Selected article for: "AUC curve and predictive performance"

Author: Wu, Linyong; Zhao, Yujia; Lin, Peng; Qin, Hui; Liu, Yichen; Wan, Da; Li, Xin; He, Yun; Yang, Hong
Title: Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ
  • Cord-id: ku4cxs77
  • Document date: 2021_5_17
  • ID: ku4cxs77
    Snippet: BACKGROUND: The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS. METHODS: 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7
    Document: BACKGROUND: The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS. METHODS: 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve. RESULTS: The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively. CONCLUSION: Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00610-7.

    Search related documents:
    Co phrase search for related documents
    • absolute lasso selection shrinkage operator and lr logistic regression: 1, 2
    • absolute lasso selection shrinkage operator and lymph node: 1, 2, 3
    • absolute lasso selection shrinkage operator and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
    • absolute lasso selection shrinkage operator and machine recursive: 1
    • absolute lasso selection shrinkage operator and machine recursive feature elimination: 1
    • absolute lasso selection shrinkage operator and magnetic resonance: 1, 2
    • acc accuracy and local binary pattern: 1
    • acc accuracy and logistic regression: 1, 2
    • acc accuracy and lr logistic regression: 1, 2
    • acc accuracy and machine learning: 1, 2, 3, 4, 5, 6
    • accurate dynamic and machine learning: 1