Literature DB >> 22608062

Differentiation between malignant and benign solitary pulmonary nodules: use of volume first-pass perfusion and combined with routine computed tomography.

Fei Shan1, Zhiyong Zhang, Wei Xing, Jianguo Qiu, Shan Yang, Jian Wang, Yaping Jiang, Gang Chen.   

Abstract

PURPOSE: To evaluate the capability of first-pass volume perfusion computed tomography (PCT) for differentiation of solitary pulmonary nodules (SPNs) and to compare that of combination of PCT and routine CT with CT alone for the differentiation.
MATERIALS AND METHODS: Our institutional review board approved this study and informed consent was obtained. With nine excluded, 65 consecutive patients having a SPN with histopathologic proof or follow-up underwent a 30s PCT using the deconvolution model were evaluated. Kruskal-Wallis tests and receiver operating characteristics (ROC) analysis were underwent. Four radiologists assessed nodules independently and retrospectively. Diagnostic capability was compared for CT alone and PCT plus CT. ROC analysis, McNemar test, and weighted kappa statistics were performed.
RESULTS: Significant differences were found in parameters between malignant and benign nodules (p<0.0001 for blood flow, blood volume, and permeability surface area product), SPNs were more likely to be malignant by using threshold values of more than 55 ml/100 g/min, 2.5 ml/100 g, and 10 ml/100 g/min, respectively. PCT plus CT was significantly better in overall sensitivity (93%, p=0.004) and accuracy (94%, p=0.003) compared to CT alone, not specificity (96%). Area under the curve for ROC analyses of PCT plus CT was significantly larger than that of CT alone (p=0.018). Mean weighted kappa for PCT plus CT was 0.715, that for CT alone was 0.447.
CONCLUSION: Volume first-pass PCT can distinguish SPNs. Using PCT plus routine CT may be more sensitive and accurate for differentiating malignant from benign nodules than CT alone and allows more confidence and constancy.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22608062     DOI: 10.1016/j.ejrad.2012.04.003

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


  8 in total

1.  Intra-observer and inter-observer agreements for the measurement of dual-input whole tumor computed tomography perfusion in patients with lung cancer: Influences of the size and inner-air density of tumors.

Authors:  Qingle Wang; Zhiyong Zhang; Fei Shan; Yuxin Shi; Wei Xing; Liangrong Shi; Xingwei Zhang
Journal:  Thorac Cancer       Date:  2017-06-06       Impact factor: 3.500

2.  Clinical Potential of UTE-MRI for Assessing COVID-19: Patient- and Lesion-Based Comparative Analysis.

Authors:  Shuyi Yang; Yunfei Zhang; Jie Shen; Yongming Dai; Yun Ling; Hongzhou Lu; Rengyin Zhang; Xueting Ding; Huali Qi; Yuxin Shi; Zhiyong Zhang; Fei Shan
Journal:  J Magn Reson Imaging       Date:  2020-06-03       Impact factor: 4.813

3.  Diagnostic Performance of Perfusion Computed Tomography for Differentiating Lung Cancer from Benign Lesions: A Meta-Analysis.

Authors:  Cuiqing Huang; Jianye Liang; Xueping Lei; Xi Xu; Zeyu Xiao; Liangping Luo
Journal:  Med Sci Monit       Date:  2019-05-11

4.  Contrast timing optimization of a two-volume dynamic CT pulmonary perfusion technique.

Authors:  Yixiao Zhao; Logan Hubbard; Shant Malkasian; Pablo Abbona; Sabee Molloi
Journal:  Sci Rep       Date:  2022-05-17       Impact factor: 4.379

5.  A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

Authors:  Shuwen Wang; Leilei Zhou; Xiaoran Li; Jie Tang; Jing Wu; Xindao Yin; Yu-Chen Chen; Lingquan Lu
Journal:  Med Sci Monit       Date:  2022-07-29

6.  CT Perfusion in Patients with Lung Cancer: Squamous Cell Carcinoma and Adenocarcinoma Show a Different Blood Flow.

Authors:  Alessandro Bevilacqua; Giampaolo Gavelli; Serena Baiocco; Domenico Barone
Journal:  Biomed Res Int       Date:  2018-09-03       Impact factor: 3.411

7.  Dynamic pulmonary CT perfusion using first-pass analysis technique with only two volume scans: Validation in a swine model.

Authors:  Yixiao Zhao; Logan Hubbard; Shant Malkasian; Pablo Abbona; Sabee Molloi
Journal:  PLoS One       Date:  2020-02-12       Impact factor: 3.240

8.  Low-dose spectral CT perfusion imaging of lung cancer quantitative analysis in different pathological subtypes.

Authors:  Mai-Lin Chen; Yi-Yuan Wei; Xiao-Ting Li; Li-Ping Qi; Ying-Shi Sun
Journal:  Transl Cancer Res       Date:  2021-06       Impact factor: 1.241

  8 in total

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