ADC Maps Texture Analysis for the Evaluation of Kidney Function: A Preliminary Study

Alnazer, Israa; Bourdon, Pascal; Urruty, Thierry; Guillevin, Carole; Naudin, Mathieu; Khalil, Mohamad; Shahin, Ahmad; Falou, Omar; Fernández-Maloigne, Christine

doi:10.1109/icabme47164.2019.8940285

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Textures of BOLD and SWI were able to discriminate control and non-severe renal dysfunction groups demonstrating the ability of textures to detect renal failure at early stages when the disease cannot be detected by eGFR (Ding et al, 2019). In line with these recently published results, our preliminary study which have applied textural analysis on DWI MR images, has confirmed that textures were affected by CKD (Alnazer et al, 2019). Despite the modest sample size, wavelet-based parameters in addition to GLCM-based parameters extracted from renal parenchyma were found to be significantly different between the two groups (Controls and CKD patients).…”

Section: Texture Analysis and Conventional Machine Learning Techniquessupporting

confidence: 85%

“…Applied to different functional MR images, textures have succeeded in evaluating renal dysfunction in recent animal (Zha et al, 2019) and human studies (Rossi et al, 2012), (Alnazer et al, 2019;Ding et al, 2019;Kociołek and Strzelecki Michałand Klepaczko, 2019;Shi et al, 2018). Histogram and GLCM based parameters were extracted from renal parenchyma on three MRI sequences including DWI, BOLD and SWI (Ding et al, 2019).…”

Section: Texture Analysis and Conventional Machine Learning Techniquesmentioning

confidence: 99%

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Recent advances in medical image processing for the evaluation of chronic kidney disease

Alnazer¹,

Bourdon²,

Urruty³

et al. 2021

Medical Image Analysis

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Section: Texture Analysis and Conventional Machine Learning Techniquessupporting

confidence: 85%

Section: Texture Analysis and Conventional Machine Learning Techniquesmentioning

confidence: 99%

Recent advances in medical image processing for the evaluation of chronic kidney disease

Alnazer¹,

Bourdon²,

Urruty³

et al. 2021

Medical Image Analysis

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“…Textural features, derived from mathematical models, have the potential to reflect the tissue heterogeneity by analyzing gray-levels spatial arrangement and describing pixels relationship and gray-levels frequencies as well 14 – 17 In terms of renal mass identification, textures have shown their ability to accurately discriminate renal lesions subtypes 18 . Moreover, textures were found to be promising in ccRCC Fuhrman and WHO/ISUP nuclear grading 4 , 13 , 19 …”

Section: Introductionmentioning

confidence: 99%

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading

et al. 2022

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Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4].Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/ validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K-NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics.Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/ grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/ 0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion:CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.

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Usefulness of Functional MRI Textures in the Evaluation of Renal Function

Alnazer

Falou

Urruty

et al. 2021

2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)

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ADC Maps Texture Analysis for the Evaluation of Kidney Function: A Preliminary Study

Cited by 4 publications

References 17 publications

Recent advances in medical image processing for the evaluation of chronic kidney disease

Recent advances in medical image processing for the evaluation of chronic kidney disease

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading

Usefulness of Functional MRI Textures in the Evaluation of Renal Function

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