Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models

Amiri, Shahram; Akbarabadi, Mina; Abdolali, Fatemeh; Nikoofar, Alireza; Esfahani, Azam Janati; Cheraghi, Susan

doi:10.1016/j.compbiomed.2021.104409

Cited by 20 publications

(6 citation statements)

References 40 publications

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“…However, the causative role of each variable is not yet demonstrated, and whether the texture index is specific to lithium nephrotoxicity or generalizable to other causes of CKD remains to be elucidated. Besides the work from Kline et al on PKD, 28 recent literature has investigated kidney texture analysis in kidney diseases such as glomerulonephritis 29 or radiation‐induced kidney damage 30 and during CKD 31,32 . It has been shown that interstitial fibrosis is associated with worst renal outcome independently of GFR, in native kidneys and in kidney allografts 33–35 .…”

Section: Discussionmentioning

confidence: 99%

MRI‐based kidney radiomic analysis during chronic lithium treatment

Beunon

Barat

Dohan

et al. 2022

Eur J Clin Investigation

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Background Lithium therapy during bipolar disorder is associated with an increased risk of chronic kidney disease (CKD) that is slowly progressive and undetectable at early stages. We aimed at identifying kidney image texture features as possible imaging biomarkers of decreased measured glomerular filtration rate (mGFR) using radiomics of T2‐weighted magnetic resonance imaging (MRI). Methods One hundred and eight patients treated with lithium were evaluated including mGFR and kidney MRI, with T2‐weighted sequence single‐shot fast spin‐echo. Computed radiomic analysis was performed after kidney segmentation. Significant features were selected to build a radiomic signature using multivariable Cox analysis to detect an mGFR <60 ml/min/1.73 m². The texture index was validated using a training and a validation cohort. Results Texture analysis index was able to detect an mGFR decrease, with an AUC of 0.85 in the training cohort and 0.71 in the validation cohort. Patients with a texture index below the median were older (59 [42–66] vs. 46 [34–54] years, p = .001), with longer treatment duration (10 [3–22] vs. 6 [2–10] years, p = .02) and a lower mGFR (66 [46–84] vs. 83 [71–94] ml/min/1.73m², p < .001). Texture analysis index was independently and negatively associated with age (β = −.004 ± 0.001, p < .001), serum vasopressin (−0.005 ± 0.002, p = .02) and lithium treatment duration (−0.01 ± 0.003, p = .001), with a significant interaction between lithium treatment duration and mGFR (p = .02). Conclusions A renal texture index was developed among patients treated with lithium associated with a decreased mGFR. This index might be relevant in the diagnosis of lithium‐induced renal toxicity.

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Section: Discussionmentioning

confidence: 99%

MRI‐based kidney radiomic analysis during chronic lithium treatment

Beunon

Barat

Dohan

et al. 2022

Eur J Clin Investigation

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“…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). Amiri et al 35 have explored the application of radiomics in predicting radiation-induced CKD on CT images using various machine learning approaches and achieved good prediction performance, with AUC ranging from 0.82 to 0.99. Deng et al 38 have applied diffusion tensor imaging (DTI)-based radiomics signature to construct a LASSO regression model for detecting early diabetic kidney damage and found that the model achieved a good performance (AUC = 0.882).…”

Section: Discussionmentioning

confidence: 99%

“…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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“…For example, Shin et al [16] improved the V-net for precise kidney segmentation and volume measurement, achieving an accuracy comparable to that of human specialists for 50 randomly selected image slices. Amiri et al [17] utilized a modified Mask R-CNN for kidney segmentation and extracted handcrafted features to train a random forest (RF) model for predicting radiation-induced kidney damage. Patro et al [18] enhanced network computational efficiency and diagnostic performance for kidney stone detection by introducing a Kronecker product-based convolution.…”

Section: Introductionmentioning

confidence: 99%

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

Fu,

Fang,

Lin

et al. 2024

Vis. Comput. Ind. Biomed. Art

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This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660–0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769–0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784–0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

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Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models

Cited by 20 publications

References 40 publications

MRI‐based kidney radiomic analysis during chronic lithium treatment

MRI‐based kidney radiomic analysis during chronic lithium treatment

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

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

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