To evaluate renal allografts function early after transplantation using intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) MRI. This prospective study was approved by the local ethics committee, and written informed consent was obtained from all participants. A total of 82 participants with 62 renal allograft recipients (2-4weeks after kidney transplantation) and 20 volunteers were enrolled to be scanned using IVIM and ASL MRI on a 3.0T MR scanner. Recipients were divided into two groups with either normal or impaired function according to the estimated glomerular filtration rate (eGFR) with a threshold of 60ml/min/1.73m(2). The apparent diffusion coefficient (ADC) of pure diffusion (ADCslow), the ADC of pseudodiffusion (ADCfast), perfusion fraction (PF), and renal blood flow (RBF) of cortex were compared among three groups. The correlation of ADCslow, ADCfast, PF and RBF with eGFR was evaluated. The receiver operating characteristic (ROC) curve and binary logistic regression analyses were performed to assess the diagnostic efficiency of using IVIM and ASL parameters to discriminate allografts with impaired function from normal function. P0.05). Cortical RBF in allografts with normal function was statistically lower than that of healthy controls (P<0.001). Mean cortical ADCslow, ADCfast, PF and RBF were lower for allografts with impaired function than that with normal function (P<0.05). Mean cortical ADCslow, ADCfast, PF and RBF showed a positive correlation with eGFR (all P<0.01) for recipients. The combination of IVIM and ASL MRI showed a higher area under the ROC curve (AUC) (0.865) than that of ASL MRI alone (P=0.02). Combined IVIM and ASL MRI can better evaluate the diffusion and perfusion properties for allografts early after kidney transplantation.

Central cervical lymph node metastasis (CLNM) is considered a risk factor for recurrence in patients with papillary thyroid carcinoma (PTC). Traditional machine learning models suffered from "black-box" problems, which could not exactly explain the interactive effects of the risk factors. We aimed to develop an eXtreme Gradient Boosting (XGBoost) model to assess CLNM, including positive and negative effects. 1,122 patients with PTC admitted at Tianjin First Central Hospital from 2016 to 2020 were retrospectively selected. They were randomly divided into the training and test datasets with an 8:2 ratio. 108 patients with PTC admitted at Binzhou Medical University Hospital in 2020 served as the validation dataset. The XGBoost model was used to assess CLNM. The 10-fold cross-validation was utilized for model selection, and the metric used to evaluate classification performance was the average area under the curve (AUC) of 10-fold cross-validation. Interpretation and transparency of the "black-box" problem were performed. SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) were used to ensure the stability and reliability of the model. The XGBoost model based on ultrasound and dual-energy computed tomography images of the solitary primary lesion had an excellent performance for assessing CLNM, with average AUCs of 0.918, 0.903, and 0.881 in the training, test, and validation datasets, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, diameter, iodine concentration in the venous phase, and calcification) and negative (i.e., sex and age) impacts. For all cases, the capsular invasion prediction weight was the highest; for individual cases, different predictors were assigned different weights. Moreover, the performance of the XGBoost model was better than classical machine-learning models. This study developed and validated an XGBoost model for assessing CLNM in patients with PTC. The ability to visually interpret the positive and negative effects made the XGBoost model an effective tool for guiding clinical treatment.

BACKGROUND AND PURPOSE: Small intracranial aneurysms are being increasingly detected while the rupture risk is not well-understood. We aimed to develop rupture-risk models of small aneurysms by combining clinical, morphologic, and hemodynamic information based on machine learning techniques and to test the models in external validation datasets. MATERIALS AND METHODS: From January 2010 to December 2016, five hundred four consecutive patients with only small aneurysms (<5 mm) detected by CTA and invasive cerebral angiography (or surgery) were retrospectively enrolled and randomly split into training (81%) and internal validation (19%) sets to derive and validate the proposed machine learning models (support vector machine, random forest, logistic regression, and multilayer perceptron). Hemodynamic parameters were obtained using computational fluid dynamics simulation. External validation was performed in other hospitals to test the models. RESULTS: The support vector machine performed the best with areas under the curve of 0.88 (95% CI, 0.85–0.92) and 0.91 (95% CI, 0.74–0.98) in the training and internal validation datasets, respectively. Feature ranks suggested hemodynamic parameters, including stable flow pattern, concentrated inflow streams, and a small (<50%) flow-impingement zone, and the oscillatory shear index coefficient of variation, were the best predictors of aneurysm rupture. The support vector machine showed an area under the curve of 0.82 (95% CI, 0.69–0.94) in the external validation dataset, and no significant difference was found for the areas under the curve between internal and external validation datasets (P = .21). CONCLUSIONS: This study revealed that machine learning had a good performance in predicting the rupture status of small aneurysms in both internal and external datasets. Aneurysm hemodynamic parameters were regarded as the most important predictors.