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01.12.2017 | Short communication | Ausgabe 1/2017 Open Access

EJNMMI Research 1/2017

Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs)

Zeitschrift:
EJNMMI Research > Ausgabe 1/2017
Autoren:
Charline Lasnon, Blandine Enilorac, Hosni Popotte, Nicolas Aide
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13550-017-0279-y) contains supplementary material, which is available to authorized users.

Abstract

Background

The clinical validation of the EARL harmonization program for standardised uptake value (SUV) metrics is well documented; however, its potential for defining metabolic active tumour volume (MATV) has not yet been investigated. We aimed to compare delineation of MATV on images reconstructed using conventional ordered subset expectation maximisation (OSEM) with those reconstructed using point spread function modelling (PSF-reconstructed images), and either optimised for diagnostic potential (PSF) or filtered to meet the EANM/EARL harmonising standards (PSF7).

Methods

Images from 18 stage IIIA-IIIB lung cancer patients were reconstructed using all the three methods. MATVs were then delineated using both a 40% isocontour and a gradient-based method. MATVs were compared by means of Bland–Altman analyses, and Dice coefficients and concordance indices based on the unions and intersections between each pair of reconstructions (PSF vs OSEM, PSF7 vs PSF and PSF7 vs OSEM).

Results

Using the 40% isocontour method and taking the MATVs delineated on OSEM images as a reference standard, the use of PSF7 images led to significantly higher Dice coefficients (median value = 0.96 vs 0.77; P < 0.0001) and concordance indices (median value = 0.92 vs 0.64; P < 0.0001) than those obtained using PSF images.
The gradient-based methodology was less sensitive to reconstruction variability than the 40% isocontour method; Dice coefficients and concordance indices were superior to 0.8 for both PSF reconstruction methods. However, the use of PSF7 images led to narrower interquartile ranges and significantly higher Dice coefficients (median value = 0.96 vs 0.94; P = 0.01) and concordance indices (median value = 0.89 vs 0.85; P = 0.003) than those obtained with PSF images.

Conclusion

This study demonstrates that automatic contouring of lung tumours on EARL-compliant PSF images using the widely adopted automatic isocontour methodology is an accurate means of overcoming reconstruction variability in MATV delineation. Although gradient-based methodology appears to be less sensitive to reconstruction variability, the use of EARL-compliant PSF images significantly improved the Dice coefficients and concordance indices, demonstrating the importance of harmonised-images, even when more advanced contouring algorithms are used.
Zusatzmaterial
Additional file 1: Figure S1. Inter-observer concordance for volume delineation. Relationships between MATVs extracted from OSEM reconstructions and PSF or PSF7 reconstructions for observers were compared using the Lin concordance coefficient (ρc) for the 40% isocontour (a) and gradient-based (b) methods. (TIFF 8917 kb)
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Additional file 2: Figure S2. Impact of the EARL harmonization strategy on MATVs defined by the isocontour and gradient-based delineation methods (observer 2). MATVs are shown as Tukey boxplots (lines displaying the median, 25th and 75th percentiles; cross represents the mean values). Legends for p values: ***<0.001; **<0.01; *<0.05. ns, not significant. (TIFF 17693 kb)
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Additional file 3: Figure S3. Impact of the EARL harmonization strategy on Dice and concordance indices between MATVs extracted from OSEM images and PSF images (observer 2). The PSF7 images were filtered to meet EARL requirements while PSF images were optimised for diagnostic potential. Data are shown as Tukey boxplots (lines displaying the median, 25th, and 75th percentiles; crosses represents the mean values). Dice coefficients and concordance indices are shown for both the isocontour method (a and b) and gradient-based method (c and d). ns, not significant. (TIFF 17434 kb)
13550_2017_279_MOESM3_ESM.tiff
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