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  • Hindawi Limited  (2)
  • 1
    Online Resource
    Online Resource
    Ultrasound Image Texture Feature Learning-Based Breast Cancer Benign and Malignant Classification
    Hindawi Limited ; 2021
    In:  Computational and Mathematical Methods in Medicine Vol. 2021 ( 2021-12-28), p. 1-8
    Details
    In: Computational and Mathematical Methods in Medicine, Hindawi Limited, Vol. 2021 ( 2021-12-28), p. 1-8
    Abstract: The use of ultrasound images to acquire breast cancer diagnosis information without invasion can reduce the physical and psychological pain of breast cancer patients and is of great significance for the diagnosis and treatment of breast cancer. There are some differences in the texture of breast cancer between benign and malignant cases. Therefore, this paper proposes an adaptive learning method based on ultrasonic image texture features to identify breast cancer. Specifically, firstly, we used dictionary learning and sparse representation to learn the ultrasonic image texture dictionary of benign and malignant cases, respectively, and then used the combination of the two dictionaries to represent the test image to obtain the texture distribution characteristics of the test image under the two dictionary representations, which called the sparse representation coefficient. Finally, these above features were filtered by sparse representation and sent to sparse representation classifier to establish benign and malignant classification model. 128 cases were randomly divided into training and testing sets according to 2: 1 for training and testing. The proposed method has achieved state-of-the-art results, with an accuracy of 0.9070 and the area under the receiver operating characteristic curve of 0.9459. The results demonstrate that the proposed method has the potential to be used in the clinical diagnosis of benign and malignant breast cancer.
    Type of Medium: Online Resource
    ISSN: 1748-6718 , 1748-670X
    URL: Article
    DOI: 10.1155/2021/6261032
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2256917-0
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  • 2
    Online Resource
    Online Resource
    Analysis of a Systemic Inflammatory Biomarker in Advanced Bile Tract Carcinoma Treated with Anti-PD-1 Therapy: Prognostic and Predictive Significance of Lung Immune Prognostic Index Score
    Hindawi Limited ; 2022
    In:  Journal of Oncology Vol. 2022 ( 2022-3-17), p. 1-13
    Details
    In: Journal of Oncology, Hindawi Limited, Vol. 2022 ( 2022-3-17), p. 1-13
    Abstract: Background. The application of immunotherapy is gradually increasing in advanced bile tract carcinoma (BTC), but only some patients could benefit from it. Validated biomarkers can screen out the beneficiaries. Therefore, the objective of this research is aimed at exploring the predictive value of lung immune prognostic index (LIPI) in advanced BTC patients receiving immunotherapy. Methods. This study was conducted on 110 BTC patients. The cut-off value of the derived neutrophil-to-lymphocyte (dNLR) ratio was obtained by the ROC curves to predict the tumor progression rate at the 6th month. The high levels of dNLR (≥the cut-off value) and lactate dehydrogenase (≥the upper limit of normal) were considered to be two risk factors for LIPI. Based on these two risk factors, patients were categorized into 3 groups based on risk factors: 0 for the good group, 1 for the intermediate group, and 2 for the poor group. Due to the limited number of patients in the poor group, it was integrated into the intermediate group to be the intermediate/poor group. Finally, the subjects were divided into two groups: LIPI-good and LIPI-intermediate/poor. Results. The results shed light on the 110 BTC patients’ LIPI in advanced BTC patients receiving immunotherapy, indicating that the cut-off value of dNLR was 1.74. According to the risk stratification, 38 (34.5%) patients had a good LIPI score, whereas the LIPI score was intermediate/poor in 72 (65.5%). In addition, patients with good LIPI were related to longer progression-free survival (PFS) and overall survival (OS), compared to those with intermediate/poor LIPI (12.17 months vs. 3.17 months; 20.2 months vs. 8.7 months). According to multivariate analysis, the intermediate/poor LIPI group was independently correlated with over 2.3 times greater risk of tumor progression ( HR = 2.301 ; 95% CI, 1.395-3.796; P = 0.001 ) and over 1.8 times greater risk of death ( HR = 1.877 ; 95% CI, 1.076-3.275; P = 0.027 ) than the good group. Moreover, the result also revealed that there were significant differences of DCR for patients of the good group and the intermediate/poor group (86.8% vs. 65.3%; P = 0.012 ). Conclusion. Finally, this study verifies, for the first time, that LIPI is an independent factor affecting the survival and clinical efficacy of advanced BTC patients receiving immunotherapy. It may be difficult for patients with intermediate/poor LIPI to benefit from immunotherapy.
    Type of Medium: Online Resource
    ISSN: 1687-8469 , 1687-8450
    URL: Article
    DOI: 10.1155/2022/1427779
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2461349-6
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