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Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics.
- Source :
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EJNMMI research [EJNMMI Res] 2018 Dec 29; Vol. 8 (1), pp. 114. Date of Electronic Publication: 2018 Dec 29. - Publication Year :
- 2018
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Abstract
- Background: The aim of this study is to determine if the choice of the <superscript>18</superscript> F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs).<br />Procedures: Forty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSF <subscript>WholeBody</subscript> ), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEM <subscript>breast</subscript> /PSF <subscript>breast</subscript> ). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics.<br />Results: PSF <subscript>WholeBody</subscript> showed lower numbers of voxels within VOIs than OSEM <subscript>breast</subscript> and PSF <subscript>breast</subscript> with median (interquartile range) equal to 130 (43-271), 316 (167-1042), 367 (107-1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSF <subscript>WholeBody</subscript> , OSEM <subscript>breast</subscript> and PSF <subscript>breast</subscript> , respectively. On matched comparisons, PSF <subscript>breast</subscript> reconstruction presented better abilities than PSF <subscript>wholeBody</subscript> and OSEM <subscript>breast</subscript> for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSF <subscript>wholeBody</subscript> and 73.8% for OSEM <subscript>breast</subscript> . PSF <subscript>breast</subscript> accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUV <subscript>max</subscript> on PSF <subscript>wholeBody</subscript> images (ρ = - 0.526, p = 0.005), whereas it was not on OSEM <subscript>breast</subscript> (ρ = - 0.183, p = 0.244) and PSF <subscript>breast</subscript> (ρ = - 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSF <subscript>breast</subscript> images as compared to OSEM <subscript>breast</subscript> , especially in small lesions (MTV < 3 ml).<br />Conclusions: High-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.
Details
- Language :
- English
- ISSN :
- 2191-219X
- Volume :
- 8
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- EJNMMI research
- Publication Type :
- Academic Journal
- Accession number :
- 30594961
- Full Text :
- https://doi.org/10.1186/s13550-018-0466-5