  Lung cancer: epidemiology, etiology, and prevention. , 2011, Clinics in chest medicine.

[7]  S. Iwano,et al.  Correlation between dual-phase dynamic multi-detector CT findings and fibrosis within lung adenocarcinoma tumors. , 2011, European journal of radiology.

[8]  E. Leen,et al.  Multimodality imaging of neoplastic and nonneoplastic solid lesions of the pancreas. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[9]  M. Chung,et al.  Solitary pulmonary nodule: characterization with combined wash-in and washout features at dynamic multi-detector row CT. , 2005, Radiology.

[10]  C. Yi,et al.  Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density. , 2004, Radiology.

[11]  Kyoung-Sik Cho,et al.  Differentiation of subtypes of renal cell carcinoma on helical CT scans. , 2002, AJR. American journal of roentgenology.

[12]  X-S Xiao,et al.  Dynamic CT of solitary pulmonary nodules: comparison of contrast medium distribution characteristic of malignant and benign lesions , 2013, Clinical and Translational Oncology.

[13]  小澤 良之.  The frequency of insufficient contrast enhancement of the pulmonary artery in routine contrast-enhanced chest CT and its improvement with an increased injection rate : a prospective study , 2010 .

[14]  A. Lim,et al.  Imaging of liver cancer. , 2009, World journal of gastroenterology.

[15]  N. Müller,et al.  Lung nodule enhancement at CT: multicenter study. , 2000, Radiology.