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  • IOS Press  (2)
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  • IOS Press  (2)
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  • 1
    In: Clinical Hemorheology and Microcirculation, IOS Press, ( 2023-09-25), p. 1-13
    Abstract: OBJECTIVE: To investigate the correlation between ultrasound performance and prognostic factors in malignant non-mass breast lesions (NMLs). MATERIALS AND METHODS: This study included 106 malignant NMLs in 104 patients. Different US features and contrast enhancement patterns were evaluated. Prognostic factors, including histological types and grades, axillary lymph node and peritumoral lymphovascular status, estrogen and progesterone receptor status and the expression of HER-2 and Ki-67 were determined. A chi-square test and logistic regression analysis were used to analyse possible associations. RESULTS: Lesion size (OR: 3.08, p = 0.033) and posterior echo attenuation (OR: 8.38, p  〈  0.001) were useful in reflecting malignant NMLs containing an invasive carcinoma component. Posterior echo attenuation (OR: 7.51, p = 0.003) and unclear enhancement margin (OR: 6.50, p = 0.018) were often found in tumors with axillary lymph node metastases. Peritumoural lymphovascular invasion mostly exhibited posterior echo attenuation (OR: 3.84, p = 0.049) and unclear enhancement margin (OR: 8.68, p = 0.042) on ultrasound images. Perfusion defect was a comparatively accurate enhancement indicator for negative ER (OR: 2.57, p = 0.041) and PR (OR: 3.04, p = 0.008) expression. Calcifications (OR: 3.03, p = 0.025) and enlarged enhancement area (OR: 5.36, p = 0.033) imply an increased risk of positive HER-2 expression. Similarly, Calcifications (OR: 4.13, p = 0.003) and enlarged enhancement area (OR: 11.05, p  〈  0.001) were valid predictors of high Ki-67 proliferation index. CONCLUSION: Ultrasound performance is valuable for non-invasive prediction of prognostic factors in malignant NMLs.
    Type of Medium: Online Resource
    ISSN: 1386-0291 , 1875-8622
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2026405-7
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  • 2
    Online Resource
    Online Resource
    IOS Press ; 2023
    In:  Journal of Intelligent & Fuzzy Systems ( 2023-09-08), p. 1-18
    In: Journal of Intelligent & Fuzzy Systems, IOS Press, ( 2023-09-08), p. 1-18
    Abstract: The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model.
    Type of Medium: Online Resource
    ISSN: 1064-1246 , 1875-8967
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2070080-5
    SSG: 11
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