Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

Luo, Xingzhang; Piao, Sirong; Li, Haiqing; Li, Yuxin; Xia, Wei; Bao, Yijun; Li, Xueling; Geng, Daoying; Wu, Hao; Yang, Li

doi:10.1007/s00330-022-08653-2

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…Eleven hundred and sixty-six (1166) ineligible articles were excluded after browsing the abstracts and titles, and full texts of the remaining sixty (60) articles were read. Finally, a total of eighteen (18) studies were included, in which ten (10) [15][16][17][18][19][20][21][22][23][24] focused on SLE and the remaining eight (8) [25][26][27][28][29][30][31][32] on NPSLE. Te study selection process is shown in Figure 1, and the characteristics of included studies are shown in Tables 1 (for SLE) and 2 (for NPSLE).…”

Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Background. Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support. Methods. Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated. Results. Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25. Conclusion. Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

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Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…Currently, glioma, brain metastases, and white matter illnesses are the main topics of radiomics research on the nervous system. 32 Neurological involvement of radiomics in patients with lupus has also been mentioned in the literature, Luo et al 33 developed an MRI-based multifocal radiomics model to distinguish relapsing-remitting multiple sclerosis (RRMS) from neuropsychiatric systemic lupus erythematosus (NPSLE) (The AUC of the training set and the test set were 0.967 and 0.962, respectively). Wu et al 34 developed a radiomic MRI-based nomogram for predicting heart failure in patients with systemic lupus erythematosus with preserved ejection fraction.…”

Section: Discussionmentioning

confidence: 96%

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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“…Radiomic features were extracted from each lesion ROI using the Standardized Environment for Radiomics Analysis (SERA) ( 27 – 29 ) ( https://qurit.ca/software/sera/ ) implemented in MATLAB (Version 2020b; MathWorks). According to guidelines from the Image Biomarker Standardization Initiative (IBSI) ( 30 ), 351 radiomics features (29 morphology, 20 statistics, 30 histogram features, and 272 higher-order texture features) were included for the following analysis.…”

Section: Methodsmentioning

confidence: 99%

“…To optimally merge RIL-based single-lesion classification into a subject-level diagnosis, a lesion-merged function ( 30 ) was used for defining and calculating the Radiomics Index for Subject (RIS). By comparing the summated distances of RIL − court (negativity-like) and RIL + court (positivity-like) lesions relative to the threshold T , the court with a higher summated distance was selected as the diagnosis of a scan, and the distance of the corresponding court was averaged as the RIS.…”

Section: Methodsmentioning

confidence: 99%

An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma

Piao

Luo²,

Bao³

et al. 2022

Front. Neurol.

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BackgroundThe differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions.MethodsIn our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists.ResultsThe joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets.ConclusionThe joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.

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Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

Cited by 15 publications

References 32 publications

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

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

An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma

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