TU-AB-BRA-04: Quantitative Radiomics: Sensitivity of PET Textural Features to Image Acquisition and Reconstruction Parameters Implies the Need for Standards

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, SR; Sandison, GA; Kinahan, Paul

doi:10.1118/1.4925509

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Factors that may introduce interstudy or interobserver variability in texture analysis are lesion morphology, imaging acquisition parameters, tumor segmentation techniques, methods of postprocessing (i.e., type of filter, number of quantization levels, and image bit depth), and the number of texture features and categories [13,18,[69][70][71]. These sources of variation must be fully addressed before texture analysis can transition from being a research tool to being a clinical tool.…”

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

“…Most texture parameters are prone to testretest variability even under identical conditions [18,70,71,[76][77][78][79][80][81]. According to Galavis et al [77], who tested reproducibility of different texture parameters on five different PET reconstruction modes, 40 of 50 first-order histogram features showed more than 30% variation in the different PET modes.…”

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

“…According to Galavis et al [77], who tested reproducibility of different texture parameters on five different PET reconstruction modes, 40 of 50 first-order histogram features showed more than 30% variation in the different PET modes. Higher-order texture features vary even more and may change by as much as 100% on repeat measurements [71,76]. Some of the more stable texture features are first-order kurtosis and entropy, which may change by less than 5% under different acquisition conditions [70].…”

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

See 2 more Smart Citations

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

129

View full text Add to dashboard Cite

Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

show abstract

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

Section: Challenges and Directions For Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

129

View full text Add to dashboard Cite

show abstract

Multi-Class SVM Prediction Model for Lung Cancer Diagnosis

Lakshmi

Sivakumar

Ramani³

2022

International Conference on Artificial Intelligence for Smart Community

View full text Add to dashboard Cite

The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer

et al. 2017

View full text Add to dashboard Cite

BackgroundMeasures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18F-FDG PET/CT images.Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC).Results40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85–0.92) compared with FLAB (median ICC 0.83; IQR 0.77–0.86) and FH (median ICC 0.77; IQR 0.7–0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs.ConclusionsCompared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of 18F-FDG PET in NSCLC.

show abstract

TU-AB-BRA-04: Quantitative Radiomics: Sensitivity of PET Textural Features to Image Acquisition and Reconstruction Parameters Implies the Need for Standards

Cited by 3 publications

References 0 publications

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Multi-Class SVM Prediction Model for Lung Cancer Diagnosis

The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer

Contact Info

Product

Resources

About