Shape and Texture Indexes Application to Cell Nuclei Classification

Thibault, Guillaume; Fertil, Bernard; Navarro, Claire; Pereira, Sandrine; Cau, Pierre; Lévy, Nicolas; Séqueira, Jean; Mari, Jean-Luc

doi:10.1142/s0218001413570024

Cited by 294 publications

(306 citation statements)

References 40 publications

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“…After resampling with 64 discrete values (D 5 64 in Eq. 3), 4 matrices were computed from each VOI: the cooccurrence matrix (CM) (14), the gray-level run length matrix (GLRLM) (15), the neighborhood gray-level different matrix (NGLDM) (16), and the gray-level zone length matrix (GLZLM) (17). The GLRLM was calculated from 13 different directions in space.…”

Section: Tumor Characterizationmentioning

confidence: 99%

Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Orlhac¹,

Soussan²,

Maisonobe³

et al. 2014

J Nucl Med

320

344

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Texture indices are of growing interest for tumor characterization in 18 F-FDG PET. Yet, on the basis of results published in the literature so far, it is unclear which indices should be used, what they represent, and how they relate to conventional indices such as standardized uptake values (SUVs), metabolic volume (MV), and total lesion glycolysis (TLG). We investigated in detail 31 texture indices, 5 firstorder statistics (histogram indices) derived from the gray-level histogram of the tumor region, and their relationship with SUV, MV, and TLG in 3 different tumor types. Methods: Three patient groups corresponding to 3 cancer types at baseline were studied independently: patients with metastatic colorectal cancer (72 lesions), nonsmall cell lung cancer (24 lesions), and breast cancer (54 lesions). Thirty-one texture indices were studied in addition to SUVs, MV, and TLG, and 5 indices extracted from histogram analysis were also investigated. The relationships between indices were studied as well as the robustness of the various texture indices with respect to the parameters involved in the calculation method (sampling schemes and tumor volume of interest). Results: Regardless of the patient group, many indices were highly correlated (Pearson correlation coefficient jrj ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (jrj ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (jrj ≤ 0.55). The resampling formula used to calculate texture indices had a substantial impact, and resampling using at least 32 discrete values should be used for texture indices calculation. The texture indices change as a function of the segmentation method was higher than that of peak and maximum SUVs but less than mean SUV for 5 texture indices and was larger than that of MV for 14 texture indices and for the 5 histogram indices. All these results were extremely consistent across the 3 tumor types and explained many of the observations reported in the literature so far. Conclusion: None of the histogram indices and only 17 of 31 texture indices were robust with respect to the tumor-segmentation method. An appropriate resampling formula with at least 32 gray levels should be used to avoid introducing a misleading relationship between texture indices and SUV. Some texture indices are highly correlated or strongly correlate with MV whatever the tumor type.Such correlation should be accounted for when interpreting the usefulness of texture indices for tumor characterization, which might call for systematic multivariate analyses.

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Section: Tumor Characterizationmentioning

confidence: 99%

Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Orlhac¹,

Soussan²,

Maisonobe³

et al. 2014

J Nucl Med

320

344

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“…The texture features included 22 gray level cooccurrence matrix (32), 11 run length matrix (33), 10 size zone matrix (34), and 5 neighborhood gray-tone difference matrix (35) features (Supplemental Table 1; supplemental materials are available at http:// jnm.snmjournals.org). In addition, 5 conventional features (MTV, SUV max , SUV peak , SUV mean , and SUV total ) were computed for comparison with the radiomic features.…”

Section: Pet Feature Extraction and Selectionmentioning

confidence: 99%

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

et al. 2016

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PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18 F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18 F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR1] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC 5 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDR Wilcoxon , FDR Noether ) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDR Wilcoxon 5 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR1 vs. EGFR-negative, AUC 5 0.67, FDR Noether 5 0.0032), as well as differentiating between KRAS-positive and EGFR1 (AUC 5 0.65, FDR Noether 5 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC 5 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

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“…The use of 18 F-FDG PET for diagnosis and staging is well established in clinical practice, and interest is now increasing in the use of this imaging modality for therapy response assessment and patient follow-up. For such applications, measurements of standardized uptake value (SUV) are used, with the maximum value (SUV max ) being the most popular since it is the easiest to obtain.…”

mentioning

confidence: 99%

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in ¹⁸F-FDG PET

Tixier

Hatt²,

Rest³

et al. 2012

J Nucl Med

293

273

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18 F-FDG PET measurement of standardized uptake value (SUV) is increasingly used for monitoring therapy response and predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the heterogeneity of tracer uptake by tumors as a significant predictor of response. The primary objective of this study was to evaluate the reproducibility of these heterogeneity measurements. Methods: Double baseline 18 F-FDG PET scans were acquired within 4 d of each other for 16 patients before any treatment was considered. A Bland-Altman analysis was performed on 8 parameters based on histogram measurements and 17 parameters based on textural heterogeneity features after discretization with values between 8 and 128. Results: The reproducibility of maximum and mean SUV was similar to that in previously reported studies, with a mean percentage difference of 4.7% 6 19.5% and 5.5% 6 21.2%, respectively. By comparison, better reproducibility was measured for some textural features describing local heterogeneity of tracer uptake, such as entropy and homogeneity, with a mean percentage difference of 22% 6 5.4% and 1.8% 6 11.5%, respectively. Several regional heterogeneity parameters such as variability in the intensity and size of regions of homogeneous activity distribution had reproducibility similar to that of SUV measurements, with 95% confidence intervals of 222.5% to 3.1% and 21.1% to 23.5%, respectively. These parameters were largely insensitive to the discretization range. Conclusion: Several parameters derived from textural analysis describing heterogeneity of tracer uptake by tumors on local and regional scales had reproducibility similar to or better than that of simple SUV measurements. These reproducibility results suggest that these 18 F-FDG PET-derived parameters, which have already been shown to have predictive and prognostic value in certain cancer models, may be used to monitor therapy response and predict patient outcome.

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Shape and Texture Indexes Application to Cell Nuclei Classification

Cited by 294 publications

References 40 publications

Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in ¹⁸F-FDG PET

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Shape and Texture Indexes Application to Cell Nuclei Classification

Cited by 294 publications

References 40 publications

Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET

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Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in ¹⁸F-FDG PET