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The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, Aerts HJ, Gillies RJ, Lambin P - Sci Rep (2015)

Bottom Line: As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins.Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods.Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands.

ABSTRACT
FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R(D), dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R(B), maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R(B) was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

No MeSH data available.


Related in: MedlinePlus

Graphical representation of pairwise Spearman rank correlations between patient rankings according to feature value for different B(ρBB), different D (ρDD) and between different B and D (ρBD), based on pre-treatment imaging.Blue lines extend from the minimum to the maximum observed pairwise ρ. Median ρ values are represented by the red markers. The gray vertical line represents ρ = 0.9. For abbreviations, see the caption of Fig. 3
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f4: Graphical representation of pairwise Spearman rank correlations between patient rankings according to feature value for different B(ρBB), different D (ρDD) and between different B and D (ρBD), based on pre-treatment imaging.Blue lines extend from the minimum to the maximum observed pairwise ρ. Median ρ values are represented by the red markers. The gray vertical line represents ρ = 0.9. For abbreviations, see the caption of Fig. 3

Mentions: For each feature we determined the patient ranking according to feature value, using RB and RD for different resampling values B and D, respectively. We then calculated pairwise ρ of patient rankings between different B (ρBB), different D (ρDD) and between different B and D (ρBD). For each feature, we reported the range and median of all pairwise ρ (Fig. 4).


The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, Aerts HJ, Gillies RJ, Lambin P - Sci Rep (2015)

Graphical representation of pairwise Spearman rank correlations between patient rankings according to feature value for different B(ρBB), different D (ρDD) and between different B and D (ρBD), based on pre-treatment imaging.Blue lines extend from the minimum to the maximum observed pairwise ρ. Median ρ values are represented by the red markers. The gray vertical line represents ρ = 0.9. For abbreviations, see the caption of Fig. 3
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525145&req=5

f4: Graphical representation of pairwise Spearman rank correlations between patient rankings according to feature value for different B(ρBB), different D (ρDD) and between different B and D (ρBD), based on pre-treatment imaging.Blue lines extend from the minimum to the maximum observed pairwise ρ. Median ρ values are represented by the red markers. The gray vertical line represents ρ = 0.9. For abbreviations, see the caption of Fig. 3
Mentions: For each feature we determined the patient ranking according to feature value, using RB and RD for different resampling values B and D, respectively. We then calculated pairwise ρ of patient rankings between different B (ρBB), different D (ρDD) and between different B and D (ρBD). For each feature, we reported the range and median of all pairwise ρ (Fig. 4).

Bottom Line: As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins.Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods.Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands.

ABSTRACT
FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R(D), dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R(B), maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R(B) was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

No MeSH data available.


Related in: MedlinePlus