Publications by authors named "Zi-Mei Lin"

Objective: The aim of this study was to evaluate whether a predictive model based on a contrast enhanced ultrasound (CEUS)-based nomogram and clinical features (Clin) could differentiate Her-2-overexpressing breast cancers from other breast cancers.

Methods: A total of 152 pathology-proven breast cancers including 55 Her-2-overexpressing cancers and 97 other cancers from two units that underwent preoperative CEUS examination, were included and divided into training (n = 102) and validation cohorts (n = 50). Multivariate regression analysis was utilized to identify independent indicators for developing predictive nomogram models.

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Background: Continuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with radiomics combined with the XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than 1 min.

Methods: We gathered breast CEUS videos of 109 benign and 81 malignant tumors from two centers.

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Objective: To study the effect of the problem-based learning (PBL) method in ultrasonography (US) resident standardization training during the COVID-19 pandemic.

Methods: Fifty residents were divided into two groups to participate in a 30-day US training program. The residents in the observation group underwent PBL combined with the lecture-based learning (LBL) method, while the residents in the control group experienced the LBL method alone, with 25 residents in each group.

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A portion of detected breast masses might be overrated by using the Breast Imaging-Reporting and Data System ultrasonography (BI-RADS US) lexicon. A principal component regression-based contrast-enhanced ultrasound (PCR-CEUS) evaluation system was built to quantitatively illustrate whether CEUS could help radiologists to differentiate 4A masses. The PCR-CEUS evaluation system, based on principal component analysis (PCA) and logistic regression, was verified by random assignment into training and test sets and shown to reduce the data dimension and avoid collinearity in CEUS variables.

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Background: BRAF V600E mutation was proved to be associated with thyroid cancer. Papillary thyroid carcinoma (PTC) with positive BRAF mutation might have a more aggressive behavior. We investigated the correlation of the contrast-enhanced ultrasound (CEUS) features with BRAF 600VE in PTC.

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The aim of the present study was to investigate the benefits of combining contrast-enhanced ultrasound (CEUS) and strain elastography (SE) for the diagnosis of thyroid nodules with non-diagnostic fine-needle aspiration cytology (FNAC) results. Between October 2013 and March 2017, CEUS and SE were performed in 226 patients (236 thyroid nodules) with non-diagnostic FNAC results prior to thyroidectomy. The diagnostic value of CEUS, SE and their combination (CEUS+SE) in distinguishing malignant from benign thyroid nodules was evaluated, using surgical pathology as a reference.

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We investigated the role of the virtual touch tissue quantification (VTQ) technique in diagnosing Hashimoto's thyroiditis (HT) and in distinguishing various HT-related thyroid dysfunctions. Two hundred HT patients and 100 healthy volunteers (the control group) were enrolled. The diagnostic performance of VTQ in predicting HT was calculated as the area under the receiver operating characteristic curve (A).

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