Literature DB >> 23701089

Comparison of quantitatively analyzed dynamic area-detector CT using various mathematic methods with FDG PET/CT in management of solitary pulmonary nodules.

Yoshiharu Ohno1, Mizuho Nishio, Hisanobu Koyama, Yasuko Fujisawa, Takeshi Yoshikawa, Sumiaki Matsumoto, Kazuro Sugimura.   

Abstract

OBJECTIVE: The objective of our study was to prospectively compare the capability of dynamic area-detector CT analyzed with different mathematic methods and PET/CT in the management of pulmonary nodules. SUBJECTS AND METHODS: Fifty-two consecutive patients with 96 pulmonary nodules underwent dynamic area-detector CT, PET/CT, and microbacterial or pathologic examinations. All nodules were classified into the following groups: malignant nodules (n = 57), benign nodules with low biologic activity (n = 15), and benign nodules with high biologic activity (n = 24). On dynamic area-detector CT, the total, pulmonary arterial, and systemic arterial perfusions were calculated using the dual-input maximum slope method; perfusion was calculated using the single-input maximum slope method; and extraction fraction and blood volume (BV) were calculated using the Patlak plot method. All indexes were statistically compared among the three nodule groups. Then, receiver operating characteristic analyses were used to compare the diagnostic capabilities of the maximum standardized uptake value (SUVmax) and each perfusion parameter having a significant difference between malignant and benign nodules. Finally, the diagnostic performances of the indexes were compared by means of the McNemar test.
RESULTS: No adverse effects were observed in this study. All indexes except extraction fraction and BV, both of which were calculated using the Patlak plot method, showed significant differences among the three groups (p < 0.05). Areas under the curve of total perfusion calculated using the dual-input method, pulmonary arterial perfusion calculated using the dual-input method, and perfusion calculated using the single-input method were significantly larger than that of SUVmax (p < 0.05). The accuracy of total perfusion (83.3%) was significantly greater than the accuracy of the other indexes: pulmonary arterial perfusion (72.9%, p < 0.05), systemic arterial perfusion calculated using the dual-input method (69.8%, p < 0.05), perfusion (66.7%, p < 0.05), and SUVmax (60.4%, p < 0.05).
CONCLUSION: Dynamic area-detector CT analyzed using the dual-input maximum slope method has better potential for the diagnosis of pulmonary nodules than dynamic area-detector CT analyzed using other methods and than PET/CT.

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Year:  2013        PMID: 23701089     DOI: 10.2214/AJR.12.9197

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  10 in total

1.  An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management.

Authors:  Christopher G Slatore; Nanda Horeweg; James R Jett; David E Midthun; Charles A Powell; Renda Soylemez Wiener; Juan P Wisnivesky; Michael K Gould
Journal:  Am J Respir Crit Care Med       Date:  2015-08-15       Impact factor: 21.405

2.  Arterial input function placement effect on computed tomography lung perfusion maps.

Authors:  Laura Jimenez-Juan; Hatem Mehrez; Chris Dey; Shabnam Homampour; Anastasia Oikonomou; Fatima Ursani; Narinder Paul
Journal:  Quant Imaging Med Surg       Date:  2016-02

3.  A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging.

Authors:  Kazuhiro Murayama; Ewoud J Smit; Mathias Prokop; Yoshihiro Ikeda; Kenji Fujii; Ichiro Nakahara; Satomu Hanamatsu; Kazuhiro Katada; Yoshiharu Ohno; Hiroshi Toyama
Journal:  Neuroradiology       Date:  2022-07-18       Impact factor: 2.995

Review 4.  Contrast-enhanced CT- and MRI-based perfusion assessment for pulmonary diseases: basics and clinical applications.

Authors:  Yoshiharu Ohno; Hisanobu Koyama; Ho Yun Lee; Sachiko Miura; Takeshi Yoshikawa; Kazuro Sugimura
Journal:  Diagn Interv Radiol       Date:  2016 Sep-Oct       Impact factor: 2.630

Review 5.  Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis.

Authors:  Stephen A Deppen; Jeffrey D Blume; Clark D Kensinger; Ashley M Morgan; Melinda C Aldrich; Pierre P Massion; Ronald C Walker; Melissa L McPheeters; Joe B Putnam; Eric L Grogan
Journal:  JAMA       Date:  2014-09-24       Impact factor: 56.272

Review 6.  Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings.

Authors:  Yoshiharu Ohno; Joon Beom Seo; Grace Parraga; Kyung Soo Lee; Warren B Gefter; Sean B Fain; Mark L Schiebler; Hiroto Hatabu
Journal:  Radiology       Date:  2021-04-06       Impact factor: 29.146

7.  Surgical resection for a second primary lung cancer originating close to the initial surgical margin for lung squamous cell carcinoma.

Authors:  Seijiro Sato; Terumoto Koike; Takehisa Hashimoto; Masanori Tsuchida
Journal:  Case Rep Surg       Date:  2015-03-10

8.  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

9.  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

10.  Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy.

Authors:  Shinichiro Seki; Yasuko Fujisawa; Masao Yui; Yuji Kishida; Hisanobu Koyama; Shigeharu Ohyu; Naoki Sugihara; Takeshi Yoshikawa; Yoshiharu Ohno
Journal:  Magn Reson Med Sci       Date:  2019-03-18       Impact factor: 2.471

  10 in total

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