ArticleEJNMMI research2017
The effects of segmentation algorithms on the measurement of
Article in EJNMMI research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
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- Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.European journal of nuclear medicine and molecular imaging · 2023Article
- Classification of solid pulmonary nodules using a machine-learning nomogram based onAnnals of translational medicine · 2022Article
- Effects of Tracer Uptake Time in Non-Small Cell Lung CancerJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter.EJNMMI research · 2022Article
- Quantitative Radiomics Features in Diffuse Large B-Cell Lymphoma: Does Segmentation Method Matter?Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Exploring the Utility of Radiomic Feature Extraction to Improve the Diagnostic Accuracy of Cardiac Sarcoidosis Using FDG PET.Frontiers in medicine · 2022Article
- Diagnostic value of baselineScientific reports · 2021Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy.Scientific reports · 2021Article
- The Influence of the Exclusion of Central Necrosis on [Diagnostics (Basel, Switzerland) · 2021Article
- Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?European radiology · 2021Article
- Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy byScientific reports · 2021Article
- Impact of segmentation and discretization on radiomic features inEJNMMI physics · 2021Article
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9 authors.
Funding
Abstract
backgroundMeasures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (
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
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