Literature DB >> 19246170

Thin-section CT of the mediastinum in preoperative N-staging of non-small cell lung cancer: comparison with FDG PET.

Atsushi Nambu1, Satoshi Kato, Utaroh Motosugi, Tsutomu Araki, Hideto Okuwaki, Keiichi Nishikawa, Akitoshi Saito, Keiko Matsumoto, Tomoaki Ichikawa.   

Abstract

PURPOSE: To compare diagnostic capability of preoperative N-staging of lung cancer between thin-section CT of the mediastinum and FDG PET, and 5mm slice thickness CT.
MATERIALS AND METHODS: The subjects were 34 patients with lung carcinoma who were examined by both CT and PET, and subsequently underwent surgery between May 2005 and January 2007. CT was carried out with a 16 detector row helical CT scanner. The raw data were reconstructed into 5 mm slice thickness and 1mm slice thickness (thin-section CT). A total of 251 lymph node stations were retrospectively assessed for the presence of lymph node metastasis with thin-section CT, 5 mm CT and PET. In the interpretations of thin-section CT and 5 mm CT, we employed multi-criteria as follows: nodular calcification and intranodal fat as benign criteria, and short-axis diameter more than 10 mm (size criterion), focal low density other than fat, surrounding fat infiltration and convex margin in hilar lymph nodes, as malignant criteria. On PET, maximum standardized uptake value (SUVmax) of 2.5 or more was used as the criterion of malignancy. Sensitivity and specificity were compared between these examinations using McNemar test.
RESULTS: Sensitivities and specificities of thin-section CT, 5 mm CT and PET were 25%, 25%, 25%, and 97%, 94%, 98%, respectively. The statistical analysis revealed that the specificity of 5 mm CT was significantly lower than those of thin-section CT (p=0.039) and PET (p=0.006), while no difference was present between thin-section CT and PET.
CONCLUSION: Thin-section CT of the mediastinum using multiple criteria was comparable to PET in preoperative N-staging of lung cancer. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19246170     DOI: 10.1016/j.ejrad.2009.01.021

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  6 in total

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6.  Machine Learning for the Prediction of Lymph Nodes Micrometastasis in Patients with Non-Small Cell Lung Cancer: A Comparative Analysis of Two Practical Prediction Models for Gross Target Volume Delineation.

Authors:  Shuli Hu; Man Luo; Yaling Li
Journal:  Cancer Manag Res       Date:  2021-06-17       Impact factor: 3.989

  6 in total

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