Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound

Qin, Xiachuan; Xia, Linlin; Zhu, Chao; Hu, Xiaomin; Xiao, Weihan; Xie, Xisheng; Zhang, Chaoxue

doi:10.2147/jir.s398399

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Quantitative data extracted from renal ultrasound based on features such as texture, shape and wavelength could also detect LN activity. 133 Novel biomarkers for LN activity include renal IFI16 135 and V-set immunoglobulin domain-containing protein 4. 136 For extrarenal flares, 59 63 66 101 106 113 137-148 approximately half of the reports used genetic or genetic expression datasets.…”

Section: Lupus Science and Medicinementioning

confidence: 99%

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Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

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Section: Lupus Science and Medicinementioning

confidence: 99%

Section: Key Sle Findings By ML Reportsmentioning

confidence: 99%

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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“…According to earlier research, radiomics-based techniques can accurately estimate the level of nephritis activity in LN patients. 13 Therefore, we extracted the two-dimensional ultrasound (US) radiomics features of patients with LN, and further establish machine learning (ML) models to try to predict the CI of LN non-invasively.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics

Yin,

Xiao,

et al. 2023

Lupus

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Objective Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI) of LN. Methods A retrospective collection of 136 patients with LN who had renal biopsy was retrospectively collected, and the patients were randomly divided into training set and validation set according to 7:3. Radiomics features are extracted from ultrasound images, independent factors are obtained by using LASSO dimensionality reduction, and then seven ML models were used to establish predictive models. At the same time, a clinical model and an US model were established. The diagnostic efficacy of the model is evaluated by analysis of the receiver operating characteristics (ROC) curve, accuracy, specificity, and sensitivity. The performance of the seven machine learning models was compared with each other and with clinical and US models. Results A total of 1314 radiomics features are extracted from ultrasound images, and 5 features are finally screened out by LASSO for model construction, and the average ROC of the seven ML is 0.683, among which the Xgboost model performed the best, and the AUC in the test set is 0.826 (95% CI: 0.681–0.936). For the same test set, the AUC of clinical model constructed based on eGFR is 0.560 (95% CI: 0.357–0.761), and the AUC of US model constructed based on Ultrasound parameters is 0.679 (95% CI: 0.489–0.853). The Xgboost model is significantly more efficient than the clinical and US models. Conclusion ML model based on ultrasound radiomics features can accurately predict the chronic degree of LN, which can provide a valuable reference for clinicians in the treatment strategy of LN patients.

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“…The conversion of medical images into digital high-throughput quantitative features in radiomics has received increased attention [ 22 , 23 ]. Recently, we found that radiomics can effectively identify subclinical changes, which may be biomarkers, using ultrasound images [ 24 , 25 ]. Deep learning (DL) can directly couple feature extraction, feature selection, and prediction model construction into a neural network model through end-to-end learning from medical images, thus greatly simplifying the process of radiomics analysis [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically

Qin,

Xia,

et al. 2023

Renal Failure

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This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model’s utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663–0.968), and the ROC of the DL model was 0.844 (95% CI:0.717–0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773–0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

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Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound

Cited by 7 publications

References 26 publications

Systemic lupus in the era of machine learning medicine

Systemic lupus in the era of machine learning medicine

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically

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