Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

García-Olalla, Oscar; Fernández-Robles, Laura; Alegre, Enrique; Castejón-Limas, Manuel; Fidalgo, Eduardo

doi:10.3390/s19051048

Cited by 7 publications

(4 citation statements)

References 64 publications

(73 reference statements)

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Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

“…The geometry features describe the tumor volume, surface area and shape, and their ratios. The texture features describe the heterogenetic of ROIs based on the gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and local binary pattern (LBP) [24][25][26][27]. In the feature extraction process, the feature aggregation of GLCM and GLRLM values was performed by averaging over 3D directional matrices to improve rotational invariance [28].…”

Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

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Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Liao

Lee

Yang

et al. 2021

Cancers

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The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients’ lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients.

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Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

Section: Mri Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Liao

Lee

Yang

et al. 2021

Cancers

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“…Geometric features measured the 3D shape and size of tumor ROIs. Texture features quantified the heterogeneity of ROIs based on the gray level co-occurrence matrices (GLCM) and gray level run length matrix (GLRLM) [ 30 , 31 , 32 ]. Wavelet features provided the detail of images by filtering image signals based on different levels of spatial frequency.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Conventional and Radiomic Features between 18F-FBPA PET/CT and PET/MR

et al. 2021

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Boron-10-containing positron emission tomography (PET) radio-tracer, 18F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from 18F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent 18F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r2) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between 18F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.

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“…Proposing new descriptors of an image that define the characteristics of the image can be key in this regard. García-Olalla et al [9] presented a new texture descriptor booster based on statistical information of the image. This descriptor is employed in texture-based classification images.…”

Section: Contributions To the Special Issue On Visual Sensorsmentioning

confidence: 99%

Special Issue on Visual Sensors

Reinoso

Payá

2020

Sensors

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Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

Cited by 7 publications

References 64 publications

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases

Comparison of Conventional and Radiomic Features between 18F-FBPA PET/CT and PET/MR

Special Issue on Visual Sensors

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