Could new reconstruction CT techniques challenge MRI for the detection of brain metastases in the context of initial lung cancer ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-02

AUTHORS

Domitille Millon, David Byl, Philippe Collard, Samantha E. Cambier, Aline G. Van Maanen, Alain Vlassenbroek, Emmanuel E. Coche

ABSTRACT

OBJECTIVES: To evaluate the diagnostic performance of brain CT images reconstructed with a model-based iterative algorithm performed at usual and reduced dose. METHODS: 115 patients with histologically proven lung cancer were prospectively included over 15 months. Patients underwent two CT acquisitions at the initial staging, performed on a 256-slice MDCT, at standard (CTDIvol: 41.4 mGy) and half dose (CTDIvol: 20.7 mGy). Both image datasets were reconstructed with filtered back projection (FBP) and iterative model-based reconstruction (IMR) algorithms. Brain MRI was considered as the reference. Two blinded independent readers analysed the images. RESULTS: Ninety-three patients underwent all examinations. At the standard dose, eight patients presented 17 and 15 lesions on IMR and FBP CT images, respectively. At half-dose, seven patients presented 15 and 13 lesions on IMR and FBP CT images, respectively. The test could not highlight any significant difference between the standard dose IMR and the half-dose FBP techniques (p-value = 0.12). MRI showed 46 metastases on 11 patients. Specificity, negative and positive predictive values were calculated (98.9-100 %, 93.6-94.6 %, 75-100 %, respectively, for all CT techniques). CONCLUSION: No significant difference could be demonstrated between the two CT reconstruction techniques. KEY POINTS: • No significant difference between IMR100 and FBP50 was shown. • Compared to FBP, IMR increased the image quality without diagnostic impairment. • A 50 % dose reduction combined with IMR reconstructions could be achieved. • Brain MRI remains the best tool in lung cancer staging. More... »

PAGES

770-779

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-017-5021-7

DOI

http://dx.doi.org/10.1007/s00330-017-5021-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091396627

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28856413


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