首页|Shanghai Public Health Clinical Center Reports Findings in Breast Cancer (A non- invasive preoperative prediction model for predicting axillary lymph node metast asis in breast cancer based on a machine learning approach: combining ...)
Shanghai Public Health Clinical Center Reports Findings in Breast Cancer (A non- invasive preoperative prediction model for predicting axillary lymph node metast asis in breast cancer based on a machine learning approach: combining ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Breast Canc er is the subject of a report. According to news reporting out of Shanghai, Peop le’s Republic of China, by NewsRx editors, research stated, “The most common rou te of breast cancer metastasis is through the mammary lymphatic network. An accu rate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications.” Our news journalists obtained a quote from the research from Shanghai Public Hea lth Clinical Center, “In this study, we aimed to develop a non-invasive predicti on model incorporating breast specific gamma image (BSGI) features and ultrasono graphic parameters to assess axillary lymph node status. Cohorts of breast cance r patients who underwent surgery between 2012 and 2021 were created (The trainin g set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) f or the development of the prediction model. six machine learning (ML) methods an d recursive feature elimination were trained in the training set to create a str ong prediction model. Based on the best-performing model, we created an online c alculator that can make a linear predictor in patients easily accessible to clin icians. The receiver operating characteristic (ROC) and calibration curve are us ed to verify the model performance respectively and evaluate the clinical effect iveness of the model. Six ultrasonographic parameters (transverse diameter of tu mour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diamet er of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines ’ model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, sp ecificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). The result in ROC showed the model could benefit from incorporating BSGI fea ture.”
ShanghaiPeople’s Republic of ChinaAs iaBreast CancerCancerCyborgsEmerging TechnologiesHealth and MedicineHemic and Immune SystemsImmunologyLymph NodesLymphoid TissueMachine Lear ningOncologyRisk and PreventionSurgeryWomen’s Health
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