Literature DB >> 29571809

Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules.

Gianluca Milanese1, Matthias Eberhard2, Katharina Martini3, Ilaria Vittoria De Martini4, Thomas Frauenfelder5.   

Abstract

OBJECTIVE: To evaluate whether vessel-suppressed computed tomography (VSCT) can be reliably used for semi-automated volumetric measurements of solid pulmonary nodules, as compared to standard CT (SCT)
MATERIAL AND METHODS: Ninety-three SCT were elaborated by dedicated software (ClearRead CT, Riverain Technologies, Miamisburg, OH, USA), that allows subtracting vessels from lung parenchyma. Semi-automated volumetric measurements of 65 solid nodules were compared between SCT and VSCT. The measurements were repeated by two readers. For each solid nodule, volume measured on SCT by Reader 1 and Reader 2 was averaged and the average volume between readers acted as standard of reference value. Concordance between measurements was assessed using Lin's Concordance Correlation Coefficient (CCC). Limits of agreement (LoA) between readers and CT datasets were evaluated.
RESULTS: Standard of reference nodule volume ranged from 13 to 366 mm3. The mean overestimation between readers was 3 mm3 and 2.9 mm3 on SCT and VSCT, respectively. Semi-automated volumetric measurements on VSCT showed substantial agreement with the standard of reference (Lin's CCC = 0.990 for Reader 1; 0.985 for Reader 2). The upper and lower LoA between readers' measurements were (16.3, -22.4 mm3) and (15.5, -21.4 mm3) for SCT and VSCT, respectively.
CONCLUSIONS: VSCT datasets are feasible for the measurements of solid nodules, showing an almost perfect concordance between readers and with measurements on SCT.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computer-Assisted; Diagnosis; Multidetector computed tomography; Solid pulmonary nodule

Mesh:

Year:  2018        PMID: 29571809     DOI: 10.1016/j.ejrad.2018.02.020

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


  5 in total

1.  Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT.

Authors:  Anne-Kathrin Wagner; Arno Hapich; Marios Nikos Psychogios; Ulf Teichgräber; Ansgar Malich; Ismini Papageorgiou
Journal:  J Med Syst       Date:  2019-01-31       Impact factor: 4.460

2.  Lung cancer screening: tell me more about post-test risk.

Authors:  Mario Silva; Gianluca Milanese; Ugo Pastorino; Nicola Sverzellati
Journal:  J Thorac Dis       Date:  2019-09       Impact factor: 2.895

3.  Volumetric Measurements in Lung Cancer Screening Reduces Unnecessary Low-Dose Computed Tomography Scans: Results from a Single-Center Prospective Trial on 4119 Subjects.

Authors:  Gianluca Milanese; Federica Sabia; Roberta Eufrasia Ledda; Stefano Sestini; Alfonso Vittorio Marchianò; Nicola Sverzellati; Ugo Pastorino
Journal:  Diagnostics (Basel)       Date:  2022-01-18

4.  Lung Nodules in Melanoma Patients: Morphologic Criteria to Differentiate Non-Metastatic and Metastatic Lesions.

Authors:  Simone Alexandra Stadelmann; Christian Blüthgen; Gianluca Milanese; Thi Dan Linh Nguyen-Kim; Julia-Tatjana Maul; Reinhard Dummer; Thomas Frauenfelder; Matthias Eberhard
Journal:  Diagnostics (Basel)       Date:  2021-05-07

Review 5.  The application of artificial intelligence in lung cancer: a narrative review.

Authors:  Huixian Zhang; Die Meng; Siqi Cai; Haoyue Guo; Peixin Chen; Zixuan Zheng; Jun Zhu; Wencheng Zhao; Hao Wang; Sha Zhao; Jia Yu; Yayi He
Journal:  Transl Cancer Res       Date:  2021-05       Impact factor: 1.241

  5 in total

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