Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases

Fu, Xu; Liu, Huaiqin; Bi, Xiaowang; Xiao, Gong

doi:10.1155/2021/3774423

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…34 By the concept that biomedical images contain information that reflects underlying pathophysiology, radiomics can quantitatively analyze the pathological characteristics hidden behind medical images. 13 In recent years, several studies have reported that medical images could be useful for detecting kidney damage and fibrosis, by using ultrasound images, 26–28 computed tomography (CT), 35–37 and magnetic resonance imaging (MRI). 38–40 Zhu et al 28 extracted radiomics features from the shear wave elastography (SWE) ultrasound images and concluded that SWE can predict kidney injury progression with an improved performance by radiomics (AUC = 0.809).…”

Section: Discussionmentioning

confidence: 99%

Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children

Chen,

Wen,

Yang

et al. 2023

Ultrason Imaging

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Henoch-Schönlein purpura nephritis (HSPN) is one of the most common kidney diseases in children. The current diagnosis and classification of HSPN depend on pathological biopsy, which is seriously limited by its invasive and high-risk nature. The aim of the study was to explore the potential of radiomics model for evaluating the histopathological classification of HSPN based on the ultrasound (US) images. A total of 440 patients with Henoch-Schönlein purpura nephritis proved by biopsy were analyzed retrospectively. They were grouped according to two histopathological categories: those without glomerular crescent formation (ISKDC grades I-II) and those with glomerular crescent formation (ISKDC grades III-V). The patients were randomly assigned to either a training cohort ( n = 308) or a validation cohort ( n = 132) with a ratio of 7:3. The sonologist manually drew the regions of interest (ROI) on the ultrasound images of the right kidney including the cortex and medulla. Then, the ultrasound radiomics features were extracted using the Pyradiomics package. The dimensions of radiomics features were reduced by Spearman correlation coefficients and least absolute shrinkage and selection operator (LASSO) method. Finally, three radiomics models using k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established, respectively. The predictive performance of such classifiers was assessed with receiver operating characteristic (ROC) curve. 105 radiomics features were extracted from derived US images of each patient and 14 features were ultimately selected for the machine learning analysis. Three machine learning models including k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established for HSPN classification. Of the three classifiers, the SVM classifier performed the best in the validation cohort [area under the curve (AUC) =0.870 (95% CI, 0.795–0.944), sensitivity = 0.706, specificity = 0.950]. The US-based radiomics had good predictive value for HSPN classification, which can be served as a noninvasive tool to evaluate the severity of renal pathology and crescentic formation in children with HSPN.

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Section: Discussionmentioning

confidence: 99%

Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children

Chen,

Wen,

Yang

et al. 2023

Ultrason Imaging

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“…Yang et al [ 28 ], Haghighi et al [ 29 ], and Mehta et al [ 30 ] used fully connected convolutional networks to segment kidney images. Fu et al [ 31 ] used ultrasound images to input an attention module to segment kidney cysts in CT images. Da Cruz et al [ 32 ] presented a fully automated method for segmenting kidneys with and without tumours on CT images.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Gharahbagh,

Hajihashemi,

Machado

et al. 2024

Bioengineering

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Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image’s gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

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“…There have been various segmentation tasks associated with kidneys, such as kidney segmentation [ 16 ], cyst segmentation [ 17 ], tumor segmentation [ 18 ], and cortex segmentation [ 19 ]. Manual kidney segmentation, such as region-of-interest (ROI) border tracing [ 20 ] or stereology [ 21 ] by experienced and qualified professionals, is considered the gold standard.…”

Section: Related Workmentioning

confidence: 99%

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

Xiao

Liu

et al. 2022

Diagnostics

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Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.

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Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases

Cited by 13 publications

References 14 publications

Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children

Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

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