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

An explainable ultrasound-based machine learning model for predicting reproductive outcomes after frozen embryo transfer

RESEARCH QUESTION

Can an optimal machine learning model be developed to predict reproductive outcomes following frozen embryo transfer (FET)?

DESIGN

This prospective study included 787 infertile females who underwent FET. The participants were split into a training cohort (n = 550) and a test cohort (n = 237) at a ratio of seven to three. Radiomics features were extracted from ultrasound images of the endometrium and junctional zone. A radiomics model was developed to generate the radiomics score (rad score). Logistic regression was applied to process the clinical data and create a clinical model. A fusion machine learning model was developed by integrating the rad score with independent clinical data using the XGboost algorithm. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to interpret and visualize the contributions of features to the outcomes of FET.

RESULTS

The fusion model demonstrated superior performance, as indicated by an AUC of 0.861 (95% CI 0.829-0.890), in the training cohort, surpassing both the clinical model (AUC 0.680, 95% CI 0.635-0.722; P < 0.001) and the radiomics model (AUC 0.814, 95% CI 0.777-0.848; P < 0.001). The SHAP summary plot reveals the impacts of each feature on the predictive model, and the rad score was found to be the main feature. SHAP force plots provided explanations at the individual level.

CONCLUSION

An explainable machine learning model was established utilizing clinical data and ultrasound images to forecast the outcomes of FET. By utilizing the SHAP method, clinicians may better comprehend the contributors to the outcomes of FET in individual patients, and make better decisions before FET.

Rough hypervolume-driven feature selection with groupwise intelligent sampling for detecting clinical characterization of lupus nephritis

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease. Lupus nephritis (LN) is a major risk factor for morbidity and mortality in SLE. Proliferative and pure membranous LN have different prognoses and may require different treatments. This study proposes a binary rough hypervolume-driven spherical evolution algorithm with groupwise intelligent sampling (bRGSE). The efficient dimensionality reduction capability of the bRGSE is verified across twelve datasets. These datasets are from the public datasets, with feature dimensions ranging from seven hundred to fifty thousand. The experimental results indicate that bRGSE performs better than seven high-performing alternatives. Then, the bRGSE was combined with adaptive boosting (AdaBoost) to form a new model (bRGSE_AdaBoost), which analyzed clinical records collected from 110 patients with LN. Experimental results show that the proposed bRGSE_AdaBoost can identify the most critical indicators, including urine latent blood, white blood cells, endogenous creatinine clearing rate, and age. These indicators may help differentiate between proliferative LN and membranous LN. The proposed bRGSE algorithm is an efficient dimensionality reduction method. The developed bRGSE_AdaBoost model, a computer-aided model, achieved an accuracy of 96.687 % and is expected to provide early warning for the treatment and diagnosis of LN.

Interpretable Machine Learning Model Based on Superb Microvascular Imaging for Non-Invasive Determination of Crescent Status of IgAN

Purpose

To assess the crescentic status of IgA nephropathy (IgAN) non-invasively using a superb microvascular imaging (SMI)-based radiomics machine learning (ML) model.

Patients and Methods

IgAN patients who underwent renal biopsy from June 2022 to October 2023, with two-dimensional ultrasound (US) and SMI examinations conducted one day prior to the renal biopsy. The patients selected were divided randomly into a training group and a test group in a 7:3 ratio. Radiomic features were extracted from US and SMI images, then radiomic features were constructed and ML models were further established using logistic regression (LR) and extreme gradient boosting (XGBoost)XGBoost to determine the crescentic status. The utility of the proposed model was evaluated using receiver operating characteristics, calibration, and decision curve analysis. The SHapley Additive exPlanations (SHAP) was utilized to explain the best-performing ML model.

Results

A total of 147 IgAN patients were included in the study, with 103 in the training group and 44 in the test group .Among them, the US-SMI based XGBoost model achieved the best results, with an the area under the curve (AUC) of 0.839 (95% CI,0.756-0.910) and an accuracy of 78.6% in the training group.In the test group, the AUC was 0.859 (95% CI,0.721-0.964), and the accuracy was 81.8%, significantly surpassing the ML model of a single modality and the clinical model established based on occult blood. Additionally, the decision curve analysis (DCA) demonstrated that the XGBoost model provided a higher overall net benefit in the both groups.

Conclusion

The SMI radiomics ML model has the capability to accurately predict the crescentic status of IgAN patients, providing effective assistance for clinical treatment decisions.

Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study

BACKGROUND

This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics.

METHODS

This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models.

RESULTS

The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688.

CONCLUSIONS

The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.

Systemic lupus in the era of machine learning medicine

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.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

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

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.

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

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.