Extremely randomized neural networks for constructing prediction intervals

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images

Objective

The pathological classification and imaging manifestation of parotid gland tumors are complex, while accurate preoperative identification plays a crucial role in clinical management and prognosis assessment. This study aims to construct and compare the performance of clinical models, traditional radiomics models, deep learning (DL) models, and deep learning radiomics (DLR) models based on ultrasound (US) images in differentiating between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs).

Methods

Retrospective analysis was conducted on 526 patients with confirmed PGTs after surgery, who were randomly divided into a training set and a testing set in the ratio of 7:3. Traditional radiomics and three DL models (DenseNet121, VGG19, ResNet50) were employed to extract handcrafted radiomics (HCR) features and DL features followed by feature fusion. Seven machine learning classifiers including logistic regression (LR), support vector machine (SVM), RandomForest, ExtraTrees, XGBoost, LightGBM and multi-layer perceptron (MLP) were combined to construct predictive models. The most optimal model was integrated with clinical and US features to develop a nomogram. Receiver operating characteristic (ROC) curve was employed for assessing performance of various models while the clinical utility was assessed by decision curve analysis (DCA).

Results

The DLR model based on ExtraTrees demonstrated superior performance with AUC values of 0.943 (95% CI: 0.918-0.969) and 0.916 (95% CI: 0.861-0.971) for the training and testing set, respectively. The combined model DLR nomogram (DLRN) further enhanced the performance, resulting in AUC values of 0.960 (95% CI: 0.940- 0.979) and 0.934 (95% CI: 0.876-0.991) for the training and testing sets, respectively. DCA analysis indicated that DLRN provided greater clinical benefits compared to other models.

Conclusion

DLRN based on US images shows exceptional performance in distinguishing BPGTs and MPGTs, providing more reliable information for personalized diagnosis and treatment plans in clinical practice.

Method based on the support vector machine and information diffusion for prediction intervals of granary airtightness

Granaries should have good airtightness to reduce grain loss in storage. Prediction of granary airtightness at the design stage is beneficial in improving granary design. This paper proposes a method for the prediction interval (PI) of granary airtightness by using small sample data, which can guide designers with granary design. PI that the probability of the true target falling in it is markedly close or larger compared with the confidence level can be the decision basis of the granary design scheme. This study adopts support vector machine as the regression model trained by the airtightness data set of built granaries, and obtains the probability distribution of regression errors through information diffusion. The probability interval of errors is derived using a search algorithm, and PIs of granary airtightness can be acquired thereafter. Assessment indexes of PIs with confidence levels of 0.8 and 0.9 indicate that the proposed method can achieve confidence level and is superior to the comparative method using artificial neural network and bootstrap for PIs in cases of only a few samples. Thus, an innovative and feasible method is proposed for the computer-aided design of granary airtightness.